From 78cd06b758c1301f6288ed10696cc97bf508601f Mon Sep 17 00:00:00 2001 From: Cheng-Jun Wang Date: Mon, 5 Aug 2024 13:00:20 +0800 Subject: [PATCH] :ear_of_rice: --- ..._homework_mobirise_repubilc_solution.ipynb | 4 +- ...nt.ipynb => 10-03-cntext-checkpoint.ipynb} | 567 +- ...10-04-topic-models-update-checkpoint.ipynb | 3750 ++++++++++ .../10-07-bert-topic-checkpoint.ipynb | 39 - .../BERTopic-checkpoint.ipynb | 357 + notebook/08-04-RNN.ipynb | 152 +- notebook/09-word2vec.ipynb | 184 +- notebook/10-01-text-mining.ipynb | 45 +- ...nsenti-cntext.ipynb => 10-03-cntext.ipynb} | 567 +- notebook/10-04-topic-models-update.ipynb | 10 +- notebook/10-06-llm-ollama.ipynb | 106 +- notebook/10-07-bert-topic.ipynb | 39 - notebook/BERTopic.ipynb | 357 + notebook/output/sopmi_candi_words/neg.txt | 1545 ++-- notebook/output/sopmi_candi_words/pos.txt | 6611 +++++++++-------- 15 files changed, 9368 insertions(+), 4965 deletions(-) rename notebook/.ipynb_checkpoints/{10-03-cnsenti-cntext-checkpoint.ipynb => 10-03-cntext-checkpoint.ipynb} (80%) create mode 100644 notebook/.ipynb_checkpoints/10-04-topic-models-update-checkpoint.ipynb delete mode 100644 notebook/.ipynb_checkpoints/10-07-bert-topic-checkpoint.ipynb create mode 100644 notebook/.ipynb_checkpoints/BERTopic-checkpoint.ipynb rename notebook/{10-03-cnsenti-cntext.ipynb => 10-03-cntext.ipynb} (80%) delete mode 100644 notebook/10-07-bert-topic.ipynb create mode 100644 notebook/BERTopic.ipynb diff --git a/homework/Day1_homework_mobirise_repubilc_solution.ipynb b/homework/Day1_homework_mobirise_repubilc_solution.ipynb index 7588b36..117e4a3 100644 --- a/homework/Day1_homework_mobirise_repubilc_solution.ipynb +++ b/homework/Day1_homework_mobirise_repubilc_solution.ipynb @@ -96,7 +96,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -110,7 +110,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" }, "toc": { "base_numbering": 1, diff --git a/notebook/.ipynb_checkpoints/10-03-cnsenti-cntext-checkpoint.ipynb b/notebook/.ipynb_checkpoints/10-03-cntext-checkpoint.ipynb similarity index 80% rename from notebook/.ipynb_checkpoints/10-03-cnsenti-cntext-checkpoint.ipynb rename to notebook/.ipynb_checkpoints/10-03-cntext-checkpoint.ipynb index 5cd1c57..ca94350 100644 --- a/notebook/.ipynb_checkpoints/10-03-cnsenti-cntext-checkpoint.ipynb +++ b/notebook/.ipynb_checkpoints/10-03-cntext-checkpoint.ipynb @@ -8,21 +8,21 @@ } }, "source": [ - "# cnsenti\n", + "# cntext\n", "\n", - "中文情感分析库(Chinese Sentiment)可对文本进行情绪分析、正负情感分析。\n", + "cntext 是一个文本分析包，提供基于词嵌入模型的语义距离和语义投影。 此外，cntext还提供了传统的方法，如字数统计、可读性、文档相似度、情感分析等。\n", "\n", - "- github地址 https://github.com/thunderhit/cnsenti\n", - "- pypi地址 https://pypi.org/project/cnsenti/" + "- github地址 https://github.com/hiDaDeng/cntext/\n", + "- pypi地址 https://pypi.org/project/cntext/" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:20:34.962273Z", - "start_time": "2022-07-21T07:20:31.728487Z" + "end_time": "2024-08-04T04:40:52.208419Z", + "start_time": "2024-08-04T04:40:46.180045Z" }, "slideshow": { "slide_type": "subslide" @@ -34,70 +34,101 @@ "output_type": "stream", "text": [ "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting cnsenti\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/64/91/06bff77081acf17c99bbc59aff7b06a834664b6bbe5c25bc250ce1f53911/cnsenti-0.0.7-py3-none-any.whl (395 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m395.8/395.8 KB\u001b[0m \u001b[31m892.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cnsenti) (0.42.1)\n", - "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.8/site-packages (from cnsenti) (1.20.1)\n", - "Installing collected packages: cnsenti\n", - "Successfully installed cnsenti-0.0.7\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", + "Requirement already satisfied: cntext in /opt/anaconda3/lib/python3.8/site-packages (1.7.9)\n", + "Requirement already satisfied: nltk in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.6.1)\n", + "Requirement already satisfied: numpy==1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.20.0)\n", + "Requirement already satisfied: mittens in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.2)\n", + "Requirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.42.1)\n", + "Requirement already satisfied: pyecharts in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.9.1)\n", + "Collecting gensim==4.0.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/81/09/6929fd1e882943d1764f2aaf1e66ed32fc1cef987dab6ddbec0291e3ae4a/gensim-4.0.0-cp38-cp38-macosx_10_9_x86_64.whl (23.9 MB)\n", + "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.3.4)\n", + "Requirement already satisfied: scikit-learn==1.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.0)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (5.2.1)\n", + "Requirement already satisfied: scipy>=0.18.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (1.6.2)\n", + "Requirement already satisfied: joblib>=0.11 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (1.0.1)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (2.1.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (0.10.0)\n", + "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (8.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.8.1)\n", + "Requirement already satisfied: tqdm in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (4.59.0)\n", + "Requirement already satisfied: regex in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (2021.4.4)\n", + "Requirement already satisfied: click in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (7.1.2)\n", + "Requirement already satisfied: prettytable in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.3.0)\n", + "Requirement already satisfied: simplejson in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.17.6)\n", + "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (2.11.3)\n", + "Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->cntext) (1.15.0)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->pyecharts->cntext) (1.1.1)\n", + "Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.8/site-packages (from prettytable->pyecharts->cntext) (0.2.5)\n", + "Installing collected packages: gensim\n", + " Attempting uninstall: gensim\n", + " Found existing installation: gensim 3.8.3\n", + " Uninstalling gensim-3.8.3:\n", + " Successfully uninstalled gensim-3.8.3\n", + "Successfully installed gensim-4.0.0\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - " pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cnsenti" + " pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cntext" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:21:32.910915Z", - "start_time": "2022-07-21T07:21:31.361943Z" + "end_time": "2024-08-04T04:42:04.753866Z", + "start_time": "2024-08-04T04:41:56.317408Z" }, "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building prefix dict from the default dictionary ...\n", - "Dumping model to file cache /var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/jieba.cache\n", - "Loading model cost 0.824 seconds.\n", - "Prefix dict has been built successfully.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "{'words': 22, 'sentences': 2, 'pos': 4, 'neg': 0}\n" + "Help on package cntext:\n", + "\n", + "NAME\n", + " cntext\n", + "\n", + "PACKAGE CONTENTS\n", + " dictionary\n", + " mind\n", + " similarity\n", + " stats\n", + "\n", + "VERSION\n", + " 1.7.9\n", + "\n", + "FILE\n", + " /opt/anaconda3/lib/python3.8/site-packages/cntext/__init__.py\n", + "\n", + "\n" ] } ], "source": [ - "from cnsenti import Sentiment\n", + "import cntext as ct\n", "\n", - "senti = Sentiment()\n", - "test_text= '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result = senti.sentiment_count(test_text)\n", - "print(result)" + "help(ct)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:21:46.164954Z", - "start_time": "2022-07-21T07:21:46.151660Z" + "end_time": "2024-08-04T04:42:32.087353Z", + "start_time": "2024-08-04T04:42:31.270546Z" }, "slideshow": { "slide_type": "subslide" @@ -105,20 +136,39 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "{'words': 22, 'sentences': 2, '好': 0, '乐': 4, '哀': 0, '怒': 0, '惧': 0, '恶': 0, '惊': 0}\n" + "Building prefix dict from the default dictionary ...\n", + "Dumping model to file cache /var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/jieba.cache\n", + "Loading model cost 0.802 seconds.\n", + "Prefix dict has been built successfully.\n" ] + }, + { + "data": { + "text/plain": [ + "Counter({'看待': 1,\n", + " '网文': 1,\n", + " '作者': 1,\n", + " '黑客': 1,\n", + " '大佬': 1,\n", + " '盗号': 1,\n", + " '改文因': 1,\n", + " '万分': 1,\n", + " '惭愧': 1,\n", + " '停': 1})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from cnsenti import Emotion\n", + "text = '如何看待一网文作者被黑客大佬盗号改文，因万分惭愧而停更。'\n", "\n", - "emotion = Emotion()\n", - "test_text = '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result = emotion.emotion_count(test_text)\n", - "print(result)" + "ct.term_freq(text, lang='chinese')" ] }, { @@ -129,20 +179,25 @@ } }, "source": [ - "**sentiment_calculate(text)** belongs to the Sentiment class, which can calculate the emotional information of the chinese text more accurately. Compared with sentiment_count only counts the number of positive and negative sentiment words in the text, sentiment_calculate also considers\n", + "## readability\n", + "文本可读性，指标越大，文章复杂度越高，可读性越差。\n", "\n", - "- Is there a modifier of strength adverbs before emotional words\n", - "- Is there an emotional semantic reversal effect of negative words before emotional words?\n", - "for examples:" + "> readability(text, lang='chinese')\n", + "\n", + "徐巍,姚振晔,陈冬华.中文年报可读性：衡量与检验[J].会计研究,2021(03):28-44.\n", + "\n", + "- readability1 ---每个分句中的平均字数\n", + "- readability2 ---每个句子中副词和连词所占的比例\n", + "- readability3 ---参考Fog Index， readability3=(readability1+readability2)×0.5" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:23:53.508215Z", - "start_time": "2022-07-21T07:23:53.488286Z" + "end_time": "2024-08-04T04:43:01.222065Z", + "start_time": "2024-08-04T04:43:01.215733Z" }, "slideshow": { "slide_type": "subslide" @@ -150,50 +205,77 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "sentiment_count {'words': 22, 'sentences': 2, 'pos': 4, 'neg': 0}\n", - "sentiment_calculate {'sentences': 2, 'words': 22, 'pos': 27.0, 'neg': 0.0}\n" - ] + "data": { + "text/plain": [ + "{'readability1': 28.0,\n", + " 'readability2': 0.15789473684210525,\n", + " 'readability3': 14.078947368421053}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from cnsenti import Sentiment\n", + "text1 = '如何看待一网文作者被黑客大佬盗号改文，因万分惭愧而停更。'\n", "\n", - "senti = Sentiment()\n", - "test_text = '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result1 = senti.sentiment_count(test_text)\n", - "result2 = senti.sentiment_calculate(test_text)\n", - "print('sentiment_count',result1)\n", - "print('sentiment_calculate',result2)" + "ct.readability(text1, lang='chinese')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": { + "ExecuteTime": { + "end_time": "2024-08-04T04:46:15.124091Z", + "start_time": "2024-08-04T04:46:15.119890Z" + }, "slideshow": { - "slide_type": "slide" + "slide_type": "subslide" } }, + "outputs": [ + { + "data": { + "text/plain": [ + "['DUTIR.pkl',\n", + " 'HOWNET.pkl',\n", + " 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',\n", + " 'SentiWS.pkl',\n", + " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", + " 'ANEW.pkl',\n", + " 'LSD2015.pkl',\n", + " 'NRC.pkl',\n", + " 'geninqposneg.pkl',\n", + " 'HuLiu.pkl',\n", + " 'Loughran_McDonald_Financial_Sentiment.pkl',\n", + " 'AFINN.pkl',\n", + " 'ADV_CONJ.pkl',\n", + " 'STOPWORDS.pkl',\n", + " 'Concreteness.pkl',\n", + " 'ChineseEmoBank.pkl']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## cntext\n", - "\n", - "https://github.com/hidadeng/cntext\n", + "import cntext as ct\n", "\n", - "cntext is a text analysis package that provides semantic distance and semantic projection based on word embedding models. Besides,cntext also provides the traditional methods, such as word count , readability, document similarity, sentiment analysis, etc." + "# 获取cntext内置词典列表(pkl格式)\n", + "ct.dict_pkl_list()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:43:09.507980Z", - "start_time": "2022-07-21T11:43:06.411977Z" - }, - "slideshow": { - "slide_type": "subslide" + "end_time": "2024-08-04T04:48:41.259954Z", + "start_time": "2024-08-04T04:48:41.249663Z" } }, "outputs": [ @@ -201,93 +283,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Requirement already satisfied: cntext in /opt/anaconda3/lib/python3.8/site-packages (1.7.9)\n", - "Requirement already satisfied: numpy==1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.20.0)\n", - "Requirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.42.1)\n", - "Requirement already satisfied: nltk in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.6.1)\n", - "Requirement already satisfied: gensim==4.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (4.0.0)\n", - "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.3.4)\n", - "Requirement already satisfied: scikit-learn==1.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.0)\n", - "Requirement already satisfied: mittens in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.2)\n", - "Requirement already satisfied: pyecharts in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.9.1)\n", - "Requirement already satisfied: smart-open>=1.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (5.2.1)\n", - "Requirement already satisfied: scipy>=0.18.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (1.6.2)\n", - "Requirement already satisfied: joblib>=0.11 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (1.0.1)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (2.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (0.10.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (1.3.1)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.8.1)\n", - "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (8.2.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.4.7)\n", - "Requirement already satisfied: regex in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (2021.4.4)\n", - "Requirement already satisfied: tqdm in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (4.59.0)\n", - "Requirement already satisfied: click in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (7.1.2)\n", - "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (2.11.3)\n", - "Requirement already satisfied: prettytable in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.3.0)\n", - "Requirement already satisfied: simplejson in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.17.6)\n", - "Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->cntext) (1.15.0)\n", - "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->pyecharts->cntext) (1.1.1)\n", - "Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.8/site-packages (from prettytable->pyecharts->cntext) (0.2.5)\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" + "1.7.9\n", + "dict_keys(['DUTIR', 'Referer', 'Desc'])\n" ] } ], "source": [ - "!pip3 install --upgrade cntext -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "import cntext as ct\n", + "\n", + "print(ct.__version__)\n", + "# 导入pkl词典文件,\n", + "dutir = ct.load_pkl_dict('DUTIR.pkl')\n", + "print(dutir.keys())" ] }, { - "cell_type": "code", - "execution_count": 21, + "cell_type": "markdown", "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:51:23.838864Z", - "start_time": "2022-07-21T11:50:32.711340Z" - }, "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.8/site-packages (1.2.4)\n", - "Collecting pandas\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/85/8afe540bd0299c4d58f0a5b88acc49a8021804abe05a00d2cbc2fccde873/pandas-1.4.3-cp38-cp38-macosx_10_9_x86_64.whl (11.4 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.4/11.4 MB\u001b[0m \u001b[31m276.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:02\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2021.1)\n", - "Requirement already satisfied: numpy>=1.18.5 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (1.20.0)\n", - "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n", - "Installing collected packages: pandas\n", - " Attempting uninstall: pandas\n", - " Found existing installation: pandas 1.2.4\n", - " Uninstalling pandas-1.2.4:\n", - " Successfully uninstalled pandas-1.2.4\n", - "Successfully installed pandas-1.4.3\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], "source": [ - "!pip3 install --upgrade pandas -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "## sentiment\n", + "\n", + "> sentiment(text, diction, lang='chinese') \n", + "\n", + "使用diy词典进行情感分析，计算各个情绪词出现次数; 未考虑强度副词、否定词对情感的复杂影响，\n", + "\n", + "- text: 待分析中文文本\n", + "- diction: 情感词字典；\n", + "- lang: 语言类型，\"chinese\"或\"english\"，默认\"chinese\"" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:52:32.466752Z", - "start_time": "2022-07-21T11:52:25.722860Z" + "end_time": "2024-08-04T04:49:32.782452Z", + "start_time": "2024-08-04T04:49:32.287078Z" }, "slideshow": { "slide_type": "subslide" @@ -295,39 +330,42 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting python-Levenshtein\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2a/dc/97f2b63ef0fa1fd78dcb7195aca577804f6b2b51e712516cc0e902a9a201/python-Levenshtein-0.12.2.tar.gz (50 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: setuptools in /opt/anaconda3/lib/python3.8/site-packages (from python-Levenshtein) (52.0.0.post20210125)\n", - "Building wheels for collected packages: python-Levenshtein\n", - " Building wheel for python-Levenshtein (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp38-cp38-macosx_10_9_x86_64.whl size=80491 sha256=6a0876479d2777b80eec9fb1b73b1f5feb1079854b8b4c41d60dba3798030bbd\n", - " Stored in directory: /Users/chengjun/Library/Caches/pip/wheels/28/a5/92/bf15714fe87b46cdfefbba580ca70f86ee6392d27a1b501d4b\n", - "Successfully built python-Levenshtein\n", - "Installing collected packages: python-Levenshtein\n", - "Successfully installed python-Levenshtein-0.12.2\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] + "data": { + "text/plain": [ + "{'乐_num': 2,\n", + " '好_num': 0,\n", + " '怒_num': 0,\n", + " '哀_num': 0,\n", + " '惧_num': 0,\n", + " '恶_num': 0,\n", + " '惊_num': 0,\n", + " 'stopword_num': 8,\n", + " 'word_num': 14,\n", + " 'sentence_num': 1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "!pip3 install --upgrade python-Levenshtein -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "import cntext as ct\n", + "\n", + "text = '我今天得奖了，很高兴，我要将快乐分享大家。'\n", + "\n", + "ct.sentiment(text=text,\n", + " diction=ct.load_pkl_dict('DUTIR.pkl')['DUTIR'],\n", + " lang='chinese')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:52:50.879009Z", - "start_time": "2022-07-21T11:52:50.874088Z" + "end_time": "2024-08-04T04:50:25.934568Z", + "start_time": "2024-08-04T04:50:25.909495Z" }, "slideshow": { "slide_type": "subslide" @@ -335,43 +373,46 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on package cntext:\n", - "\n", - "NAME\n", - " cntext\n", - "\n", - "PACKAGE CONTENTS\n", - " dictionary\n", - " mind\n", - " similarity\n", - " stats\n", - "\n", - "VERSION\n", - " 1.7.9\n", - "\n", - "FILE\n", - " /opt/anaconda3/lib/python3.8/site-packages/cntext/__init__.py\n", - "\n", - "\n" - ] + "data": { + "text/plain": [ + "{'Referer': 'Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911',\n", + " 'Desc': '语言具体性词典， 具体性计算应用案例可参考Packard, Grant, and Jonah Berger. \"How concrete language shapes customer satisfaction.\" *Journal of Consumer Research* 47, no. 5 (2021): 787-806.',\n", + " 'Concreteness': word valence\n", + " 0 roadsweeper 4.85\n", + " 1 traindriver 4.54\n", + " 2 tush 4.45\n", + " 3 hairdress 3.93\n", + " 4 pharmaceutics 3.77\n", + " ... ... ...\n", + " 39949 unenvied 1.21\n", + " 39950 agnostically 1.20\n", + " 39951 conceptualistic 1.18\n", + " 39952 conventionalism 1.18\n", + " 39953 essentialness 1.04\n", + " \n", + " [39954 rows x 2 columns]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "import cntext as ct\n", "\n", - "help(ct)" + "# load the concreteness.pkl dictionary file\n", + "concreteness_df = ct.load_pkl_dict('concreteness.pkl')\n", + "concreteness_df" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:56:12.150628Z", - "start_time": "2022-07-21T11:56:12.146199Z" + "end_time": "2024-08-04T04:53:34.420645Z", + "start_time": "2024-08-04T04:53:34.404483Z" }, "slideshow": { "slide_type": "subslide" @@ -381,40 +422,30 @@ { "data": { "text/plain": [ - "['DUTIR.pkl',\n", - " 'HOWNET.pkl',\n", - " 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'SentiWS.pkl',\n", - " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", - " 'ANEW.pkl',\n", - " 'LSD2015.pkl',\n", - " 'NRC.pkl',\n", - " 'geninqposneg.pkl',\n", - " 'HuLiu.pkl',\n", - " 'Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'AFINN.pkl',\n", - " 'ADV_CONJ.pkl',\n", - " 'STOPWORDS.pkl',\n", - " 'Concreteness.pkl',\n", - " 'ChineseEmoBank.pkl']" + "{'valence': 9.28, 'word_num': 5}" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ct.dict_pkl_list()" + "reply = \"I'll go look for that\"\n", + "\n", + "score=ct.sentiment_by_valence(text=reply, \n", + " diction=concreteness_df, \n", + " lang='english')\n", + "score" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:55:38.007632Z", - "start_time": "2022-07-21T11:55:37.997085Z" + "end_time": "2024-08-04T04:52:33.083998Z", + "start_time": "2024-08-04T04:52:33.076815Z" }, "slideshow": { "slide_type": "subslide" @@ -439,7 +470,7 @@ " 'sentence_num': 1}" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -454,54 +485,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:57:03.874083Z", - "start_time": "2022-07-21T11:57:03.861742Z" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Referer': 'Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911',\n", - " 'Desc': '语言具体性词典， 具体性计算应用案例可参考Packard, Grant, and Jonah Berger. \"How concrete language shapes customer satisfaction.\" *Journal of Consumer Research* 47, no. 5 (2021): 787-806.',\n", - " 'Concreteness': word valence\n", - " 0 roadsweeper 4.85\n", - " 1 traindriver 4.54\n", - " 2 tush 4.45\n", - " 3 hairdress 3.93\n", - " 4 pharmaceutics 3.77\n", - " ... ... ...\n", - " 39949 unenvied 1.21\n", - " 39950 agnostically 1.20\n", - " 39951 conceptualistic 1.18\n", - " 39952 conventionalism 1.18\n", - " 39953 essentialness 1.04\n", - " \n", - " [39954 rows x 2 columns]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ct.load_pkl_dict('concreteness.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:57:29.203436Z", - "start_time": "2022-07-21T11:57:29.190140Z" + "end_time": "2024-08-04T04:52:50.963134Z", + "start_time": "2024-08-04T04:52:50.948664Z" }, "slideshow": { "slide_type": "subslide" @@ -572,7 +560,7 @@ "4 pharmaceutics 3.77" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -585,44 +573,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:57:57.215210Z", - "start_time": "2022-07-21T11:57:57.197092Z" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'valence': 9.28, 'word_num': 5}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reply = \"I'll go look for that\"\n", - "\n", - "score=ct.sentiment_by_valence(text=reply, \n", - " diction=concreteness_df, \n", - " lang='english')\n", - "score" - ] - }, - { - "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T12:02:53.846511Z", - "start_time": "2022-07-21T12:02:53.746320Z" + "end_time": "2024-08-04T04:53:50.123421Z", + "start_time": "2024-08-04T04:53:50.021025Z" }, "slideshow": { "slide_type": "subslide" @@ -665,11 +620,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T12:11:05.599905Z", - "start_time": "2022-07-21T12:10:03.155453Z" + "end_time": "2024-08-04T04:56:34.798503Z", + "start_time": "2024-08-04T04:55:33.788633Z" }, "slideshow": { "slide_type": "subslide" @@ -684,7 +639,7 @@ "Step 2/4:...Collect co-occurrency information ...\n", "Step 3/4:...Calculate mutual information ...\n", "Step 4/4:...Save candidate words ...\n", - "Finish! used 62.41 s\n" + "Finish! used 60.98 s\n" ] } ], diff --git a/notebook/.ipynb_checkpoints/10-04-topic-models-update-checkpoint.ipynb b/notebook/.ipynb_checkpoints/10-04-topic-models-update-checkpoint.ipynb new file mode 100644 index 0000000..e9718a0 --- /dev/null +++ b/notebook/.ipynb_checkpoints/10-04-topic-models-update-checkpoint.ipynb @@ -0,0 +1,3750 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "***\n", + "***\n", + "# 主题模型\n", + "\n", + "***\n", + "***\n", + "\n", + "\n", + "![image.png](img/chengjun.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "2014年高考前夕，百度“基于海量作文范文和搜索数据，利用概率主题模型，预测2014年高考作文的命题方向”。共分为了六个主题：时间、生命、民族、教育、心灵、发展。而每个主题下面又包括了一些具体的关键词。比如，生命的主题对应：平凡、自由、美丽、梦想、奋斗、青春、快乐、孤独。[Read more](https://site.douban.com/146782/widget/notes/15462869/note/356806087/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Latent Dirichlet Allocation (LDA)\n", + "\n", + "LDA (潜在狄利克雷分配) is a generative model that **infers unobserved meanings** from a large set of observations. \n", + "- Blei DM, Ng J, Jordan MI. **Latent dirichlet allocation**. J Mach Learn Res. 2003; 3: 993–1022.\n", + "- Blei DM, Lafferty JD. Correction: a correlated topic model of science. Ann Appl Stat. 2007; 1: 634. \n", + "- Blei DM. **Probabilistic topic models**. Commun ACM. 2012; 55: 55–65.\n", + "- Chandra Y, Jiang LC, Wang C-J (2016) Mining Social Entrepreneurship Strategies Using Topic Modeling. PLoS ONE 11(3): e0151342. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "\n", + "### Topic models assume that each document contains a mixture of topics.\n", + "\n", + "It is impossible to directly assess the relationships between topics and documents and between topics and terms. \n", + "\n", + "- Topics are considered latent/unobserved variables that stand between the documents and terms\n", + "\n", + "- What can be directly observed is the distribution of terms over documents, which is known as the document term matrix (DTM).\n", + "\n", + "Topic models algorithmically identify the best set of latent variables (topics) that can best explain the observed distribution of terms in the documents. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The DTM is further decomposed into two matrices：\n", + "- a term-topic matrix (TTM) \n", + "- a topic-document matrix (TDM)\n", + "\n", + "Each document can be assigned to a primary topic that demonstrates the highest topic-document probability and can then be linked to other topics with declining probabilities." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "![image.png](img/txt7.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### LDA（Latent Dirichlet Allocation）是一种**文档主题**生成模型\n", + "- 三层贝叶斯概率模型，包含词、主题和文档三层结构。\n", + "\n", + "**生成模型**认为一篇文章的每个词都是通过这样一个过程得到:\n", + "\n", + " 以一定概率选择了某个主题，并从这个主题中以一定概率选择某个词语\n", + "\n", + "- 文档到主题服从**多项式分布**，主题到词服从多项式分布。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 多项式分布（Multinomial Distribution）是二项式分布的推广\n", + "- 二项分布的典型例子是扔硬币，硬币正面朝上概率为p, 重复扔n次硬币，k次为正面的概率即为一个二项分布概率。（严格定义见伯努利实验定义）。\n", + "- 把二项分布公式推广至多种状态，就得到了多项分布。\n", + " - 例如在上面例子中1出现k1次，2出现k2次，3出现k3次的概率分布情况。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## LDA是一种**非监督机器学习技术**\n", + "\n", + "可以用来识别大规模文档集（document collection）或语料库（corpus）中潜藏的主题信息。\n", + "- 采用了词袋（bag of words）的方法，将每一篇文档视为一个词频向量，从而将文本信息转化为了易于建模的数字信息。 \n", + "- 但是词袋方法没有考虑词与词之间的顺序，这简化了问题的复杂性，同时也为模型的改进提供了契机。\n", + "- 每一篇文档代表了一些主题所构成的一个概率分布，而每一个主题又代表了很多单词所构成的一个概率分布。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![image.png](img/txt8.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "\n", + "\n", + "### 多项分布的参数服从Dirichlet分布\n", + "- Dirichlet分布是多项分布的参数的分布， 被认为是“分布上的分布”。\n", + "\n", + "\\begin{equation}\n", + " \\text{Dir}\\left(\\boldsymbol{\\alpha}\\right)\\rightarrow \\mathrm{p}\\left(\\boldsymbol{\\theta}\\mid\\boldsymbol{\\alpha}\\right)=\\frac{\\Gamma\\left(\\sum_{i=1}^{k}\\boldsymbol{\\alpha}_{i}\\right)}{\\prod_{i=1}^{k}\\Gamma\\left(\\boldsymbol{\\alpha}_{i}\\right)}\\prod_{i=1}^{k}\\boldsymbol{\\theta}_{i}^{\\boldsymbol{\\alpha}_{i}-1}\n", + "\\end{equation}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T08:28:51.352185Z", + "start_time": "2020-06-04T08:28:51.346897Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2843831684937255" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# http://blog.bogatron.net/blog/2014/02/02/visualizing-dirichlet-distributions/\n", + "from scipy.stats import dirichlet\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import matplotlib.tri as tri\n", + "\n", + "quantiles = np.array([0.2, 0.2, 0.6]) # specify quantiles\n", + "alpha = np.array([0.4, 5, 15]) # specify concentration parameters\n", + "dirichlet.pdf(quantiles, alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T08:27:19.274562Z", + "start_time": "2020-06-04T08:27:19.269699Z" + } + }, + "outputs": [], + "source": [ + "corners = np.array([[0, 0], [1, 0], [0.5, 0.75**0.5]])\n", + "triangle = tri.Triangulation(corners[:, 0], corners[:, 1])\n", + "\n", + "# Mid-points of triangle sides opposite of each corner\n", + "midpoints = [(corners[(i + 1) % 3] + corners[(i + 2) % 3]) / 2.0 \\\n", + " for i in range(3)]\n", + "def xy2bc(xy, tol=1.e-3):\n", + " '''Converts 2D Cartesian coordinates to barycentric.'''\n", + " s = [(corners[i] - midpoints[i]).dot(xy - midpoints[i]) / 0.75 \\\n", + " for i in range(3)]\n", + " return np.clip(s, tol, 1.0 - tol)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T08:28:17.018875Z", + "start_time": "2020-06-04T08:28:17.013751Z" + } + }, + "outputs": [], + "source": [ + "class Dirichlet(object):\n", + " def __init__(self, alpha):\n", + " self._alpha = np.array(alpha)\n", + " \n", + " def pdf(self, x):\n", + " x=x/x.sum() # enforce simplex constraint\n", + " return dirichlet.pdf(x=x,alpha=self._alpha)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T08:35:48.394094Z", + "start_time": "2020-06-04T08:35:48.389269Z" + } + }, + "outputs": [], + "source": [ + "def draw_pdf_contours(dist, nlevels=200, subdiv=8, **kwargs):\n", + " import math\n", + "\n", + " refiner = tri.UniformTriRefiner(triangle)\n", + " trimesh = refiner.refine_triangulation(subdiv=subdiv)\n", + " pvals = [dist.pdf(xy2bc(xy)) for xy in zip(trimesh.x, trimesh.y)]\n", + " plt.tricontourf(trimesh, pvals, nlevels, cmap='jet', **kwargs)\n", + "\n", + " plt.axis('equal')\n", + " plt.xlim(0, 1)\n", + " plt.ylim(0, 0.75**0.5)\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T08:36:02.999940Z", + "start_time": "2020-06-04T08:35:58.773419Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "draw_pdf_contours(Dirichlet([1, 2, 3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## LDA的名字由来\n", + "\n", + "存在两个隐含的Dirichlet分布。\n", + "\n", + "- 每篇文档对应一个不同的topic分布，服从多项分布\n", + " - topic多项分布的参数服从一个Dirichlet分布。 \n", + "- 每个topic下存在一个term的多项分布\n", + " - term多项分布的参数服从一个Dirichlet分布。\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Assume K topics are in D documents.\n", + "\n", + "`主题在词语上的分布` Each topic is denoted with $\\beta_{1:K}$， \n", + "\n", + "- 主题$\\beta_K$ 是第k个主题，这个主题表达为一系列的terms。\n", + "- Each topic is a distribution of fixed words. \n", + "\n", + "`主题在文本上的分布` The topics proportion in the document *d* is denoted as $\\theta_d$\n", + "\n", + "- e.g., the kth topic's proportion in document d is $\\theta_{d, k}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-22T22:12:40.058178", + "start_time": "2017-09-22T22:12:40.051172" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "`主题在文本和词上的分配`\n", + "\n", + "topic models assign topics to a document and its terms. \n", + "- The topic assigned to document *d* is denoted as $z_d$, \n", + "- The topic assigned to the nth term in document *d* is denoted as $z_{d,n}$. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "`可以观察到的是？`\n", + "\n", + "词在文档中的位置，也就是文档-词矩阵（document-term matrix）\n", + "\n", + "Let $w_{d,n}$ denote the nth term in document d. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![image.png](img/txt9.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "`联合概率分布` According to Blei et al. the joint distribution of $\\beta_{1:K}$,$\\theta_{1:D}$, $z_{1:D}$ and $w_{d, n}$ plus the generative process for LDA can be expressed as:\n", + "\n", + "$$ p(\\beta_{1:K}, \\theta_{1:D}, z_{1:D}, w_{d, n}) = $$\n", + "\n", + "$$\\prod_{i=1}^{K} p(\\beta_i) \\prod_{d =1}^D p(\\theta_d)(\\prod_{n=1}^N p(z_{d,n} \\mid \\theta_d) \\times p(w_{d, n} \\mid \\beta_{1:K}, Z_{d, n}) ) $$\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "![image.png](img/txt11.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "\n", + "**后验分布** Note that $\\beta_{1:k},\\theta_{1:D},and z_{1:D}$ are latent, unobservable variables. Thus, the computational challenge of LDA is to compute the conditional distribution of them given the observable specific words in the documents $w_{d, n}$. \n", + "\n", + "Accordingly, the posterior distribution of LDA can be expressed as:\n", + "\n", + "$$p(\\beta_{1:K}, \\theta_{1:D}, z_{1:D} \\mid w_{d, n}) = \\frac{p(\\beta_{1:K}, \\theta_{1:D}, z_{1:D}, w_{d, n})}{p(w_{1:D})}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Because the number of possible topic structures is exponentially large, it is impossible to compute the posterior of LDA. \n", + "\n", + "Topic models aim to develop efficient algorithms to **approximate** the posterior of LDA. There are two categories of algorithms: \n", + "- sampling-based algorithms\n", + "- variational algorithms \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Gibbs sampling\n", + "In statistics, Gibbs sampling or a Gibbs sampler is a **Markov chain Monte Carlo (MCMC)** algorithm for obtaining a sequence of observations which are approximated from a specified **multivariate probability distribution**, when direct sampling is difficult. \n", + "\n", + "Using the Gibbs sampling method, we can build a Markov chain for the sequence of random variables (see Eq 1). \n", + "\n", + "The sampling algorithm is applied to the chain to sample from the limited distribution, and it approximates the **posterior**. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "# Gensim: Topic modelling for humans\n", + "\n", + "\n", + "\n", + "Gensim is developed by Radim Řehůřek,who is a machine learning researcher and consultant in the Czech Republic. We must start by installing it. We can achieve this by running the following command:\n", + "\n", + "> # pip install gensim\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:15.512864Z", + "start_time": "2020-06-04T09:40:14.970494Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from gensim import corpora, models, similarities, matutils\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Download data\n", + "\n", + "http://www.cs.princeton.edu/~blei/lda-c/ap.tgz\n", + "\n", + "http://www.cs.columbia.edu/~blei/lda-c/\n", + "\n", + "Unzip the data and put them into your folder, e.g., /Users/datalab/bigdata/ap/" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:17.866518Z", + "start_time": "2020-06-04T09:40:17.852825Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# Load the data\n", + "corpus = corpora.BleiCorpus('/Users/datalab/bigdata/ap/ap.dat',\\\n", + " '/Users/datalab/bigdata/ap/vocab.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T01:02:28.358323", + "start_time": "2017-09-23T01:02:28.352898" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "# 使用help命令理解corpora.BleiCorpus函数\n", + "\n", + "> help(corpora.BleiCorpus)" + ] + }, + { + "cell_type": "raw", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + " class BleiCorpus(gensim.corpora.indexedcorpus.IndexedCorpus)\n", + " | Corpus in Blei's LDA-C format.\n", + " | \n", + " | The corpus is represented as two files: \n", + " | one describing the documents, \n", + " | and another describing the mapping between words and their ids." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:19.954314Z", + "start_time": "2020-06-04T09:40:19.949857Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['docbyoffset',\n", + " 'fname',\n", + " 'id2word',\n", + " 'index',\n", + " 'length',\n", + " 'line2doc',\n", + " 'load',\n", + " 'save',\n", + " 'save_corpus',\n", + " 'serialize']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 使用dir看一下有corpus有哪些子函数？\n", + "dir(corpus)[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:20.777541Z", + "start_time": "2020-06-04T09:40:20.773383Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'i', 1: 'new', 2: 'percent', 3: 'people', 4: 'year', 5: 'two'}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# corpus.id2word is a dict which has keys and values, e.g., \n", + "{0: u'i', 1: u'new', 2: u'percent', 3: u'people', 4: u'year', 5: u'two'}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:22.037219Z", + "start_time": "2020-06-04T09:40:21.993038Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# transform the dict to list using items()\n", + "corpusList = list(corpus.id2word.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:22.761505Z", + "start_time": "2020-06-04T09:40:22.758017Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 'i'), (1, 'new'), (2, 'percent'), (3, 'people'), (4, 'year')]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show the first 5 elements of the list\n", + "corpusList[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Build the topic model" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:24.588314Z", + "start_time": "2020-06-04T09:40:24.585309Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# 设置主题数量\n", + "NUM_TOPICS = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:30.616590Z", + "start_time": "2020-06-04T09:40:25.689812Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "model = models.ldamodel.LdaModel(\n", + " corpus, num_topics=NUM_TOPICS, \n", + " id2word=corpus.id2word, \n", + " alpha=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-22T23:59:07.812226", + "start_time": "2017-09-22T23:59:07.803861" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "# help(models.ldamodel.LdaModel)\n", + "\n", + "Help on class LdaModel in module gensim.models.ldamodel:\n", + "\n", + "class LdaModel(gensim.interfaces.TransformationABC, gensim.models.basemodel.BaseTopicModel)\n", + "- The constructor estimates Latent Dirichlet Allocation model parameters based on a training corpus:\n", + " \n", + "> lda = LdaModel(corpus, num_topics=10)\n", + " \n", + "- You can then infer topic distributions on new, unseen documents, with\n", + "\n", + "> doc_lda = lda[doc_bow] \n", + "\n", + "- The model can be updated (trained) with new documents via\n", + "\n", + "> lda.update(other_corpus)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:48.298279Z", + "start_time": "2020-06-04T09:40:48.294539Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'__class__ __delattr__ __dict__ __dir__ __doc__ __eq__ __format__ __ge__ __getattribute__ __getitem__ __gt__ __hash__ __init__ __init_subclass__ __le__ __lt__ __module__ __ne__ __new__ __reduce__ __reduce_ex__ __repr__ __setattr__ __sizeof__ __str__ __subclasshook__ __weakref__ _adapt_by_suffix _apply _load_specials _save_specials _smart_save alpha bound callbacks chunksize clear decay diff dispatcher distributed do_estep do_mstep dtype eta eval_every expElogbeta gamma_threshold get_document_topics get_term_topics get_topic_terms get_topics id2word inference init_dir_prior iterations load log_perplexity minimum_phi_value minimum_probability num_terms num_topics num_updates numworkers offset optimize_alpha optimize_eta passes per_word_topics print_topic print_topics random_state save show_topic show_topics state sync_state top_topics update update_alpha update_eta update_every'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 看一下训练出来的模型有哪些函数？\n", + "' '.join(dir(model))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# We can see the list of topics a document refers to \n", + "\n", + "by using the model[doc] syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:40:58.446238Z", + "start_time": "2020-06-04T09:40:54.406526Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "document_topics = [model[c] for c in corpus]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:04.066198Z", + "start_time": "2020-06-04T09:41:04.061687Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(2, 0.048023984),\n", + " (7, 0.067052096),\n", + " (22, 0.078788824),\n", + " (31, 0.049663354),\n", + " (34, 0.0149856135),\n", + " (39, 0.022489905),\n", + " (42, 0.010858211),\n", + " (53, 0.041905038),\n", + " (61, 0.033337574),\n", + " (68, 0.04844338),\n", + " (72, 0.012781475),\n", + " (75, 0.016759546),\n", + " (78, 0.4995139),\n", + " (85, 0.024694487)]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many topics does one document cover?\n", + "# 例如，对于文档2来说，他所覆盖的主题和比例如下：\n", + "document_topics[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:06.795582Z", + "start_time": "2020-06-04T09:41:06.787225Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('salvadoran', 0.016505657),\n", + " ('feared', 0.009027323),\n", + " ('priests', 0.006627582),\n", + " ('supreme', 0.0062730736),\n", + " ('court', 0.0058254534),\n", + " ('cesar', 0.0057329326),\n", + " ('anc', 0.005718951),\n", + " ('two', 0.005506124),\n", + " ('executed', 0.005237348),\n", + " ('mandelas', 0.0049420297),\n", + " ('i', 0.004933451),\n", + " ('convicted', 0.0043522445),\n", + " ('trial', 0.0042409007),\n", + " ('new', 0.0038077259),\n", + " ('murdering', 0.00376128),\n", + " ('participated', 0.0036495375),\n", + " ('teeth', 0.0035992293),\n", + " ('states', 0.003568108),\n", + " ('million', 0.003400971),\n", + " ('mandela', 0.003306418)]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The first topic\n", + "# 对于主题0而言，它所对应10个词语和比重如下：\n", + "model.show_topic(55, 20)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:33.180416Z", + "start_time": "2020-06-04T09:41:33.174220Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('soviet', 0.014213265),\n", + " ('heat', 0.010803085),\n", + " ('people', 0.008755475),\n", + " ('electricity', 0.008300867),\n", + " ('waters', 0.006102835),\n", + " ('reported', 0.0059347493),\n", + " ('kabul', 0.005585455),\n", + " ('afghan', 0.005549842),\n", + " ('assaults', 0.0052388334),\n", + " ('government', 0.0051779784)]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 对于主题0而言，它所对应10个词语和比重如下：\n", + "words = model.show_topic(0, 10)\n", + "words" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:36.561975Z", + "start_time": "2020-06-04T09:41:36.556740Z" + }, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "soviet 0.014213265\n", + "heat 0.010803085\n", + "people 0.008755475\n", + "electricity 0.008300867\n", + "waters 0.006102835\n", + "reported 0.0059347493\n", + "kabul 0.005585455\n", + "afghan 0.005549842\n", + "assaults 0.0052388334\n", + "government 0.0051779784\n" + ] + } + ], + "source": [ + "for f, w in words[:10]:\n", + " print(f, w)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:41.866630Z", + "start_time": "2020-06-04T09:41:41.860759Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('lenders', 0.007934938),\n", + " ('chinese', 0.006651945),\n", + " ('security', 0.006582973),\n", + " ('new', 0.006525414),\n", + " ('village', 0.0055111744),\n", + " ('police', 0.005486252),\n", + " ('group', 0.0054585696),\n", + " ('program', 0.0054052803),\n", + " ('chains', 0.004982714),\n", + " ('united', 0.004855675)]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 对于主题99而言，它所对应10个词语和比重如下：\n", + "\n", + "model.show_topic(99, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:44.705812Z", + "start_time": "2020-06-04T09:41:44.700061Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('soviet', 0.014213265),\n", + " ('heat', 0.010803085),\n", + " ('people', 0.008755475),\n", + " ('electricity', 0.008300867),\n", + " ('waters', 0.006102835),\n", + " ('reported', 0.0059347493),\n", + " ('kabul', 0.005585455),\n", + " ('afghan', 0.005549842),\n", + " ('assaults', 0.0052388334),\n", + " ('government', 0.0051779784)]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 模型计算出来的所有的主题当中的第1个是？\n", + "model.show_topic(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:41:59.439351Z", + "start_time": "2020-06-04T09:41:59.433959Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "soviet 0.014213265\n", + "heat 0.010803085\n", + "people 0.008755475\n", + "electricity 0.008300867\n", + "waters 0.006102835\n", + "reported 0.0059347493\n", + "kabul 0.005585455\n", + "afghan 0.005549842\n", + "assaults 0.0052388334\n", + "government 0.0051779784\n" + ] + } + ], + "source": [ + "#help(model.show_topics(0))\n", + "for w, f in words:\n", + " print(w, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T00:14:53.757535", + "start_time": "2017-09-23T00:14:53.622856" + }, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# write out topcis with 10 terms with weights\n", + "for ti in range(model.num_topics):\n", + " words = model.show_topic(ti, 10)\n", + " tf = sum(f for w, f in words)\n", + " with open('/Users/chengjun/github/workshop/data/topics_term_weight.txt', 'a') as output:\n", + " for w, f in words:\n", + " line = str(ti) + '\\t' + w + '\\t' + str(f/tf) \n", + " output.write(line + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Find the most discussed topic\n", + "\n", + "i.e., the one with the highest total weight" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:21.254431Z", + "start_time": "2020-06-04T09:42:21.250781Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function corpus2dense in module gensim.matutils:\n", + "\n", + "corpus2dense(corpus, num_terms, num_docs=None, dtype=)\n", + " Convert corpus into a dense numpy 2D array, with documents as columns.\n", + " \n", + " Parameters\n", + " ----------\n", + " corpus : iterable of iterable of (int, number)\n", + " Input corpus in the Gensim bag-of-words format.\n", + " num_terms : int\n", + " Number of terms in the dictionary. X-axis of the resulting matrix.\n", + " num_docs : int, optional\n", + " Number of documents in the corpus. If provided, a slightly more memory-efficient code path is taken.\n", + " Y-axis of the resulting matrix.\n", + " dtype : data-type, optional\n", + " Data type of the output matrix.\n", + " \n", + " Returns\n", + " -------\n", + " numpy.ndarray\n", + " Dense 2D array that presents `corpus`.\n", + " \n", + " See Also\n", + " --------\n", + " :class:`~gensim.matutils.Dense2Corpus`\n", + " Convert dense matrix to Gensim corpus format.\n", + "\n" + ] + } + ], + "source": [ + "## Convert corpus into a dense np array \n", + "help(matutils.corpus2dense)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:32.059333Z", + "start_time": "2020-06-04T09:42:28.308118Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.4285222 , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0.0132917 , 0.04585509, ..., 0.2665006 , 0. ,\n", + " 0. ],\n", + " ...,\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0.1297617 , 0. ,\n", + " 0. ],\n", + " [0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ]], dtype=float32)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topics = matutils.corpus2dense(model[corpus], \n", + " num_terms=model.num_topics)\n", + "topics" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:35.047491Z", + "start_time": "2020-06-04T09:42:35.044455Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function sum:\n", + "\n", + "sum(...) method of numpy.ndarray instance\n", + " a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)\n", + " \n", + " Return the sum of the array elements over the given axis.\n", + " \n", + " Refer to `numpy.sum` for full documentation.\n", + " \n", + " See Also\n", + " --------\n", + " numpy.sum : equivalent function\n", + "\n" + ] + } + ], + "source": [ + "# Return the sum of the array elements \n", + "help(topics.sum)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:38.638353Z", + "start_time": "2020-06-04T09:42:38.634653Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12.804762" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 第一个主题的词语总权重\n", + "topics[0].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:41.560433Z", + "start_time": "2020-06-04T09:42:41.554793Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 12.804762 , 46.931618 , 59.26241 , 2.2143471 ,\n", + " 0.67802817, 2.2622046 , 3.0233366 , 17.583002 ,\n", + " 6.1049767 , 31.3768 , 5.187127 , 19.628056 ,\n", + " 28.910555 , 10.1352 , 50.009346 , 15.996473 ,\n", + " 5.852129 , 8.703129 , 27.550959 , 1.7105172 ,\n", + " 34.74263 , 10.605288 , 8.8306265 , 6.129652 ,\n", + " 32.472363 , 17.814533 , 30.612034 , 3.6717734 ,\n", + " 1.3829954 , 6.2177258 , 4.56864 , 60.068176 ,\n", + " 15.500901 , 2.0661712 , 61.25909 , 30.734415 ,\n", + " 26.266684 , 9.214224 , 45.01914 , 8.051634 ,\n", + " 21.10302 , 7.245178 , 6.3920393 , 9.723191 ,\n", + " 12.641086 , 5.443553 , 7.4248734 , 20.882336 ,\n", + " 3.1587074 , 68.153 , 13.018834 , 20.467255 ,\n", + " 11.351901 , 11.534626 , 27.307915 , 4.6151204 ,\n", + " 22.742682 , 11.901951 , 18.917133 , 3.0844703 ,\n", + " 30.553188 , 23.919426 , 9.98344 , 0.15819305,\n", + " 44.44998 , 37.791748 , 14.777735 , 25.710835 ,\n", + " 72.76366 , 8.23982 , 84.36966 , 9.064886 ,\n", + " 9.127086 , 7.0372686 , 14.222766 , 117.03595 ,\n", + " 3.6701863 , 5.8301563 , 57.57015 , 7.4279833 ,\n", + " 2.1190832 , 45.842808 , 129.05334 , 4.9494762 ,\n", + " 19.346832 , 29.210262 , 12.784327 , 55.087257 ,\n", + " 2.5386662 , 15.4423065 , 23.01918 , 25.509815 ,\n", + " 49.456696 , 28.466774 , 33.971733 , 15.042379 ,\n", + " 2.3246293 , 22.830368 , 9.793747 , 14.287384 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 将每一个主题的词语总权重算出来\n", + "weight = topics.sum(1)\n", + "weight" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:48.660692Z", + "start_time": "2020-06-04T09:42:48.657707Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function argmax:\n", + "\n", + "argmax(...) method of numpy.ndarray instance\n", + " a.argmax(axis=None, out=None)\n", + " \n", + " Return indices of the maximum values along the given axis.\n", + " \n", + " Refer to `numpy.argmax` for full documentation.\n", + " \n", + " See Also\n", + " --------\n", + " numpy.argmax : equivalent function\n", + "\n" + ] + } + ], + "source": [ + "# 找到最大值在哪里\n", + "\n", + "help(weight.argmax) " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:51.573931Z", + "start_time": "2020-06-04T09:42:51.570725Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82\n" + ] + } + ], + "source": [ + "# 找出具有最大权重的主题是哪一个\n", + "max_topic = weight.argmax()\n", + "print(max_topic)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:42:57.194080Z", + "start_time": "2020-06-04T09:42:57.188284Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# Get the top 64 words for this topic\n", + "# Without the argument, show_topic would return only 10 words\n", + "words = model.show_topic(max_topic, 64)\n", + "words = np.array(words).T\n", + "words_freq=[float(i)*10000000 for i in words[1]]\n", + "words = list(zip(words[0], words_freq))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 主题词云" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:00.576787Z", + "start_time": "2020-06-04T09:43:00.574031Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "words = {i:j for i, j in words}" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:02.384580Z", + "start_time": "2020-06-04T09:43:02.089146Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from wordcloud import WordCloud\n", + "\n", + "fig = plt.figure(figsize=(15, 8),facecolor='white')\n", + "\n", + "wordcloud = WordCloud().generate_from_frequencies(words)\n", + "plt.imshow(wordcloud)\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 每个文档有多少主题\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:09.901961Z", + "start_time": "2020-06-04T09:43:06.264683Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# 每个文档有多少主题\n", + "num_topics_used = [len(model[doc]) for doc in corpus]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:12.801853Z", + "start_time": "2020-06-04T09:43:12.369170Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 画出来每个文档主题数量的直方图\n", + "\n", + "fig,ax = plt.subplots()\n", + "ax.hist(num_topics_used, np.arange(27))\n", + "ax.set_ylabel('$Number \\;of\\; documents$', fontsize = 20)\n", + "ax.set_xlabel('$Number \\;of \\;topics$', fontsize = 20)\n", + "fig.tight_layout()\n", + "#fig.savefig('Figure_04_01.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### We can see that about 150 documents have 5 topics, \n", + "- while the majority deal with around 10 to 12 of them. \n", + " - No document talks about more than 30 topics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 改变超级参数alpha" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:25.410042Z", + "start_time": "2020-06-04T09:43:19.633952Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# Now, repeat the same exercise using alpha=1.0\n", + "# You can edit the constant below to play around with this parameter\n", + "ALPHA = 1.0\n", + "model1 = models.ldamodel.LdaModel(\n", + " corpus, num_topics=NUM_TOPICS, id2word=corpus.id2word, \n", + " alpha=ALPHA)\n", + "\n", + "num_topics_used1 = [len(model1[doc]) for doc in corpus]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:28.399532Z", + "start_time": "2020-06-04T09:43:28.065483Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "ax.hist([num_topics_used, num_topics_used1], np.arange(42))\n", + "ax.set_ylabel('$Number \\;of\\; documents$', fontsize = 20)\n", + "ax.set_xlabel('$Number \\;of \\;topics$', fontsize = 20)\n", + "# The coordinates below were fit by trial and error to look good\n", + "plt.text(9, 223, r'default alpha')\n", + "plt.text(26, 156, 'alpha=1.0')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 问题：$\\alpha$引起主题数量分布的变化意味着什么？" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 从原始文本到主题模型：一个完整的例子\n", + "\n", + "刚才的例子使用的是一个已经处理好的语料库，已经构建完整的语料和字典，并清洗好了数据。" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:43:59.968659Z", + "start_time": "2020-06-04T09:43:59.952530Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "with open('/Users/datalab/bigdata/ap/ap.txt', 'r') as f:\n", + " dat = f.readlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:00.843200Z", + "start_time": "2020-06-04T09:44:00.839352Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['\\n',\n", + " ' AP881218-0003 \\n',\n", + " '\\n',\n", + " \" A 16-year-old student at a private Baptist school who allegedly killed one teacher and wounded another before firing into a filled classroom apparently ``just snapped,'' the school's pastor said. ``I don't know how it could have happened,'' said George Sweet, pastor of Atlantic Shores Baptist Church. ``This is a good, Christian school. We pride ourselves on discipline. Our kids are good kids.'' The Atlantic Shores Christian School sophomore was arrested and charged with first-degree murder, attempted murder, malicious assault and related felony charges for the Friday morning shooting. Police would not release the boy's name because he is a juvenile, but neighbors and relatives identified him as Nicholas Elliott. Police said the student was tackled by a teacher and other students when his semiautomatic pistol jammed as he fired on the classroom as the students cowered on the floor crying ``Jesus save us! God save us!'' Friends and family said the boy apparently was troubled by his grandmother's death and the divorce of his parents and had been tormented by classmates. Nicholas' grandfather, Clarence Elliott Sr., said Saturday that the boy's parents separated about four years ago and his maternal grandmother, Channey Williams, died last year after a long illness. The grandfather also said his grandson was fascinated with guns. ``The boy was always talking about guns,'' he said. ``He knew a lot about them. He knew all the names of them _ none of those little guns like a .32 or a .22 or nothing like that. He liked the big ones.'' The slain teacher was identified as Karen H. Farley, 40. The wounded teacher, 37-year-old Sam Marino, was in serious condition Saturday with gunshot wounds in the shoulder. Police said the boy also shot at a third teacher, Susan Allen, 31, as she fled from the room where Marino was shot. He then shot Marino again before running to a third classroom where a Bible class was meeting. The youngster shot the glass out of a locked door before opening fire, police spokesman Lewis Thurston said. When the youth's pistol jammed, he was tackled by teacher Maurice Matteson, 24, and other students, Thurston said. ``Once you see what went on in there, it's a miracle that we didn't have more people killed,'' Police Chief Charles R. Wall said. Police didn't have a motive, Detective Tom Zucaro said, but believe the boy's primary target was not a teacher but a classmate. Officers found what appeared to be three Molotov cocktails in the boy's locker and confiscated the gun and several spent shell casings. Fourteen rounds were fired before the gun jammed, Thurston said. The gun, which the boy carried to school in his knapsack, was purchased by an adult at the youngster's request, Thurston said, adding that authorities have interviewed the adult, whose name is being withheld pending an investigation by the federal Bureau of Alcohol, Tobacco and Firearms. The shootings occurred in a complex of four portable classrooms for junior and senior high school students outside the main building of the 4-year-old school. The school has 500 students in kindergarten through 12th grade. Police said they were trying to reconstruct the sequence of events and had not resolved who was shot first. The body of Ms. Farley was found about an hour after the shootings behind a classroom door.\\n\",\n", + " ' \\n',\n", + " '\\n']" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 需要进行文本清洗\n", + "dat[:6]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:01.665699Z", + "start_time": "2020-06-04T09:44:01.661856Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'<'" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 如果包含'<'就去掉这一行\n", + "dat[4].strip()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:02.447904Z", + "start_time": "2020-06-04T09:44:02.438279Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6897 string index out of range\n", + "9183 string index out of range\n" + ] + } + ], + "source": [ + "# 选取前100篇文档\n", + "docs = []\n", + "for k, i in enumerate(dat): #[:100]:\n", + " #print(k)\n", + " try:\n", + " if i.strip()[0] != '<':\n", + " docs.append(i)\n", + " except Exception as e:\n", + " print(k, e)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:03.375245Z", + "start_time": "2020-06-04T09:44:03.370209Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2248" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:04.361249Z", + "start_time": "2020-06-04T09:44:04.357282Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "' A Navy anti-submarine helicopter crashed while preparing to land on a frigate in the North Arabian Sea and its three crewmen were presumed dead, officials announced Friday. The SH-2F helicopter was returning to the USS Barbey at the end of a dawn flight and crashed on approach, said Ken Mitchell, spokesman for North Island Naval Air Station. The Barbey is based at San Diego. The crash occurred about 7 p.m. PST Thursday, Mitchell said. Lost and presumed dead were Lt. Cmdr. Gerald C. Pelz, 37, of Coronado, Calif., Lt. j.g. Gerald T. Ramsdell, age unknown, of Ridgewood, N.J., and the anti-submarine warfare operator, Petty Officer 3rd Class William E. Martinie, 24, of Peoria, Ill. Helicopters from the aircraft carrier USS Nimitz, 70 miles away, and boats from the Barbey and USS California unsuccessfully searched for survivors. The craft was part of Helicopter Anti-Submarine Squadron Light 33.\\n'" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:06.533656Z", + "start_time": "2020-06-04T09:44:06.494340Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# 定义一个函数，进一步清洗\n", + "def clean_doc(doc):\n", + " doc = doc.replace('.', '').replace(',', '')\n", + " doc = doc.replace('``', '').replace('\"', '')\n", + " doc = doc.replace('_', '').replace(\"'\", '')\n", + " doc = doc.replace('!', '')\n", + " return doc\n", + "docs = [clean_doc(doc) for doc in docs]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:07.540612Z", + "start_time": "2020-06-04T09:44:07.423011Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "texts = [[i for i in doc.lower().split()] for doc in docs]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## 停用词" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:10.041761Z", + "start_time": "2020-06-04T09:44:09.412209Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import nltk\n", + "#nltk.download()\n", + "# 会打开一个窗口，选择book，download，待下载完毕就可以使用了。" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:10.847448Z", + "start_time": "2020-06-04T09:44:10.837717Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from nltk.corpus import stopwords\n", + "stop = stopwords.words('english') # 如果此处出错，请执行上一个block的代码\n", + "# 停用词stopword：在英语里面会遇到很多a,the,or等使用频率很多的字或词,常为冠词、介词、副词或连词等。\n", + "# 人类语言包含很多功能词。与其他词相比，功能词没有什么实际含义。" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:11.979980Z", + "start_time": "2020-06-04T09:44:11.976081Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"i me my myself we our ours ourselves you you're you've you'll you'd your yours yourself yourselves he him his himself she she's her hers herself it it's its itself they them their theirs themselves what which who whom this that that'll these those am is are was were be been being have has had having do does did doing a an the and but if or because as until while of at by for with about against between into through during before after above below to from up down in out on off over under again further then once here there when where why how all any both each few more most other some such no nor not only own same so than too very s t can will just don don't should should've now d ll m o re ve y ain aren aren't couldn couldn't didn didn't doesn doesn't hadn hadn't hasn hasn't haven haven't isn isn't ma mightn mightn't mustn mustn't needn needn't shan shan't shouldn shouldn't wasn wasn't weren weren't won won't wouldn wouldn't\"" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join(stop) " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:12.986817Z", + "start_time": "2020-06-04T09:44:12.982593Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'done see together just serious from become seeming system sincere wherever well above ie etc will inc afterwards only do already how always herself too whoever onto some go twelve least few him which say last yourself ourselves show didn twenty beside seem everyone he myself more doesn latterly enough own the themselves cannot where cant thru never not anyhow must now neither your hundred really quite whereupon became herein describe as his them mine detail sixty during why was otherwise of eleven with de fifteen or fire whereafter ltd sometime bottom someone since eg among further becoming about off thin per this whenever something various whence hereby name whither my re another behind such five ten toward mill first did often in our were that please thereby after nor via until everywhere hers moreover doing whom a same most before us they her without made although who back former unless you wherein i himself mostly around thereupon latter though may however along find other if against might somehow three whereby perhaps rather front ours had because whole give while should eight six meanwhile than third don call full does are even here anyway still computer km no get yourselves any make therefore yet to whose forty out within several anything elsewhere co has indeed empty would somewhere couldnt fifty two what amongst can it regarding noone on under else both over therein into then others very be amount their sometimes due put and could nine thence throughout towards whereas nowhere also seemed nevertheless an keep its those every con four take hence besides move whether all amoungst am at un none hereafter down been formerly kg for using one hasnt interest anywhere thus through next anyone fill seems cry between up used top these nothing much yours we have either there everything she each beyond by many being bill ever found beforehand again nobody below part side so thereafter is but alone except hereupon becomes almost thick namely itself once upon less across me when whatever'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gensim.parsing.preprocessing import STOPWORDS\n", + "\n", + "' '.join(STOPWORDS)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:17.106781Z", + "start_time": "2020-06-04T09:44:17.104239Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "stop.append('said')" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:18.150997Z", + "start_time": "2020-06-04T09:44:17.927613Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# 计算每一个词的频数\n", + "from collections import defaultdict\n", + "frequency = defaultdict(int)\n", + "for text in texts:\n", + " for token in text:\n", + " frequency[token] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:20.408677Z", + "start_time": "2020-06-04T09:44:18.863170Z" + } + }, + "outputs": [], + "source": [ + "# 去掉只出现一次的词和\n", + "texts = [[token for token in text \\\n", + " if frequency[token] > 1 and token not in stop]\n", + " for text in texts]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:22.088046Z", + "start_time": "2020-06-04T09:44:22.084594Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "' Here is a summary of developments in forest and brush fires in Western states:\\n'" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "docs[8]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:22.837071Z", + "start_time": "2020-06-04T09:44:22.833355Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'stirbois 2 man extreme-right national front party leader jean-marie le pen died saturday automobile accident police 43 stirbois attended political meeting friday city dreux 60 miles west paris traveling toward capital car ran road smashed tree 2:40 police stirbois secretary-general national front member party leadership since 1981 born jan 30 1945 paris held degrees law marketing headed printing business stirbois active several extreme-right political movements joining national front 1977 1982 126 percent vote local elections district west paris highest vote percentage france right-wing candidate year half later election deputy mayor dreux stirbois elected deputy national assembly 1986 lost seat legislative elections last summer national front founded le pen 1972 strongly opposed frances highly centralized bureaucratic government personal taxes favors death penalty priority french citizens jobs stopping immigration first round years presidential elections le pen surprising 144 percent vote worrying many feared national front could awaken racist sentiments'" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' '.join(texts[9])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T00:53:55.569764", + "start_time": "2017-09-23T00:53:55.560716" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# help(corpora.Dictionary)\n", + "\n", + "Help on class Dictionary in module gensim.corpora.dictionary:\n", + "\n", + "class Dictionary(gensim.utils.SaveLoad, _abcoll.Mapping)\n", + "- Dictionary encapsulates the mapping between normalized words and their integer ids.\n", + " \n", + "- The main function is **doc2bow**\n", + " - which converts a collection of words to its bag-of-words representation: a list of (word_id, word_frequency) 2-tuples.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:27.002387Z", + "start_time": "2020-06-04T09:44:25.967797Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "dictionary = corpora.Dictionary(texts)\n", + "lda_corpus = [dictionary.doc2bow(text) for text in texts]\n", + "# The function doc2bow() simply counts the number of occurences of each distinct word, \n", + "# converts the word to its integer word id and returns the result as a sparse vector. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:36.654181Z", + "start_time": "2020-06-04T09:44:31.031321Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "NUM_TOPICS = 100\n", + "lda_model = models.ldamodel.LdaModel(\n", + " lda_corpus, num_topics=NUM_TOPICS, \n", + " id2word=dictionary, alpha=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 使用pyLDAvis可视化主题模型\n", + "http://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb\n", + "\n", + "> # pip install pyldavis" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:44:56.378734Z", + "start_time": "2020-06-04T09:44:49.835669Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing /Users/datalab/Library/Caches/pip/wheels/98/71/24/513a99e58bb6b8465bae4d2d5e9dba8f0bef8179e3051ac414/pyLDAvis-2.1.2-py2.py3-none-any.whl\n", + "Requirement already satisfied: numexpr in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (2.7.1)\n", + "Requirement already satisfied: future in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (0.18.2)\n", + "Requirement already satisfied: scipy>=0.18.0 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (1.4.1)\n", + "Requirement already satisfied: joblib>=0.8.4 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (0.14.1)\n", + "Requirement already satisfied: numpy>=1.9.2 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (1.18.1)\n", + "Collecting funcy\n", + " Downloading funcy-1.14.tar.gz (548 kB)\n", + "\u001b[K |████████████████████████████████| 548 kB 617 kB/s eta 0:00:01\n", + "\u001b[?25hRequirement already satisfied: jinja2>=2.7.2 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (2.11.1)\n", + "Requirement already satisfied: wheel>=0.23.0 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (0.34.2)\n", + "Requirement already satisfied: pandas>=0.17.0 in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (1.0.1)\n", + "Requirement already satisfied: pytest in /opt/anaconda3/lib/python3.7/site-packages (from pyldavis) (5.3.5)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.7/site-packages (from jinja2>=2.7.2->pyldavis) (1.1.1)\n", + "Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.17.0->pyldavis) (2019.3)\n", + "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.17.0->pyldavis) (2.8.1)\n", + "Requirement already satisfied: py>=1.5.0 in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (1.8.1)\n", + "Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (20.1)\n", + "Requirement already satisfied: attrs>=17.4.0 in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (19.3.0)\n", + "Requirement already satisfied: more-itertools>=4.0.0 in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (8.2.0)\n", + "Requirement already satisfied: pluggy<1.0,>=0.12 in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (0.13.1)\n", + "Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (0.1.8)\n", + "Requirement already satisfied: importlib-metadata>=0.12 in /opt/anaconda3/lib/python3.7/site-packages (from pytest->pyldavis) (1.5.0)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.17.0->pyldavis) (1.14.0)\n", + "Requirement already satisfied: pyparsing>=2.0.2 in /opt/anaconda3/lib/python3.7/site-packages (from packaging->pytest->pyldavis) (2.4.6)\n", + "Requirement already satisfied: zipp>=0.5 in /opt/anaconda3/lib/python3.7/site-packages (from importlib-metadata>=0.12->pytest->pyldavis) (2.2.0)\n", + "Building wheels for collected packages: funcy\n", + " Building wheel for funcy (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=c6d0439921d3af5dd3b061cf33fd4b2c1767a4d4fafa2e7923791441d9f79e6c\n", + " Stored in directory: /Users/datalab/Library/Caches/pip/wheels/3c/33/97/805b282e129f60bb4e87cea622338f30b65f21eaf65219971f\n", + "Successfully built funcy\n", + "Installing collected packages: funcy, pyldavis\n", + "Successfully installed funcy-1.14 pyldavis-2.1.2\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install pyldavis" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-04T09:45:34.817677Z", + "start_time": "2020-06-04T09:45:00.551190Z" + }, + "code_folding": [], + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pyldavis\n", + "import pyLDAvis.gensim\n", + "\n", + "ap_data = pyLDAvis.gensim.prepare(lda_model, lda_corpus, dictionary, mds = 'mmds')\n", + "\n", + "pyLDAvis.enable_notebook()\n", + "pyLDAvis.display(ap_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-19T07:35:57.717698Z", + "start_time": "2018-06-19T07:35:36.913789Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pyLDAvis.gensim\n", + "\n", + "ap_data = pyLDAvis.gensim.prepare(lda_model, lda_corpus, dictionary, mds = 'tsne')\n", + "\n", + "pyLDAvis.enable_notebook()\n", + "pyLDAvis.display(ap_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-14T09:11:01.823632Z", + "start_time": "2019-06-14T09:10:48.064806Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Note: if you're in the IPython notebook, pyLDAvis.show() is not the best command\n", + " to use. Consider using pyLDAvis.display(), or pyLDAvis.enable_notebook().\n", + " See more information at http://pyLDAvis.github.io/quickstart.html .\n", + "\n", + "You must interrupt the kernel to end this command\n", + "\n", + "Serving to http://127.0.0.1:8891/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [14/Jun/2019 17:10:48] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [14/Jun/2019 17:10:48] \"GET /LDAvis.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [14/Jun/2019 17:10:48] \"GET /d3.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [14/Jun/2019 17:10:48] \"GET /LDAvis.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [14/Jun/2019 17:10:48] code 404, message Not Found\n", + "127.0.0.1 - - [14/Jun/2019 17:10:48] \"GET /favicon.ico HTTP/1.1\" 404 -\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "pyLDAvis.show(ap_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-14T09:07:33.030165Z", + "start_time": "2019-06-14T09:07:32.944971Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "pyLDAvis.save_html(ap_data, '../data/ap_ldavis2.html')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![image.png](img/chengjun2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 对2016年政府工作报告建立主题模型" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# pip install jieba\n", + "> https://github.com/fxsjy/jieba\n", + "\n", + "# pip install wordcloud\n", + "> https://github.com/amueller/word_cloud\n", + "\n", + "# pip install gensim" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "ExecuteTime": { + "end_time": "2018-06-19T07:19:40.047489Z", + "start_time": "2018-06-19T07:19:40.041908Z" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "import gensim\n", + "from gensim import corpora, models, similarities\n", + "from gensim.utils import simple_preprocess\n", + "from gensim.parsing.preprocessing import STOPWORDS\n", + "import matplotlib\n", + "matplotlib.rc(\"savefig\", dpi=400)\n", + "#matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei'] #指定默认字体 " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T20:58:22.151180", + "start_time": "2017-09-21T20:58:21.427623" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "import urllib2\n", + "from bs4 import BeautifulSoup\n", + "import sys\n", + "\n", + "url2016 = 'http://news.xinhuanet.com/fortune/2016-03/05/c_128775704.htm'\n", + "content = urllib2.urlopen(url2016).read()\n", + "soup = BeautifulSoup(content) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T20:58:22.932824", + "start_time": "2017-09-21T20:58:22.927673" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "政府工作报告\n", + "——2016年3月5日在第十二届全国人民代表大会第四次会议上\n", + "国务院总理 李克强\n", + "各位代表：\n", + "　　现在，我代表国务院，向大会报告政府工作，请予审议，并请全国政协各位委员提出意见。\n", + "　　一、2015年工作回顾\n", + "　　过去一年，我国发展面临多重困难和严峻挑战。在以习近平同志为总书记的党中央坚强领导下，全国各族人民以坚定的信心和非凡的勇气，攻坚克难，开拓进取，经济社会发展稳中有进、稳中有好，完成了全年主要目标任务，改革开放和社会主义现代化建设取得新的重大成就。\n", + "　　——经济运行保持在合理区间。国内生产总值达到67.7万亿元，增长6.9%，在世界主要经济体中位居前列。粮食产量实现\"十二连增\"，居民消费价格涨幅保持较低水平。特别是就业形势总体稳定，城镇新增就业1312万人，超过全年预期目标，成为经济运行的一大亮点。\n", + "　　——结构调整取得积极进展。服务业在国内生产总值中的比重上升到50.5%，首次占据\"半壁江山\"。消费对经济增长的贡献率达到66.4%。高技术产业和装备制造业增速快于一般工业。单位国内生产总值能耗下降5.6%。\n", + "　　——发展新动能加快成长。创新驱动发展战略持续推进，互联网与各行业加速融合，新兴产业快速增长。大众创业、万众创新蓬勃发展，全年新登记注册企业增长21.6%，平均每天新增1.2万户。新动能对稳就业、促升级发挥了突出作用，正在推动经济社会发生深刻变革。\n" + ] + } + ], + "source": [ + "gov_report_2016 = [s.text for s in soup('p')]\n", + "for i in gov_report_2016[:10]:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T20:58:23.648813", + "start_time": "2017-09-21T20:58:23.640728" + }, + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "def clean_txt(txt):\n", + " for i in [u'、', u'，', u'—', u'！', u'。', u'《', u'》', u'（', u'）']:\n", + " txt = txt.replace(i, ' ')\n", + " return txt" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T20:58:24.403545", + "start_time": "2017-09-21T20:58:24.400002" + }, + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "gov_report_2016 = [clean_txt(i) for i in gov_report_2016]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:55.291079", + "start_time": "2017-09-21T21:23:55.287292" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "109" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(gov_report_2016)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:56.663064", + "start_time": "2017-09-21T21:23:56.659405" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "政府工作报告\n", + " 2016年3月5日在第十二届全国人民代表大会第四次会议上\n", + "国务院总理 李克强\n", + "各位代表：\n", + "　　现在 我代表国务院 向大会报告政府工作 请予审议 并请全国政协各位委员提出意见 \n", + "　　一 2015年工作回顾\n", + "　　过去一年 我国发展面临多重困难和严峻挑战 在以习近平同志为总书记的党中央坚强领导下 全国各族人民以坚定的信心和非凡的勇气 攻坚克难 开拓进取 经济社会发展稳中有进 稳中有好 完成了全年主要目标任务 改革开放和社会主义现代化建设取得新的重大成就 \n", + "　　 经济运行保持在合理区间 国内生产总值达到67.7万亿元 增长6.9% 在世界主要经济体中位居前列 粮食产量实现\"十二连增\" 居民消费价格涨幅保持较低水平 特别是就业形势总体稳定 城镇新增就业1312万人 超过全年预期目标 成为经济运行的一大亮点 \n", + "　　 结构调整取得积极进展 服务业在国内生产总值中的比重上升到50.5% 首次占据\"半壁江山\" 消费对经济增长的贡献率达到66.4% 高技术产业和装备制造业增速快于一般工业 单位国内生产总值能耗下降5.6% \n", + "　　 发展新动能加快成长 创新驱动发展战略持续推进 互联网与各行业加速融合 新兴产业快速增长 大众创业 万众创新蓬勃发展 全年新登记注册企业增长21.6% 平均每天新增1.2万户 新动能对稳就业 促升级发挥了突出作用 正在推动经济社会发生深刻变革 \n" + ] + } + ], + "source": [ + "for i in gov_report_2016[:10]:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:57.271323", + "start_time": "2017-09-21T21:23:57.267238" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "103" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(gov_report_2016[5:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:57.938860", + "start_time": "2017-09-21T21:23:57.934178" + }, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/chengjun/GitHub/cjc'" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the Working Directory \n", + "import os\n", + "os.getcwd() \n", + "os.chdir('/Users/chengjun/github/cjc/')\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:58.536608", + "start_time": "2017-09-21T21:23:58.531037" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "filename = 'data/stopwords.txt'\n", + "stopwords = {}\n", + "f = open(filename, 'r')\n", + "line = f.readline().rstrip()\n", + "while line:\n", + " stopwords.setdefault(line, 0)\n", + " stopwords[line.decode('utf-8')] = 1\n", + " line = f.readline().rstrip()\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:23:59.478892", + "start_time": "2017-09-21T21:23:59.474891" + }, + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "adding_stopwords = [u'我们', u'要', u'地', u'有', u'这', u'人',\n", + " u'发展',u'建设',u'加强',u'继续',u'对',u'等',\n", + " u'推进',u'工作',u'增加']\n", + "for s in adding_stopwords: stopwords[s]=10" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T01:11:48.864554", + "start_time": "2017-09-23T01:11:47.406335" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "import jieba.analyse\n", + "\n", + "def cleancntxt(txt, stopwords):\n", + " tfidf1000= jieba.analyse.extract_tags(txt, topK=1000, withWeight=False)\n", + " seg_generator = jieba.cut(txt, cut_all=False)\n", + " seg_list = [i for i in seg_generator if i not in stopwords]\n", + " seg_list = [i for i in seg_list if i != u' ']\n", + " seg_list = [i for i in seg_list if i in tfidf1000]\n", + " return(seg_list)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T01:11:52.689478", + "start_time": "2017-09-23T01:11:52.677888" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "def getCorpus(data):\n", + " processed_docs = [tokenize(doc) for doc in data]\n", + " word_count_dict = gensim.corpora.Dictionary(processed_docs)\n", + " print (\"In the corpus there are\", len(word_count_dict), \"unique tokens\")\n", + " word_count_dict.filter_extremes(no_below=5, no_above=0.2) # word must appear >5 times, and no more than 10% documents\n", + " print (\"After filtering, in the corpus there are only\", len(word_count_dict), \"unique tokens\")\n", + " bag_of_words_corpus = [word_count_dict.doc2bow(pdoc) for pdoc in processed_docs]\n", + " return bag_of_words_corpus, word_count_dict\n", + "\n", + "\n", + "def getCnCorpus(data):\n", + " processed_docs = [cleancntxt(doc) for doc in data]\n", + " word_count_dict = gensim.corpora.Dictionary(processed_docs)\n", + " print (\"In the corpus there are\", len(word_count_dict), \"unique tokens\")\n", + " #word_count_dict.filter_extremes(no_below=5, no_above=0.2) \n", + " # word must appear >5 times, and no more than 10% documents\n", + " print (\"After filtering, in the corpus there are only\", len(word_count_dict), \"unique tokens\")\n", + " bag_of_words_corpus = [word_count_dict.doc2bow(pdoc) for pdoc in processed_docs]\n", + " return bag_of_words_corpus, word_count_dict\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-23T01:11:54.942191", + "start_time": "2017-09-23T01:11:54.933808" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "def inferTopicNumber(bag_of_words_corpus, num, word_count_dict):\n", + " lda_model = gensim.models.LdaModel(bag_of_words_corpus, num_topics=num, id2word=word_count_dict, passes=10)\n", + " _ = lda_model.print_topics(-1) #use _ for throwaway variables.\n", + " logperplexity = lda_model.log_perplexity(bag_of_words_corpus)\n", + " return logperplexity\n", + "\n", + "def fastInferTopicNumber(bag_of_words_corpus, num, word_count_dict):\n", + " lda_model = gensim.models.ldamulticore.LdaMulticore(corpus=bag_of_words_corpus, num_topics=num, \\\n", + " id2word=word_count_dict,\\\n", + " workers=None, chunksize=2000, passes=2, \\\n", + " batch=False, alpha='symmetric', eta=None, \\\n", + " decay=0.5, offset=1.0, eval_every=10, \\\n", + " iterations=50, gamma_threshold=0.001, random_state=None)\n", + " _ = lda_model.print_topics(-1) #use _ for throwaway variables.\n", + " logperplexity = lda_model.log_perplexity(bag_of_words_corpus)\n", + " return logperplexity" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:01.279551", + "start_time": "2017-09-21T21:24:01.274876" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "屠呦呦 获得 诺贝尔 医学奖\n" + ] + } + ], + "source": [ + "import jieba.analyse\n", + "\n", + "jieba.add_word(u'屠呦呦', freq=None, tag=None)\n", + "#del_word(word) \n", + "\n", + "print (' '.join(cleancntxt(u'屠呦呦获得了诺贝尔医学奖。', stopwords)))" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:06.715241", + "start_time": "2017-09-21T21:24:06.378441" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the corpus there are 2622 unique tokens\n" + ] + } + ], + "source": [ + "import gensim\n", + "\n", + "processed_docs = [cleancntxt(doc, stopwords) for doc in gov_report_2016[5:-1]]\n", + "word_count_dict = gensim.corpora.Dictionary(processed_docs)\n", + "print (\"In the corpus there are\", len(word_count_dict), \"unique tokens\")\n", + "# word_count_dict.filter_extremes(no_below=5, no_above=0.2) # word must appear >5 times, and no more than 10% documents\n", + "# print \"After filtering, in the corpus there are only\", len(word_count_dict), \"unique tokens\"\n", + "bag_of_words_corpus = [word_count_dict.doc2bow(pdoc) for pdoc in processed_docs]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:13.773158", + "start_time": "2017-09-21T21:24:09.218857" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "tfidf = models.TfidfModel(bag_of_words_corpus )\n", + "corpus_tfidf = tfidf[bag_of_words_corpus ]\n", + "#lda_model = gensim.models.LdaModel(corpus_tfidf, num_topics=20, id2word=word_count_dict, passes=10)\n", + "lda_model = gensim.models.LdaMulticore(corpus_tfidf, num_topics=20, id2word=word_count_dict, passes=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:07:18.364889", + "start_time": "2017-09-21T21:06:39.818687" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "perplexity_list = [inferTopicNumber(bag_of_words_corpus, num, word_count_dict) for num in [5, 10, 15, 20, 25, 30 ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:08:07.599996", + "start_time": "2017-09-21T21:08:06.435130" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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OOSf54Q+TpZbq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\nMDfGuLDY9XP0ZhBDkiRJkiRJktQhrFuXnjFpODnjX/8qXr399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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([5, 10, 15, 20, 25, 30 ], perplexity_list)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:18.365316", + "start_time": "2017-09-21T21:24:18.359148" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(15,\n", + " u'0.005*\"\\u5c31\\u4e1a\" + 0.003*\"\\u56fd\\u6709\\u4f01\\u4e1a\" + 0.002*\"\\u8c08\\u5224\" + 0.002*\"\\u81ea\\u8d38\\u533a\" + 0.002*\"\\u534f\\u5b9a\" + 0.002*\"\\u91d1\\u878d\" + 0.002*\"\\u56fd\\u6709\\u8d44\\u4ea7\" + 0.002*\"\\u5e02\\u573a\\u5316\" + 0.002*\"\\u521b\\u4e1a\" + 0.002*\"\\u5747\\u8861\"'),\n", + " (8,\n", + " u'0.005*\"\\u4ee5\\u4e0b\" + 0.004*\"\\u4e00\\u5e74\" + 0.003*\"\\u4e3b\\u8981\" + 0.003*\"\\u533b\\u4fdd\" + 0.002*\"\\u6559\\u80b2\" + 0.002*\"\\u533b\\u7597\" + 0.002*\"\\u5b66\\u6821\" + 0.002*\"\\u514d\\u9664\" + 0.002*\"\\u5b66\\u6742\\u8d39\" + 0.002*\"C919\"'),\n", + " (10,\n", + " u'0.004*\"\\u4e24\\u5cb8\" + 0.004*\"\\u9700\\u6c42\" + 0.003*\"\\u6295\\u8d44\" + 0.002*\"\\u6210\\u7ee9\" + 0.002*\"\\u767e\\u5206\\u70b9\" + 0.002*\"\\u6709\\u6548\" + 0.002*\"\\u8fd9\\u4e9b\" + 0.002*\"\\u9532\\u800c\\u4e0d\\u820d\" + 0.002*\"\\u8150\\u8d25\\u5206\\u5b50\" + 0.002*\"\\u89c4\\u5b9a\"')]" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lda_model.print_topics(3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:19.466666", + "start_time": "2017-09-21T21:24:19.458674" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"创新\" \"企业\" \"创业\" \"党风廉政\" \"财政支出\" \"就业\" \"经济运行\" \"全年\" \"范围\" \"所有\"\n", + "\"更加\" \"支付\" \"取消\" \"审批\" \"存款\" \"如期\" \"光明\" \"美好\" \"前景\" \"将会\"\n", + "\"海洋\" \"合作\" \"地区\" \"产能\" \"支持\" \"金融\" \"存在\" \"一些\" \"去年\" \"基金\"\n", + "\"财政赤字\" \"领导人\" \"论坛\" \"安排\" \"亿元\" \"峰会\" \"地方\" \"债券\" \"联合国\" \"万亿元\"\n", + "\"万公里\" \"农村\" \"城乡\" \"救助\" \"里程\" \"协调\" \"重大\" \"覆盖\" \"主要\" \"突出\"\n", + "\"农业\" \"提高\" \"加快\" \"改革\" \"实施\" \"保护\" \"政策\" \"人民\" \"基本\" \"民族\"\n", + "\"住房\" \"考虑\" \"就业\" \"6.5\" \"文化\" \"预期\" \"相衔接\" \"有利于\" \"全民\" \"补贴\"\n", + "\"节能\" \"环保\" \"消费\" \"国民经济\" \"第十三个\" \"增长\" \"国内\" \"规划\" \"生产总值\" \"五年\"\n", + "\"以下\" \"一年\" \"主要\" \"医保\" \"教育\" \"医疗\" \"学校\" \"免除\" \"学杂费\" \"C919\"\n", + "\"军队\" \"国防\" \"强军\" \"政治\" \"领导\" \"一年\" \"领域\" \"鱼水情深\" \"战备\" \"武装警察\"\n", + "\"两岸\" \"需求\" \"投资\" \"成绩\" \"百分点\" \"有效\" \"这些\" \"锲而不舍\" \"腐败分子\" \"规定\"\n", + "\"2016\" \"重点\" \"回顾\" \"八个\" \"2015\" \"脱贫\" \"扶贫\" \"做好\" \"接受\" \"今年\"\n", + "\"民生\" \"非公有制\" \"时期\" \"十三\" \"举措\" \"竞争\" \"任务\" \"对外开放\" \"重大\" \"主要\"\n", + "\"各位\" \"代表\" \"安全\" \"伟大\" \"民主\" \"作出\" \"富强\" \"聚力\" \"紧密\" \"复兴\"\n", + "\"合作\" \"依法\" \"地方\" \"作用\" \"宗教\" \"大国\" \"产能\" \"关系\" \"政府\" \"维护\"\n", + "\"就业\" \"国有企业\" \"谈判\" \"自贸区\" \"协定\" \"金融\" \"国有资产\" \"市场化\" \"创业\" \"均衡\"\n", + "\"2020\" \"万元\" \"外商投资\" \"强国\" \"环境治理\" \"强力\" \"下决心\" \"事关\" \"双赢\" \"一批\"\n", + "\"13\" \"意识\" \"干事\" \"服务业\" \"辉煌成就\" \"左右\" \"货币政策\" \"制造\" \"广大干部\" \"品质\"\n", + "\"港澳\" \"居民\" \"自贸\" \"香港\" \"实际\" \"亿美元\" \"勇气\" \"重大成就\" \"信心\" \"稳中有\"\n", + "\"调控\" \"供给\" \"结构性\" \"政府\" \"城镇化\" \"货币政策\" \"出口\" \"新型\" \"区域\" \"宏观调控\"\n" + ] + } + ], + "source": [ + "topictermlist = lda_model.print_topics(-1)\n", + "top_words = [[j.split('*')[1] for j in i[1].split(' + ')] for i in topictermlist] \n", + "for i in top_words: \n", + " print (\" \".join(i) )" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:21.574906", + "start_time": "2017-09-21T21:24:21.546367" + }, + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "top_words_shares = [[j.split('*')[0] for j in i[1].split(' + ')] for i in topictermlist] \n", + "top_words_shares = [map(float, i) for i in top_words_shares]\n", + "def weightvalue(x):\n", + " return (x - np.min(top_words_shares))*40/(np.max(top_words_shares) -np.min(top_words_shares)) + 10\n", + " \n", + "top_words_shares = [map(weightvalue, i) for i in top_words_shares] \n", + "\n", + "def plotTopics(mintopics, maxtopics):\n", + " num_top_words = 10\n", + " plt.rcParams['figure.figsize'] = (20.0, 8.0) \n", + " n = 0\n", + " for t in range(mintopics , maxtopics):\n", + " plt.subplot(2, 15, n + 1) # plot numbering starts with 1\n", + " plt.ylim(0, num_top_words) # stretch the y-axis to accommodate the words\n", + " plt.xticks([]) # remove x-axis markings ('ticks')\n", + " plt.yticks([]) # remove y-axis markings ('ticks')\n", + " plt.title(u'主题 #{}'.format(t+1), size = 15)\n", + " words = top_words[t][0:num_top_words ]\n", + " words_shares = top_words_shares[t][0:num_top_words ]\n", + " for i, (word, share) in enumerate(zip(words, words_shares)):\n", + " plt.text(0.05, num_top_words-i-0.9, word, fontsize= np.log(share*1000))\n", + " n += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:29.024955", + "start_time": "2017-09-21T21:24:22.898347" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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jVbHxwuK62oWoq3Uta1WdiC4LvGuHYwRw/Ohju/4YePkO7t8VdYRZK4GYmcfW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\n0OLMof4wh7YpM98FvCsi7gy8ELjM2E2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PAAAg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\n5oJX+2LXK1eu6OTJk7px40aixU6DBg1yqI6bN29q/vz5iSZHJze5DgCQd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AHmWVI4SQ8iczMxO///47jh8/XmzShcmTJxd73bhx43D48GFMnjwZOTk5YBgGWVlZmDBhArZu\n3Yq5c+fKLeYLFy7g6dOnvIl806dPL/F8lmXh5+fH+36AfJMYySKjOCGEVARubm7Izs4GkH/fXbp0\nKapVq8Y7p2/fvliyZAm8vLy4/sXixYsxaNAgtG7dWqz6hIkXhPfhrl27yuaLkDI9fPiQd6yjoyP2\nJElCCJE1af8uL7jgiP7GJ4QQUpr69evj48ePpZ7DsmyRBKdCzZo1w7Nnz4r9LDs7G71795Y6RiD/\n7/aCyYQIIYQQaRXXT7pz5w5GjhyJ9+/f8+YTzJo1i/e8hhBCSMWwZcuWEj+bNGkSl3gBAObOnQtL\nS0up62RZFqdPn+b1bxSxQcShQ4e4+oUbRBBCCCGK8OLFCzg6OvLavt69e+Pq1asSl+nk5IT169dz\nx4MGDcLhw4eljpUQQsiPISoqCsD3/tG4ceO4zyQd/1PE/G5CSPlDiReU5Pr16xg7diw+fvzIW4ij\npaWFI0eOYPjw4SVeW6tWLVy4cAHDhw9HXFwc11h8+PABQ4cOhYeHB5YsWSKzWN++fYsdO3Zg2rRp\nJWZ9VabXr1+jYcOGlA2cEPLDGjFiBNLS0uRSdmxsLJcdnGEYtGnTBj///LPM6+ncubNI53l7e/Mm\nnDdv3hxGRkYyj4cQQoj8fPjwAWPGjMGNGzeKJF0ICAjAlClTSr3e1NQUDMPAwsKCS74gEAgwb948\n3Lp1C35+ftDU1JR53Dt37uReMwyDfv36lbowNywsDM+ePeO+Y3EDcuLu4FQWGvQjhJD8PkxQUBD3\n4L9hw4aYP39+seeuWrUKYWFh3CKgb9++YfLkybh+/TrU1dVFrjM+Pp430aBLly4y+S6kbIUTL1Sp\nUkVJkRBCyHfCnX4kWax6+/ZtREdH8xYJzZ07V2Z9HH9//1LHEUNDQ+Ho6FhqGampqbzjVq1alXju\nypUrYWFhgeDgYKknzd25c4d3vGPHDkRERBR7bp8+feDk5CRVfYQQUh4IN2koTlnjTqUlQRCOt8XG\nxkofZCkxSOLr169F+gHK0KFDhyIJ/ggh5EfXuHFjbCZoOwAAIABJREFUbNy4UezrIiMjuQnTDMPA\nwMAAhoaGIl0rnMcWHR2NsWPHIiUlBcD3iddubm5wdXUVOyahnJwcCAQCia9nWbZIm5uZmSnW2KM4\ntLS05FIuIYSQfDExMfj06RPXv9HW1sbQoUPlWufHjx8RGRnJ1Tlw4EC0aNFCrnUSQgghQtbW1khN\nTeXaIS0tLfj7+ys5KkIIIT+ywnMExowZI9J1pT03k3ZjDZq7TUjFRIkXlMDf3x92dnbIysri3mNZ\nFtWqVUNoaGiJO04UVLt2bVy6dAljxoxBdHQ0d6MWCARwdHTE5cuXsWfPHtSvX1/iOG/fvg0vLy8c\nP34cOTk5yMzMxIYNGyQuT17GjRuHd+/eYdq0aZg2bRqaNm2q7JAIIUSh5Jnpc8SIEYiMjOSOp0yZ\nAmdnZ7nVV5qkpCTs37+/yO4UQjdu3Chxh3RZePXqFe/Y1tYWS5cuLfbcfv36ITAwUG6xEEJIeRUd\nHY3Jkyfj3bt3RRLQBQYGwtzcXKRyxo8fj5CQEFhYWCAzM5ObZB4QEIB79+4hJCQEzZo1k1ncHz9+\nRFhYWIltUGEsy2LFihW887t06YIhQ4Zw53z69IlbGAwAjRo1gpmZmUzipZ2+CSE/KoFAgLlz5wL4\n/lDD3d29xAnHmpqaCAgIQL9+/bhJ0HFxcVi+fDk8PDxEqpNlWTx69Ig7VldXR8eOHaX/MipIFXen\nFSa9EPr48SMeP35canIkQghRBAMDA6xatUrs6/r37887VlNTw7p161C9enWZxBUaGlpq4oWUlBQ8\nffq0zIkBwjaBZdlid0oXtsPJyckAgMePH+PMmTMy2+2cZVk8ePAADx48KPIZwzDQ0NCQug5CCKno\nyrrXq+oksatXryo9ITfDMLh27Rp69Oih1DgIIUTV1K1bF4sWLRL7utTUVC7xApCfSE2ccgIDAzF3\n7lzk5OQAyO8vVKpUCX5+fpgxY4bY8RTUpUsXxMfHS1UGwE+WpK2tLXV5JXny5AktxiWEEDkKDQ3l\nXjMMg+HDh8t9HMrf359r4xiGkbptI4QQQkS1ZcsWXL58mTf/zdnZGW3atFF2aIQQQkRgYmKCsLAw\nhdfr4+NT4iZJ0kpKSsLNmze5tqlFixbo0KEDXr16hUGDBpV4XUJCAt68eQMgv1/VtGlTmc4xB4AG\nDRrItDxCiPJR4gUFSk1NxaxZsxASEsJbZATkL4wJDw9Hnz59RC5PW1sbERER+P3333Hs2DGuQ8Mw\nDM6cOYP27dtj69atEi9CjYqKwpEjR7hYAwMDsWbNGpXKjv348WOu0Vy9ejXWrFmDbdu2cZPsCSGE\nVBzbtm1DRkYGb7JfwQRDGRkZIk0Ml0bBSeUfPnwo8TzhzhqEEEK+W7t2Ldzd3SEQCHiLbWrVqoXQ\n0NAiC4zKYmJigvPnz8PExARJSUlcf+iff/7Br7/+is2bN2PatGkyiT0gIAA5OTm8NmbkyJElnh8U\nFIT79+/zzvf398evv/7KHd+5cwdBQUHcccuWLeHl5SWTeAkh5Efl6+uL27dvc/ff1q1bw9ramvv8\nypUr6Nu3L29HuV69esHe3h6bN2/m2qeNGzdixIgRaNCgAfz8/Eqt89u3b7x+SpUqVbBs2TKRYx42\nbBiGDx8uztcEy7LYuXMndu7cKdZ1slBwUoWyvX//Hm/evCkSS1RUFCVeIISUS5cuXcLff//Nu68J\nBAJcvHgRJiYmSoxMdhSRxEcVEwURQoi8zJ8/H6mpqUXeP3XqFP73v/8ByP8b3tzcvNjNC0pK3smy\nLDQ0NLB69WqZxBkQEIDHjx/LpCyhgu2lIu79hed3EEIIUT6BQAAHBwfeuB4A6OrqIjg4WOwxt+IU\nTJggKWHC14JlyppwvE5WSfsIIYQUxbIsgoODec9JFDFm5+/vz9Wpq6uL8ePHy71OQggh5NGjR3B2\ndub1X9q3bw8nJyclRkUIIUQcwnu4ouZ4KWI+WWRkJDcHnWEYjB07FgDQuHFjXLx4scTrbG1teXMA\nLS0t4e7uLtdYCSHlHyVeQOkPx2X14Pz69euYMmUKnj17VuShfKtWrXDmzBn8/PPPYperqamJkJAQ\nrFq1CqtWrYJAIAAAbieh33//HYcPH4aPjw9atWolVtmWlpZwcXFBXl4eAODLly8IDg6GlZWV2HHK\ny/79+4u8N3jwYJGvp4kRhBBVoIh2qLxLTU3F1q1bReqMKepnJqvd+QghRNnk3Q69ePEC06dPx6VL\nl4r0hVq2bImzZ8+K3VcR6tOnD65du4ZRo0YhISGBG7hLS0vDjBkzcPLkSezYsQM//fSTxPHn5eVh\n9+7dRdqggot2C8rOzoa7uztvwsW4ceN4SReUjf72IISoElndk96/fw83Nzfe/Xf9+vVQU1NDVFQU\nVq5ciZiYGGzevBn29va8a9esWYPQ0FAkJiYCyJ+0ffToUZiYmGDLli1l1l3wQVVqaqpI1wjP19HR\nkWgSuCokPijsyZMnEo1vSurGjRvFvh8VFYV58+YpLI7CqJ0lpHxRpf9nS1rcKkw4pwj169cvdSeI\nZ8+e4eXLl7z2duDAgSWeX7AvJou2S9TFSqo0hkgIIaWRth0qaaLz8+fPucQLADB9+nQMGTJErNjU\n1dXh6Ogo1jUluXTpkswTLwDfJ/GpYv9IFKr0dwghhJQ3KSkpMDMzQ2RkJK9/8vPPPyMsLEwuO7BK\nOnlcke2Ujo6OwuoihBBplbe/hy9cuIB3795x9/WqVavC2NhYrnVGRkZyc86FiR5UaeM8Sani75cQ\n8uMpb+2QIqWkpMDExASZmZkA8n8e6urq8Pf3R6VKFWP5Gf3+CSHKpqj7kCTjUpImEFXEGFhERASA\n7+N0os6jKPwzLWn+tzxR20NI+VMx/vKVQsEbe+GbfGmfiSo9PR3Ozs7Yvn077wGM8HX//v1x4sQJ\n1KpVS6LyhVxdXdGpUydMnToVaWlpvEkGEREROH/+POzs7ODq6ipydut69eph1KhROHXqFPeQavv2\n7SqVeOHQoUO8B2h9+/ZF27ZtRbpW1N89IYTIk7zboYrCx8cHX758KfPnIO+fkywnd1M7RAhRBfJu\nh3bt2gUHBwekpaUV6QsNHDgQf/zxB/T09CQqW6hly5a4du0aTE1NER0dzd2rGYbB6dOncenSJSxf\nvhyLFi2ChoaG2OUHBQXhxYsXIv8MfH19uYVIwjhUKTMq/e1BCFElsrwn2djYICUlhTvX0NAQxsbG\nSExMxKhRo7jEou7u7pg0aRLq1q3LXVulShXs2LEDI0aMAMMwsLGxga+vLy5cuMCrv6SHHOI+/Ci4\nA19FIpxgOGvWLFhYWPB+xvLwzz//8I6FP9fz588jKytLKZMPqZ0lpHxRpf9nL1++jOjo6CL9JpZl\nERUVJff6hYYNG4Zhw4aV+vnLly9575W2c4SQm5sb3NzcpIrN2toaQUFBAPJ/J76+vrCxsZGoLBqX\nI4SoAlVqh8orhmEwdepUBAQEyL0uIyMjnDlzRmbl0e+fEKIKyutk33v37sHU1BQJCQm8OWP6+voI\nCQlBjRo1ZF6nsI7AwECZly2N3bt3IyYmBgCgoaEh8niYKv9+CSE/hvL493DBTeKEmy9Uq1ZNrnVu\n2rQJwPd2SB5tnKLRuBwhRBWUx3ZIUQQCASZOnIhHjx7x+lsLFy5E9+7dlR2eTNDvnxCibIq6D02b\nNg39+/cX65rw8HBcvnyZq3/hwoVo2LChWGUMGDBArPNFJRAIEBERwbVPdevWRZ8+fUS+tiA1NTV5\nhFgiansIKZ9+6MQLTZs25SZdFzZw4MASPxPV2bNnYWNjw1t0A3wfBLO1tcWmTZtKzfz25s0bvH79\nmjvW1dUtMbGAiYkJ4uLiYGZmhn///ZeXfCE3Nxfe3t7Yt28fFi9eDDs7O2hra5f5HaZPn45Tp05x\nx3Fxcfjnn39UouP0119/ITExkfeznTNnjkjXBgYGlvgw7NKlSzKJjxBCyiLvdqiiSElJwebNm8vs\nSMj7Z9axY0fEx8dz7euxY8cwduxYicqidogQogpKu29Kuyjm0aNHmDdvHi5evMjbdY5lWaipqcHZ\n2RkrV66U2SBRzZo1cf78ebi7u2PdunXcIBnDMPj27RucnJwQEBAADw8PjBs3TuRy8/LysG7dOpHj\n/PLlCzw8PHgPnszNzdG+fXuJvpesyfN3Tggh4pJlf2jPnj04efIkd//V1NTEli1buHpmz56N7du3\ng2EYpKSkwNHREfv27eOVMWzYMFhYWKBdu3ZwcXEpUoekO9rJA8Mw6Nixo9g71korMTERoaGhAIpO\nzs7NzcXTp0+Rm5uLRYsWwdHREVFRUaXugi4t4YO+wjIyMhAVFYXRo0fLre7ilNaWltYHJIQohyqN\ny7EsC3t7+yJJF4SePHmC58+fo3nz5jKrU5I2LTU1FZcvX1aZ9lBSNC5HCFEFqtQOEcWbOnUqpk6d\nWuxnNEZHCFGU8jrZNzAwELa2tsjMzOQ9i7Gzs4O3t7fcJ01bWlqKfc38+fMRHBzMHZ87dw5dunSR\nSTxHjx7lXuvo6Ih0DS14JYQoW3nsD2VkZPCeQzEMI1GbII579+7h/PnzFSqZN43LEUJUQXlshxRp\n4cKFOHfuHK/96devHzw8PJQcmWzQuBwhRNkUOYfXyMhI7GvevXvHm49laWmJTp06ySwmacTExODz\n58/cnPQxY8aIfG3hPpW6urqswysRzdsmpPz6oRMvyMv9+/fh4OCAyMjIIouMgPwHHXv37sWECRPK\nLGvnzp1Yu3Ytd6yvr49z586VeL5wt1cHBwf4+vrydntlGAZfvnyBi4sLNm3ahEWLFsHGxqbULKgj\nR45EgwYN8P79e+69Xbt2qUTihb179/KOa9asCVNTUyVFQwghRF7Wrl2L5ORklXvIX1EeahFCiCx9\n+/YNq1atgo+PD7Kzs4v0herUqYMDBw7A0NBQ5nUzDIOVK1di4MCBmDJlCj58+MBLRpeQkABTU1N0\n7twZq1atEmkx5oEDB/Ds2TPeBIrS2NracgN7QP7gHA2KEUKIfD169AgLFy7k3asXLFiA1q1bc+e4\nuroiKCgI6enpYFkWBw4cgI2NDXr06MErKygoqMQHKwzDYMOGDVi0aJFU8d66dQvdu3eXun/Tp08f\neHt7S1WGuCIjIxEaGlps7A8fPkROTg73WV5eXonJY2UhPT0dsbGxRZLdCoWGhio88QIhhACSJerZ\ntWsX7t69y2vLhgwZggsXLnDnBAcHw9nZWer4Zs+ejc+fP3PH4ixKioyM5PXzCCGEVHxv3ryRSTlZ\nWVkyKYcQQohslMeFRpmZmbCxscG+fft4z540NTWxfft2TJ8+XckRliw1NRVJSUkA8scYc3JyZFb2\np0+fuNeiJF6gBa+EECKZgwcPIi0tjWuDfvrpJwwdOlSudXp5eUl1Pcuy2LhxIzZu3CijiL6rSMkg\nCCGEfLdv3z74+vryngPVr18fISEhCl2gSgghhBTnzJkzAL7PyTAxMRH52uzsbN6xpqamTGMjhFRM\nlHhBht6/fw83NzcEBAQgLy+vyMRfhmHQrVs3HDp0CK1atRKrbHEmsmloaMDHxwfGxsaYOXMmXrx4\nUWwChuXLl2PdunWYPHkybG1t0bFjxyJlqampYerUqfD09OQGy0JCQuDj4wNtbW2xvoMsff36FceO\nHeNNRLSysqLGjxBCKpiXL19i69atNKGbEEJUHMuyOHjwIFxcXPD69Wuu31GwHwIAPXv2RGhoKLdb\ntrz07t2bWxhaOIbbt2/D2NgYnTp1gr29PSwsLKClpVWkDIFAgHXr1nHXldUWhYWFITg4uEgfRdy+\nHyGEENHl5ubCwsICGRkZ3HsNGzbEihUreOfVqVMH9vb2WLt2LXc/d3Jy4i1oBRSbzbo86tq1K06e\nPMkdN2jQgHt98+ZN3rktW7ZEvXr15BbL5cuXuUQPhdt6lmURGhqKnTt3QkNDQ24xEEJIQerq6qhc\nuTJ3LOr95+vXr3B1deX1I8aMGYP169ejTZs23PuHDh2SSeKFZcuWSXxtWFiY1PUTQggpHxiGQWZm\nJho3bizTMmlhDiGEVCz+/v6YOXOmTMoq+AzG3d0d7u7uZZ4nTGIwc+ZMieNwd3eHq6urRNeqgo8f\nP3Kvq1evrsRICCGkYvPz8+ON302ZMkWu9SUkJODIkSNSz5ej+XaEEELE0b9/f7Rr1w4PHz4Ey7Ko\nVKkS/vjjD9SvXx9A/q7cL1++lLqeW7du8Y5jY2NhbW0tVZm1atXCpk2bpCqDEEKIajt16hTXL9PR\n0RErGV7hBOEF53YQQkhJKPGCDLx+/Rqenp4ICAhAZmYmt8gI+L7TWqVKleDk5ARXV1eFTeIeMmQI\n7t27BycnJ2zfvh0sy/J2W2IYBhkZGdizZw/8/f1x+/ZtdOjQoUg506ZNg6enJ3f87ds3HDx4EHPm\nzFHI9yjOgQMHkJGRwfsus2bNUlo8hBCiaszMzJCSkiJ1OYUHuIKCgvDXX3+JXU6tWrVw6NAhsa9b\nunQpsrKyil1MQwghRDWcPn0azs7OuH//fpG+EMMwqFy5Mtq1a4e4uDicPn1aYXExDAMvLy9s3rwZ\n7969K9KO3L17F9OnT8fSpUsRHBxcZBDu4MGDePLkSZHvU5zk5GTMnTuX97menh6vH0UIIUT2Ll26\nhH///Zc32c3Pzw/VqlUrcq6DgwO2bduGlJQUsCyL6OhonDt3DoaGhjKJpX///njy5AkAoEmTJoiN\njZVJuUKF2yBl7DxYp04dGBsbF/tZTEwM95phGAwcOFCusURERPCO1dTUMHDgQG5nvq9fv+LMmTNi\nZTcnhBBpTJkyRaIJ1/Pnz8d///3H3ee1tLTg7e2NZs2aoXPnzrhz5w4A4OHDh4iLi0PXrl1lGreo\ncnNzERERQeNyhBBSgVy9ehWNGjVCs2bNiv1clokS5NV+sCyLffv2Yd++fXIpvzBKHkEIIcWT9j4v\nnM8mSnnF3Yclqb+0Zz7Klp2djbS0tGL/DRgwADVq1ODO/fTpE/c9KPECIeRHs2LFCqxdu7bM81iW\nhZWVFaysrIr9/MWLF2jSpEmJ18fExODOnTu8diM9PR1btmwBAPTq1Qs9e/YUL/gyrFy5ssjmf+IS\n9VpR22BCCCEVX8uWLREbGwsjIyNcuXIF69evR79+/bjPw8LCuOdWsiBsg54/f47nz59LVVajRo0o\n8QIhhFRgCQkJePjwITdHfdSoUWJtxpOdnc07Lm6zPkIIKYwSL0hpwYIF2LFjB7fDWuGECwzDoF27\ndggICECPHj0UHl/VqlWxZcsWWFpaws7ODrGxsbzYhA+S5s2bV2zSBQD4+eefMWDAAFy5coW7Zs+e\nPUpNvLB3715e/Pr6+mjdurXS4iGEEFVz8eJFfP78WeoJYAXbNiC/05KQkCB2OcKMp+K4ePEiQkJC\neEl21NXVlbLAiBBCSPFsbW253R2K6wvp6+vDz88Px44dQ1xcnMLjmzBhAmbPng0XFxf4+flBIBAU\n6Q/VqlULffr04V2Xk5ODNWvWiJR0AQDs7e3x7t07Xh/F29sbtWrVkt+XI4QQAgMDA1haWmL//v1g\nGAYTJ06EkZFRsedWr14dtra2WLt2LXe/dnZ2llnihf/++4/bYa5KlSoyKbOgwskkpJ14IEsCgQCR\nkZG8dlCcrOKSCA0N5dXXvXt3zJ8/H5cuXeLa7AMHDlDiBUKISjt58iQOHDjAu58tWrSIWwBrbm6O\n27dv8+5rkiZeeP78OW7dugVTU1OJrj9+/DiSkpJkMvk6Li4Oq1evxv79+6GjoyN1eYQQQsQTFxcH\nFxcXREZG4uTJkyUmXlDlBamEEEIqluLam8LJtGW9SYMsysnJyeESIqSnp5eYLOHevXu865YvX47K\nlSuXeH5ubm6JMV++fLnIwqesrCwkJycXm4yWEEJ+BCXd08tKKCBqn2fbtm1F3vP19eVeu7u78xIv\nJCQkoFWrVmWWW5IHDx7gyJEjvDFDSenr62P8+PElfi4QCGBjY8PV8csvv8DOzq7UMnfs2CHTRbeE\nEEJUi7a2NiIiIuDr64uFCxcW+bxgu0QJSgkhhCjKyZMnAXzvx40ZM0as6zMyMgB8b8e8vLykTup9\n5swZSoRKSAVHiRek1L59ey7pgpCwE6GlpQUnJyc4OTmJlUlHHrp164aYmBgEBQXB2dkZ79+/5+Js\n3LgxPDw8Sr1++vTpuHLlCnd8+/Zt3Lp1C926dZNr3MW5ceMG7t69y/uZlzXYRwghP6KydnsQZdBL\n0oExaQfXcnJyuAc7wg5S69at0ahRI1y4cEGimAghhMiera0tAgMDkZmZyZv0VrduXWzatAkWFhbc\nuYqeqC2sT1tbG1u2bIG1tTUWL16M6OhoAPntk7q6Ovbv319kgayXlxeePHnClaGurg4tLS1u8K2g\n7OxsbocL4c/AwMAAkydPFivep0+fwsHBQdyvybN8+XLo6upKVQYhhJQ3O3bswN9//42vX7/yJroV\nZ8GCBdi0aROysrKgqamJHj16ICMjQ6aJEuQ1uaBBgwa8Ov7++288ffoULVu2lEt94vD19cWrV6+4\ndlNNTQ0GBgZyq+/vv//G27dveX9bmJiYYPjw4dDW1kZ6ejpYlsXp06fx4cMH1KtXT26xEEKIpP77\n7z/MmTOHdy9r0KABnJ2duWMzMzMsW7YMQP69PzAwECtXrhT74X1QUBDmz5+PrKwsNG3aFN27dxc7\n3h07dnCvS5rwHRUVVer9PyUlBc7Ozti5cydYlsXChQuxd+9esWMhhBAimUePHmH58uU4ceIEdy9/\n+/ZtkfOEY1xaWlqIiYmRSR/Hzs4O169fl7qcwigxBCGEKFfXrl3h7u4u0zKzs7Ph7++PDx8+FEmO\n3axZM1haWsr0/j9o0CCxrwkNDYW5uTlycnLEvpZlWZw/f17s64TS0tK410+ePMHgwYMlLosQQkjZ\nXr16hePHj/Pmr3Xr1g03b94s0h7dv38fNjY2iIuLw61bt9CmTRuJ6ly8eDEEAoFM2rtff/0Vs2bN\nKvHzvLw82NjYcMeNGzcu9XwAiIiIwN27d6WOjRBCyrPXr19j06ZNyg5DZtzc3FCjRg3uuHLlyiXO\nYRO2h9ImByorQZK4aJyQEEIqtmvXrvGOY2NjMXHiRJGvT0lJ4V6zLCvxZrRChTe3JYRUTJR4QUoz\nZ85EZGQkTpw4wb3HMAwGDhyInTt3onXr1kqMrqipU6di4sSJ2LJlCzZs2IDk5GT4+fmhatWqpV5n\namoKOzs7pKamcu8FBAQoJfFC4QyyzZo1w6hRoxQeByGEqLqyMnrL84/9woNq6urqYl2/Zs0aPH78\nmFeGt7c3tm7dKnYcO3bswMGDB3H58mWlJ0IihJCKpm3btvD09IS9vT0YhoGmpibmz5+P5cuX83Yu\nXbp0KZYuXarESIHOnTvjwoULOH/+PFxcXHDz5k3Mnz8fPXr04J2XmJiIdevW8R4STZ8+HZGRkUhM\nTCxSrqamJq5evYpx48bh/PnzqFq1Knbt2iVWbCzL4tWrV1I9lGMYBnZ2dpR4gRDyw6lSpQr8/f3x\n+vVr6OnplXpu7dq1YWVlhaysLLi6uqJp06YKilJ6v/32GzQ0NJCbmwuGYZCZmYlhw4bBw8MDffv2\nRcOGDRUWS15eHj59+oT4+HgEBQXxdmsHgMGDB6N27dpyqz8kJKTIe8bGxtDS0sLo0aNx5MgRAEBu\nbi727t0LFxcXucVCCCGSsrKywsePH3n9jh07dvCe1TRt2hQDBgzgkmKnpqZix44dIvetvnz5gtmz\nZ+PYsWPcGNuECRPw77//ombNmiLH+uDBA1y5coWXmE4gEHCff/78GdbW1ggPD4e3tzcWLFhQbDm3\nbt2Cn58f950DAwMxceJEGBoaihwLIYQQybi6uiI2NhZ5eXm85y5v3rzhndenTx9uTE9LSwtdunSR\nSf2GhoZcH0FWz6aE7efIkSOxYsUKmZRZmsWLF+Pvv/+Wez2EEFKedOnSRWZtBQD89ddfmDt3Lpd0\noeC8hmnTpsHHxwfVqlWTWX2S0tTU5DZpKitBUcHkEYXfE0elSpWgo6ODvLw8APkLvdq0aQMDAwPM\nmDEDRkZG0NLSErtcQggpzzp16gRzc/NiP7t27Rr3bJ9hGPTo0QPNmzcv9tzS2hYvLy/uni8sp1+/\nfrh58yZvblxQUBBmzpzJ3acnTpyIGzduiH1v/vPPP3Hu3Dmu3MaNG+PVq1ciX6+np4devXpxx/J4\nDjZ79mxe8tXy9KyNEEJk5cOHD9iyZYuyw5AJhmGwcOFCXuKFss4XtoHnz5+XKBmck5MT1q9fz7V3\nZmZmOHz4sNjlEEIIKduxY8eKnfdckps3b/KOg4KCxJqTZmBggE6dOol8vqhcXV1x9uxZ5OTkgGVZ\nbN26FRMmTOD1f0pTcC2sKGN6xSk8zqepqSl2GYSQ8oUSL8jAnj17cP36dbx79w5NmjSBl5cXJkyY\noOywSlSlShUsW7YMc+fOxalTpzBy5EiRrjEzM8OePXu4RubIkSPYvHmzQhuLT58+4ejRo7xOW8GM\nq4QQQvJ9+vSpyHuTJ09GcHAw90f/yJEjER4eLvO6w8PDMWbMGADfJ75ZWlqKfP3Nmzfh4eHBu9cb\nGRlhxIgRYiVeuH//PmbOnInY2FgwDAM3NzesW7dO7O9DCCGkdLa2tjh8+DDq16+PTZs2oUWLFsoO\nqVT6+vrQ19dHeHg4hg4dWuRzOzs7ZGRkcO2ljo4OVq9ejcjIyBIH3LS1tXH69GlYWFigV69eNLmA\nEEIUbMCAASKf6+fnJ8dI5Kdq1aowNzfnkhwAwPPnz2FmZqbkyPiTxRmGgZOTk9zqysrKwqFDh3h1\ntmzZEu3atQOQP5nxyJEjXJu9a9cuLFu2TOxkgIQQIk/CSQEFx75mzpwJIyOjIucuWrSIS3ognECw\naNGiMpOLhoWFYc6cOXj//j1vAsCrV69ga2up9hqOAAAgAElEQVSLQ4cOiRzv9u3budc9e/bE//73\nP3z9+pV7r1OnTtyO6Q4ODmjTpg1GjBhRpJzBgwdjwoQJvGc8s2bNwv3796GtrS1yPIQQQkQnvN/G\nxMQU2TVcU1MTP/30E+/8tWvXyiUONzc3mZcp/D516tQpklhVHmrUqEG7FxFCiJwkJSVhyZIlCAoK\nAsBfyFO/fn3s3r1bpTbkKbhAt7i2oVKlSqhWrRq0tbWho6ODjx8/IikpiTvf2NgYzZs3h7a2NndO\ncf8t+Lrw3Lz4+HiwLIuoqCicO3cOnTt3RlxcnHy/OCGEqJgJEyaUOE970qRJvMVFc+fOFWvuGpA/\n9y4gIIDXLi1durTYhGwGBgbQ0dFBcnIyWJbF/fv3YWdnh927d4tcX05ODhYuXMirz8PDA1OmTBG5\nLzJ06NBi50DI0vDhw+VaPiGElCflfaxIkkWnsryeEEKI/O3atQsXLlyQ6FqWZbF582aRz2cYBr6+\nvnJJvNC5c2esXr0aS5cuBcMwEAgEmDZtGu7duyfSnLCUlJQi89vEVXhjWkqCSkjFR4kXZKBmzZrY\nv38/rl69iqVLl5abm6eurq5Yg4nm5ubYs2cPd5ycnIwTJ06UmDVWHnbu3ImsrCyusapSpQqmTZum\nsPoJIaQ8mzdvHoKDgwHk/+F/9uxZ3Lt3Dx07dpRpPWvWrOEd6+rqwtHRUaRrc3JyMHXqVC4DOJA/\ncWHbtm1ix+Hh4cElXWBZFhs3bsT48ePRrVs3scsihBBSMoZhcOnSJVSuXFnZoYiluEVN4eHhOH36\nNG8yg7OzM+rUqVNmeZqamjh69KjED5WkfRhX3h/mEUIIKdv69etx/vx5vH//nrfjnzKxLMuLZcWK\nFRLtaiGqkJAQfPnyhddWT5w4kft8xIgRqFGjBrcg+M2bNwgODsaUKVPkFhMhhIgjLCwMa9eu5d2/\nW7duXeKEBWNjY7Ru3RoJCQkAgPfv3yMgIACzZ88u9vzk5GTY29tziXoK7xJrZmYm1i5MSUlJOHjw\nIHc8ZcoUuLi48ModNWoUl7Q7Ly8PkyZNQkxMDH755Zci5W3atAlnz57Ft2/fAACvXr3C4sWLsWvX\nLpFjIoQQUrobN27g3r17Rd4vmHDB2toazs7OaNSokRIilE7Xrl15bVPLli0VUq+NjQ0vsVBJu+US\nQggRz969e7Fs2TJ8/vy5SJKgcePGYdeuXahVq5aSo+Tr2LEjjh8/XmLihMLzBqdNm4Z9+/Zxxw4O\nDujTp49UMRRc9MswjMi7+xFCCBHdpk2beBs2tGvXDiYmJsUmXmjYsCH8/PwwadIkbtzM398fQ4cO\nFTmB9rp16/Do0SNuTG/ixIno27evTL8TIYQQ2aLEA4QQQojiLFmyBIcOHcLdu3cBAI8ePcKePXsw\nZ86cMq8VbmorHHd89+4d6tatK3LdAoEAlSp9X4JNGwAR8mOgxAsyMmTIEAwZMqTYz+7du4fDhw9z\nxzo6OnB2dlZUaDIzcOBA1KtXDx8/fuTeCwgIUFjihby8POzatYs3sXrq1KmoWbOmQuonhJDyrk+f\nPujSpQv+/fdf7r1169ZxyRhkISwsDP/88w/vXu3o6AhdXV2RrtfQ0EBWVhaA7x2b1atXSzT5b8uW\nLTh//jz+++8/buL3rFmz8M8//0BNTU3s8gghhJSsvCVdKE5mZibs7e15C6CaNm2KBQsWiFyGcBKE\nuBiGwcCBA3Hx4kWxryWEEALMnDkT/v7+Milr0KBBKns/rl+/Pq5duwZra2tER0crOxwA3xM/NG3a\nFJ6enrwkCPJQeHcohmEwY8YM7lhLSwtWVlbw8fHhJeGjxAuEEFXw4MEDXjJslmWhoaGBgwcPokqV\nKiVet2jRIsyZM4e7r7m5uWHSpEmoXr0677zg4GAsXLgQHz9+LLJgqVGjRti5cydvwagoVq1ahbS0\nNAD543bm5uZwcXHhnbNt2zbcuXMHN27cAMMwSElJwejRo3Hjxg3o6enxzm3UqBGWL18OJycn7vvs\n3bsXEydOlPtufIQQUtGFhYVh48aNuHr1Ku99YZugoaGBadOmcQkXkpKScObMGWWEWkTLli3Rtm1b\nkc5t0KABLCws5BxRUeK2oYQQQkp3/vx5ODg44M6dO7xnK8I+TLdu3dCrVy8EBgbKtN5KlSrB3t5e\nqjJq166NsWPHinx+tWrVeMdPnz6VKvGCQCBAUFAQb06GoaGhxOURQggp6tOnT/Dz8+Pdax0cHEq9\nxszMDGFhYQgODuaumz17Nn777bcyE8Y9f/4cnp6e3HWVK1eGl5cXBAKBLL8WIYQQGVL2BgmyUlG+\nByGEkJKJe68vmFhIldoJhmHg4+ODIUOGcH2n1atXw8rKqtQ57FlZWUhLS+O+C8MwReYxlCUnJ4d3\nXF42bCeESIcSLyhAfHw81q9fzx3Xr1+/XCZeUFNTg6mpKbZv3841UhcvXsSrV6/QuHFjudd/9OhR\nvH37ltfYibMIihBCCLBs2TKYmZlx9/Fjx45hzZo1MtkRKDc3Fw4ODrwOVt26dcWeuGBhYYE1a9aA\nYRj069dP4okPenp62L59OyZOnMh939u3b8Pb2xtLliyRqExCCCEVl0Ag4HbHFk6e8PHxgaamppIj\nI4QQIipVedgjzziaNGmCCxcu4P79+4iOjsazZ8+QkpKi8Ml36urq0NbWRtOmTdGzZ0/07t1b7nVe\nvXoVMTExvImOBgYGaNasGe88Gxsb3m7u9+7dQ2hoqFgT4gkhRNZevHgBQ0NDpKamAvje51i8eDG6\ndetW6rVTp07FihUr8N9//wHIn/jt5uaGzZs3AwASEhJgZ2eHc+fOcQuWhJMh1NXVYWNjg3Xr1hVZ\n7CNKzMJk2ED+gtPidprV0NDA0aNH0bVrV26X2sTERIwdOxYXL16EhoYG7/xFixYhMDAQjx8/5mKd\nM2cOHjx4QP0vQggRU1ZWFoKCgrB582Y8evQIAL8/ImxvzMzMsH79et4z/du3b8PIyEjhMRdnyZIl\n8PLy4r2XlJSEOnXqKCkiyaWlpaFq1arKDoMQQlTW3bt34ejoyOu/AN8ndAuPb968iZs3b8q8/sqV\nK0s8/0DSMb8mTZrwrt+xYwcmT54s8WYRixcvxsuXL7nyKleuTInsCCFExtzc3HgLc3766SdMnjy5\nzOv8/Pzw119/4fXr12AYBqmpqbCwsEBMTEypO6Lq6ury2kJHR0c0btwYiYmJsvlChBBCZKpbt27I\ny8tTdhiEEEJImaKiosQ638HBAZs2bQKQ3ze5ffs2OnbsKI/QJDJo0CAMGzYMkZGRAID379/jwIED\nmDlzZonXfPjwgXdco0YNscflKPECIT8mSrygQAUnu5VX5ubm2L59O3fMsiwOHDigkEQSwgnTwgki\no0ePRqtWreReLyGEVCSmpqZo27YtHj58CCB/kambmxsOHjwoddnr169HQkICbxGMm5tbqTv2FWfs\n2LFYs2YNqlWrhn379kkVk6mpKUaOHImzZ89yca1cuRJmZmYKSRpECCGk/KhatSrmzJkDDw8PbkK6\nsbGxssMihBAiImmTHQj7MJL69u0b97rwAlN56NChAzp06CD3elTJ2rVri7w3Z86cIu/9/PPPMDAw\n4CbwsywLV1dXSrxACFGajx8/wtDQEO/fvy/y2c8//1zm9VpaWnBxccGCBQu4+5qfnx8mTZqEEydO\nwMfHB9nZ2bxnUAzD4Ndff8XOnTvRo0cPieJ2dnZGdnY2V97s2bNLPLdx48Y4dOgQRo4cCZZlwbIs\nYmJiMGPGDAQFBfHO1dDQgK+vL4YNG8a99+zZM3h5eWH58uUSxUoIIT+iu3fvwsDAAJ8+fSp14SoA\nzJgxo8RnItLMYSjch5KknLL6YaqSYK8s0vYpCSGkonvz5g2WL1+OAwcOQCAQlNj+qNq8uoLJTiVN\nFFcwKQLLsrh+/Tr69u2LhQsXolWrVqhevXqZZSQlJeHRo0fYvXs3/v77b96cDAsLC+jo6EgUGyGE\nkKISEhKwd+/eIvPfKlUqe7q9rq4u9u3bBwMDAwD59/2bN2/CxcUFnp6eJV5Xq1YtjBo1CqGhoWjb\ntq3I87GFC6IkVTip94sXL6Qus3PnzpQQiBBCCCGEkAqi8FjdixcvsGjRInh7exfZKEdR7OzsEBkZ\nyT2TCQwMLDXxQuGEdpIk/c7KyuIdV65cWewyCCHlDyVeKEdYlsX58+clzngNAMeOHcO4ceMkvr5v\n375o1KgR3rx5ww0sKiLxwpUrVxAbG8t78LZo0SK51kkIIRURwzBYs2YNTE1NuXtqcHAwfv/9d95k\nZ3HFx8dj9erVvPv0gAEDil0EU5YuXbqgUaNGWLduHZo3by5xTELbtm1D+/btkZmZCSB/QZS9vT1O\nnDghddmEEEJKdv/+fTg5OSk7DE54eHiZ59ja2mLDhg2oVasWfH19FRAVIYQQWRg6dKjYCd+Erl27\nhlu3bnHH4u4GLiTcwRyoWFmto6KiMH/+fO64RYsWOHPmjMLjuHXrFu+hGQA0aNCgxB16bWxscO7c\nOe44Pj4egYGBsLa2lnushBBS0JcvXzBs2DA8efJEqsWY8+bNw65du/C///0PDMMgNzcXvXv35iZ/\nF5wIXqNGDaxatQo2NjYS1xkXF4eQkBCu3LZt22L48OGlXmNoaAgnJyesXbuWu+7gwYNo06ZNkWdI\nBgYGMDIyQnh4OHeup6cnLC0tud1gCSGElE4gEHBJF4DvC//V1NRQvXp1JCcnl1mG8FpJ24uCE+4K\nJn+QNUpqQAgh5derV6/g6emJgIAAZGVlFem/CCnqXi9uHQUnVUuaeKFr164wMDDgdhhkGAaxsbEw\nNzeXqLyCczIaNmxYbLJSQgghknNyckJubi7XZvzyyy+YNm2ayNcPGTIE8+fPx5YtW7h79saNG2Fg\nYFBqQgIrKyucOnUKu3fvFrnNcXBwEDmu0gjblYcPH0pd5pw5cyjxAiGEEEIIIRXQ0aNHMWvWLHz9\n+hX37t3D1atXUa9ePYXHMWLECDRu3BivX78Gy7KIjY1FYmIimjZtWuz5BRMvMAyD+vXri12ncMMK\noYo0N5AQUjJKvFDOSDPpQVYPqCZOnAhvb2+uvMePH+PmzZv47bffZFJ+cby8vHjHXbp0wYABA+RW\nHyGEVGTjxo1D3759eTshzJkzB/fv35dokVFWVhYsLCyQk5PDvaejo4N9+/ZJHOOePXukSgRRULNm\nzeDs7IwVK1Zw3/fUqVP4888/y5wwTgghRHJJSUk4c+aMVLvmyYqofaEGDRrAxMQEEyZMQO3ateUc\nFSGEEFkxNzeXeKJyp06deG2VOJPnCkpLS+NeV6Ss1ikpKXj06FGxE+IVacmSJdxrYRz29vYlJqgd\nPXo0WrVqxS10ZlkWzs7OMDU1pR0ACSEK8/79exgYGODBgwdFFsWKS11dHT4+PkXGywqWq6amBisr\nK3h6ekJPT0/iuHNzczF9+nRevAsXLhTp2pUrV+Kvv/7ClStXuPuvq6srevbsWWTC9fr163H27Flu\nZ72MjAwsWLCAkqUSQoiIGjduzL0W3q9Hjx6NNWvWYN26dQgJCSn1+v79++P58+cS13/t2jVMmjSJ\na4vGjx+PjRs3SlSWrq5umef89NNP2LZtm0Tly9PMmTPx33//KTsMQghROc+ePYOHhwf279+PnJwc\nXsIFAFy75eTkBENDQ6Snp4NlWTRq1AjPnz+Hurq6kr9BvoKTqiVNvAAABw8ehIGBAe7duyeLsMAw\nDNq0aYPQ0FDUrVtXJmUSQggBLl68iBMnTvCeiXh5eYk9nufp6YmoqCjEx8eDYRgIBAJYWlri7t27\nJc5DGDFiBJydndGvXz+x6hJ3Pkbh71I4oV5Jn0lSNiGEEEIIIaRiyMrKwurVq5GSkgKGYfD06VPo\n6+vjypUrqFmzpkJjYRgGBgYGCAgI4N6LiYkpMfFCQkIC77hdu3Zi11k48UJFmhtICCkZJV4gYjMz\nM4O3tzfvvf3798st8cL//vc/RERE8AYzVWnnXEIIKY+2bNmCnj17chObX758CScnJ2zdulXssubN\nm4e7d+/y7tObNm0qsfMiClklXRBasmQJ9uzZg1evXnFxzp8/Hw8ePICGhoZM6yKEEFJUwcVAql6n\nj48PGjZsKI+QCCGEqJhLly7h/v37XJvRsmVLjB07VuxyMjIyuN2PGIZBnTp1ZB3qDy00NBSXL1/m\nTdirW7cubG1tS7xGTU0N7u7umDx5Mnfdx48f4erqis2bN8s9ZkIISUxMhL6+Pp4+fSp10gUhAwMD\nGBsbIywsrEiZ3bt3x7Zt29C9e3epY1+1ahXu3LnD1aGnp4fff/9dpGvV1NRw+PBh/Prrr0hKSgLD\nMLC2tsbAgQOLnNu2bVvMmDEDu3bt4iVLjYyMlPnYICGEVES1a9dGlSpVkJmZiYEDB8LDwwO9evUS\n+XpNTU00adJE4voLJ23Q1taWqryyaGtrw9jYWG7lS8re3l7ZIRBCiEp5+PAh1q1bh+DgYOTl5RWb\ncKF9+/bw9vaGvr4+li1bxiU0ZRgGDg4OKpN0AQAyMzO519JMqK5Tpw5u3LiBXbt24fDhw7h9+3aR\nCdui0NXVxW+//QZzc3NYWlqiUiWa+kkIIbKSk5MDGxsb3vjd4MGDMWLECLHL0tLSwoEDB9CrVy/k\n5uYCyE/SOmPGDISGhhZ7TaVKlbBq1Sqx6hHGKu6YY2njlAU/k2Qsk5IvEEIIIYQQUvFoaWkhMjIS\nvXv3xsuXL8EwDOLj42FiYoILFy4ofIxq0KBBCAgI4Pof//zzDyZNmlTsuffv3wfwva/TsWNHsevL\nysriHVPiBUJ+DDT6Xo4wDAM9PT307NlT4jIaNGggdRzdu3dH8+bN8eLFCwD5jU9ISAg2b94sl4df\nXl5evMG81q1bY/z48TKvhxBCfiRdu3aFvb09vL29uYkOfn5+MDQ0xOjRo0Uux8fHh+u0CO/VI0aM\nwIwZM+QYvfi0tLTg6ekJCwsLrj15+vQpNm/eDEdHRyVHRwghFZ+wjVDkQ3ZJFzVR0gVCCPlxCHdL\nFbYZixYtkqicx48f847r1asndWyqQpFJk4qTmZkJR0fHIguMnZycUKVKlVKvNTc3h4eHB5dcg2VZ\nbNu2DWZmZmItSCOEEHHdu3cPI0eOxNu3b3n3Lz09PQwYMAAnTpyQuGwfHx9ER0cjNTWVe09XVxdh\nYWEyaX9u3boFT09P3lifi4sLtLS0RC6jYcOGCAwMxPjx47Fp06ZSE+WsXLkShw4dQnp6Oveem5sb\nJV4ghBARGRsbw9raGoaGhjIpb86cObhw4QJ3fObMGbRu3VomZRNCCKnYzp49i61btyIqKor3TKhg\nwoX69evD1dUVM2fOhJqaGj5//gw/Pz/uPD09PcycOVPJ34Tvy5cv3OtatWpJVZampibs7OxgZ2cH\nlmWRnJyMjIwMka5VU1NDtWrVoKOjI1UMhBBC+LKzs6GpqQkA8PT0xOPHj7l2SU1NDRs3bpS47C5d\nusDZ2RkrV67kygwLC0NgYCCsra1lEn9eXp5Y53/9+hWdOnXCq1evAOTPBb9x4wY3hvnTTz/h7du3\nEAgE0NPTQ3x8PPT09GQSKyGEEEIIIaR8a9CgAc6dO4c+ffrg8+fPYBgGV69exYwZM7Bv3z6FxtK+\nfXvesXB9a3EKbjALAJ06dRK7voKJFxiGocQLhPwgKPFCOfPrr78iLCxM2WHAzMyMm3zXqVMnLF68\nWC6LqFiW5Q1mMgyDpUuXyrweQgj5Ea1evRrh4eFISEgAwzAQCASwsLDA5cuX0aVLlzKvP3HiBJYs\nWcK7/7do0ULhHSdRmZubY+vWrbh+/TrXrqxduxZTp06tUAujCCFEVXXr1g03btxQSF3p6ek0+YwQ\nQkipnj9/jlOnTnF9g9q1a8PKykqish48eMA7lmaX16CgIJlNuJMl4cOnhw8fQk1NTaRrGIYRe9Jf\nYc7Ozrzd4oH8Bb1z5swRqf7Vq1dj7Nix3HsCgQDW1ta4ffu2WIuICSFEVCdPnoSlpSUvkQDLsqhT\npw7Onz+PEydOSJV4oVmzZti1axcmTZrE3RuTk5MxatQoXL58GdWqVZO47OzsbEydOpV3727evDls\nbGzELmv06NF4+PAhmjdvXup5devWhaOjI1xdXaGuro4ZM2ZgzZo1YtdHCCE/quDgYJmW988//+DZ\ns2fcc/nyssCGZVl8+/ZNbuVL074SQkhFlpaWhoCAAGzfvh0JCQkAUGzCBV1dXTg6OsLe3p6XSHPD\nhg1IS0vjzluwYIFKTVpOTk5GXl4e951k2S7GxcVhz549uHnzJm7cuCHyeBshhBDp/fvvv4iMjMSf\nf/6J3NxcXL16FU+ePIGHhwdvnrKlpSU6d+4sVV1OTk44cuQIHj16xJW9cOFCDBkyBE2bNpXRNxLd\nzJkzuaQLampq8PX15W0E2KFDB/Tv3x/BwcFISkrCvHnzEBISovA4CSGEEEIIIaqpdevWOHz4MEaO\nHAmBQAAAOH36NJ4+fYqWLVsqLI6Cc/NYlsWbN2+KPe/Dhw9F5p117NhR7PoKJ0+tWrWq2GUQQsof\nSrxAJGJubo47d+5gyZIlGDx4sNzqYRgGf//9N6Kjo7FhwwbEx8fj999/l1t9hBDyI6lSpQqOHDmC\n3r17IycnBwzDIC0tDUZGRrh+/ToaNWpU4rWnT5+GhYUFN2FCuFApIiJCpSfieXp6YtCgQdxxWloa\nVq1ahe3btysvKEIIIYQQQojCbdq0iXsAxDAM7O3tJZ7YLUy8IJyM93/s3Xd0FdXax/HfpBECCRAi\nXYog0qQY6R0pUuMFpDcJCCjVYAAVaQrSRLmCUqSElxJABCQIShWUKnApooiCFEGa9PTk/YN1xgwJ\nmJycJITz/ax1FpkzM3s/E10z2Xv2fnaZMmVSHV9aJDi1l63dJyU/roTn2GvXrl2aPn26ZbV4wzA0\nevRocwWqfxMQEKDKlStr37595qDGEydOqH///pozZ06qYwSAhMaNG6fRo0eb27Z7YZ48ebRp0yaV\nK1cuVUkXbNq3b69Nmzbp888/N+9tBw8eVEBAgL7++mu5u7vbVW5cXJwuXbpkxm4YhiZMmCA3N/te\nJf5b0gWboKAgHTlyRG+//bZdq0sAABznzJkz5s9Zs2ZN9cre6eXw4cPJSihur59//lklS5ZMs/IB\nIDNatmyZ+vTpo1u3bpmJCSRZEi54e3vr9ddf15tvvqmcOXNazo+OjtaMGTMsSRrCw8M1depUh8VY\nt25dPf/883aff+XKFct27ty5Ldt3796Vq6tripN7tmrVSuvWrZN07/c0e/bsZCUZvd+FCxe0YMEC\nDRs2jMQNAPAvNm/erE2bNumbb74x+78kqV69eoqLi1P37t0VERFhPs/y5MnjkGeSh4eHZs2aZRmr\nduvWLXXr1k3bt29PdfkpMX/+fK1cudK8xl69eum5555LdNyECRO0atUqRUZGauXKlZo3b5569uyZ\nrrECANLfpUuX1L1794ce8/vvv1u2hw8fnqidlBwnTpywbG/btk1NmzZNcTkPs2zZMuXIkcOhZQIA\n7mncuLHGjx+vESNGqFq1agoNDX3ovKO0kD17dsu2Lbnr/bZt22bZLl++vHx8fFJcX2RkpGU7YXJZ\nAI8vEi/ALuXLl1dYWFi61VevXj3Vq1dPt2/flqura7rVCwCPu0qVKmnKlCkaOHCgOSDiwoULat68\nuTZt2qQnnngi0TlffvmlOnXqpOjoaEn3Bk94enpqzZo1KlGiRHpfQorUqVNHTZo00caNG2UYhrp0\n6aKxY8dmdFgAAOi3337Tm2++maoyihQpov79+zsoIgDIvKKiovTVV1+pTZs2Se6/du2aFixYYA7s\n9vb2TtX9c+vWrZZtezJjw+rGjRvq3LmzJdmfJNWsWVOBgYEpKmvmzJmqVq2a4uLizP/m8+bNU716\n9dS5c2eHxw7A+Vy7dk09e/bU2rVrE002KlGihDZs2KCnnnrKoXVOnz5du3bt0k8//WTe27Zu3apO\nnTopNDTUrkk3np6e6tu3r9577z0ZhqHatWvr5ZdfdmjcScmaNSsr5wHAI+DixYu6evWq+SxLuFpQ\nckRGRurq1aspOsfLy8uhg9MSJopLTTK4+5O/AQASa9iwoTw9Pc1BzQkTLuTIkUMDBw7U4MGDEyVc\nsImKitLt27ctSRvee+89h8VnGIbmzZuXqsQLp06dsmznzZvXsr1w4UIFBwerYcOGatmypVq1amUu\nUvH++++bx/n4+GjAgAHmduPGjbVu3TqzLTdu3Dh179492c/EQ4cOacqUKVqxYoViYmLk6+urPn36\n2HuZAPDYuX37tq5duybpn7/tFy1aZG7b7r+2fydNmqRdu3ZZvv/vf/+rXLlyOSSeOnXq6JVXXtH8\n+fPNOnbu3Klp06ZpyJAhDqnj35w8eVKDBg0y68+fP78mTpyY5LGFCxfWu+++q7fffluS1L9/f1Ws\nWDHJJA0AgMdHeHi4Oa76Qf1qtueq7d/9+/enqk5bPRcvXtTFixdTVVZChmEkmiALAHCs4OBg5c+f\nX506dcqQOZ4PSrRwP9vCGLa2XrNmzeyq786dO5ZtLy8vu8oBkLmQeAGZyv1ZiQAAqde/f38dPXpU\ns2fPNjvEjhw5omrVqmn9+vV65plnzGM/++wz9e/f3zL5xcXFRSEhIapRo0aGxJ9SEyZM0O+//65P\nP/1UDRo0yOhwAABQfHy8zp49m+pVM6pVq0biBQBO77ffflO7du108OBBzZs3Tz169Eh0zLRp03T3\n7l1J91669+vXz+7VDs6fP689e/aYAxD8/PxUqlQpu+MvWrSo2rZtm+zjIyMjU7yqXkqcP3/eMuDQ\n29tbjRs3Tta5qZmg1LVrV50+fdpSRpYsWTR37twUl+Xv76/Bgwdr6tSplkGVffr0UcmSJVW5cmW7\n4wSAzZs3q3v37vrzzz8TTdJ8/vnntRQ0v1cAACAASURBVG7duiQTm6ZW1qxZ9cUXX6hWrVqWSbJf\nfPGF/vOf/2jJkiXKli1bisvt37+/Jk+eLHd3d82fP9/RYQMAHmEJB0obhqEKFSok+9z4+HgtXbpU\nS5cuTVGdw4cP1/jx41N0TnJiSTiJFwCQNvz8/PTpp5+qbdu25n03f/78GjBggF577TV5e3snq5zU\nJMq53/2J8IoVK5aq8n777TezPMMwEiXU++abb3T37l2tWbNGa9asUUhIiLp06SJJGjlypHlcoUKF\nLIkXXn31VU2cOFF//vmnpHuTjD788ENzguu/WbNmjZYsWZIocYOnp2eqrhcAMquoqCjt2bNHmzdv\n1ubNm7V3715FR0cnei4kZBiGypYtq+LFi2v06NGWpAutWrVK0Xua5Jg8ebLWrVunK1eumHWNHDlS\nAQEBDk/Yer/bt2+rXbt2unPnjnmNs2bNko+Pj2JjY5M8Jzg4WCtWrNChQ4cUGRmpFi1aaOfOnWke\nKwAg4z2sjebI9hsAIG1duHDB7HtKib/++suyfezYMXOx1vuVKVNGhw4dSnJf6dKl0zQ5QcJ6DcMw\nk6EmdOvWLYWFhVmSCjVt2tSu+sLDwy3bJF4AnAOJFwAAgD755BOdOXNGGzZsMAdGnDp1SjVq1NDq\n1atVvXp1vfHGG/rkk08sL6bc3d01Z84ch79wSksVK1bU8ePH7Vr5DwAAAMCja+fOnWrRooVu3bol\nSerXr5+eeeYZVa9e3Tzmxo0bZrsmPj5enp6eGjx48EPLfdgqpytXrrRM7EmYuM6eMuvWrau6desm\nq4yIiAg1bdpUefLk0bx58+yaYPtvvvjiC8tq5wUKFNDy5csdXk9C7733nrnqn/TP7+rtt99WyZIl\n7Spz7Nix+vLLL/X777+b/63u3r2rgIAA7d69O8Wr+QKAJI0ZM0Zjx46VlHhl7LZt22r+/Plp+sL9\nmWeeUVhYmF544QXduXPHvL999dVXqlmzpr766is9+eSTKSozT5486ty5s55//vlUT1ICAGQuu3fv\nlmRNIJQWEk5oSqvye/furTfeeMPuMsaPH6+QkBAHRgUAj6fWrVurY8eO+umnnxQUFKQOHTrIzS35\nQxHTOklOats0J0+etGwXL17c/Dk2NlZbt241t93c3NS8eXPL8Q9aKdbDw0NvvfWWXn/9dfOYqVOn\nauDAgclKWDF48GBNmzbN7AO9cOGCZsyYoaCgoBRdHwBkZidPntTSpUu1bds27dq1SxEREea+hAmY\nbdu2f59//nm1bt1abdq0Uf78+VW5cmXLJCIfHx/NnDnT4fH6+vpq6tSp6tatmxlPeHi4evbsqW3b\ntjm8PpuoqCi1bNlShw4dMn8v3bt3V4sWLR56nqurqxYtWqQqVaooIiJCf/31lxo3bqwtW7bwPgUA\nHmPpmcg0YVvJ0fWSkBUApNmzZ2vMmDF2nZvwPtq5c2e7zt+xY0eaLupqW+TO9r6pRIkSiY5ZvHix\n7t69a15Pzpw57Y7pzp07lm0SLwDOgRmHAABAbm5uWrVqlerUqaP4+HizEXL9+nU1atRIVapUSZR0\nwcvLS6tXr1a3bt0yOPqUI+kCAOBRYxvokNoPADizChUqqFChQmZ7JjIyUq1bt9a5c+fMYz766CPd\nuHFD0r17b58+fZQ3b94Hlvmw+2tUVJQ+/vhjSztp586datOmjW7evGlXmckVFRWlgIAAbd++XStW\nrNDzzz+v48ePp6rMR0FISIjefffdRL+fChUqaPjw4XaXmzVrVs2aNctSrmEYunjxoho2bKgLFy7Y\nXTYA5+Xq6moODLP1p7m6umr8+PEKDQ1Nl5ftVapU0ZdffikPDw/LYPLDhw+rSpUq2rNnT4rLHD9+\nvPr27evoUAEAj7iEyc8kyd/fP0Xnp6TvKq37sHx9fVWyZEm7P7ly5UrT+ADgcbJw4UIdPHhQXbp0\nSVHShWzZsik2NtZhn/sHP7u7u6tQoUKpurYDBw5YthMmXti6davZ/2cYhmrUqJGi50evXr0sifJs\nyWKTcv36dct2jhw5NGDAALMPND4+XpMnT9bdu3eTXT8AZHY//PCDRo0apW3btikyMtLS3kjYR+bm\n5qa6devq448/1pkzZ7R7924FBwerePHiCgwMNN+r2O6pH374ofLnz58mMXfp0kUvvPCC5f69b98+\nHT16NE3qi4uLU7t27bR9+3azvjJlymjGjBnJOr9s2bKaPn26+fv8/fffVatWLf3yyy9pEi8AIGMV\nKVLEoW20h32Cg4Ml/dOf2L59e4eWHxMTozx58mTwbxQAHg32vI+xjX1IKqGoo+t6mNOnT2vixIkK\nCwvT4cOHdejQIW3evFmtW7c2F5u1ady4seXcuLg4ffjhh5Zk4F27drV7DtHt27ct29mzZ7erHACZ\nC7MOAQCAJMnT01Pr169Xo0aNLA2R6OhoM/O1dK8x5efnpy1btqhp06YZFS4AAI8NwzBUt27dVL84\n+v777zP6UgAgQ3l7e2vNmjXKlSuX+dLk0qVLCggIUEREhK5evWp5qeLp6alhw4Y9sLznn39eW7du\nNT/t27e37J8+fbpOnz5t+c4wDH355Zfy9/fXoUOHEpXp7++vW7dumZ93333XrmtduHChvv32W3Mw\nwi+//KKqVatq5cqVdpX3KNi4caN69+5taY/Gx8crV65cWrVqVYoG7yflhRde0LBhwxKtnnHy5Em9\n8MILJF8AkGJDhw61JPzJkyePvv7664c+W1IjLi5Oy5Yt0+LFiy3fN2zYUP/3f/8nNzc3y8Dyv/76\nS/Xq1dOHH36ouLi4ZNfDYDQAcD5nzpzR4cOHLd+FhIQk+3zDMNS0aVPt2LEjRZ9+/fo5+lIAAOks\ntf01jnLy5ElLn0+RIkVSPdj7wIEDZhkeHh4qWbKkuW/FihWS/pnc+9JLL6WobHd3d/Xv398y+faj\njz6yrNhu061bN9WuXVtLliwxkysMGTLEMsD78uXLD0zcAACPo4T35IQTggzDkIeHh1588UXNmTNH\nFy5c0JYtW9S/f38VKFDAPOfjjz9WaGioZRJOp06d9Morr6Rp3J999pk8PT0lSSVKlNCuXbtUrly5\nNKkrMDBQa9euNa8xW7ZsWr58ubJmzZqiMnr16mX+js6dO6caNWro22+/TZOYAQAAADhOwmTY6fFJ\nWKcj5MuXT+PGjVPLli1VsWJFPffcc2rUqJFWr15tqSd//vxq1aqV5dwFCxbo5MmTlu9S807q1q1b\nkv7pCyTxAuAcHo23HwAA4JHg5eWldevWqXnz5tq0aVOiRlB8fLzc3Ny0du1aValSJSNDBQAAAIBE\nihcvrtDQUDVt2lRxcXGKj4/XoUOH1K1bNxUsWNB8EWIYhvr27au8efM+sKwcOXKoTp06Se47e/as\nxo8fb2krJRyg99tvv6lGjRqaPn26evXqZTnXESug9+7dWzdu3NDw4cMVFxcnwzB0584dtWvXTsHB\nwZowYUKaryLrSBs2bFDr1q0VExNjfmdbOX7p0qUqWrSoQ+p5//339eOPP5pJK6R7/y/8/PPPqlGj\nhjZu3GgZsAkAD+Pp6al33nlH/fr1U9OmTTV//nw98cQTDq8nNjZWixYt0oQJE/Trr7/qjTfeUOfO\nnS3HtG3bVt7e3mrfvr1l1dWoqCgNHTpUy5Yt05w5c1ShQgWHxwcAyPzmz59v/mxr1yxcuFCVK1dO\n9kC0PHnyqEaNGmkVIgAAD2VbsVy69ywrXrx4qsu7efOm2X9Uvnx5M8lEXFycOcDb1hfYtm3bFNfR\nu3dvjRkzRuHh4ZKkK1euaNasWRo0aJDluD/++ENHjhzR999/Ly8vL23fvl3+/v567bXXNGnSJDOO\nDz/8UAMHDjQn9ALA4+yZZ54xfzYMQ56enmrcuLHatGmjVq1aycfH54Hnnj17VsOGDTPvn5JUqlQp\nzZo1K83jLl68uEaOHKl9+/ZpwYIFD43TXlFRUQoMDNTixYvNa3RxcdHixYtVunTpFJf36aef6syZ\nM/rmm29kGIb+/vtvNWvWTO+9956Cg4Mz1bsoAAAAwFm88sorql+/fobV/+yzz6a6DE9PT9WvX19h\nYWGJFvGxteVcXV01c+ZMS4K5v//+WyNGjLD03TVo0MDSjkypGzduWLZJvAA4BxIvAAAA06lTpzR6\n9Ght2bLFMoFI+ifbXUxMjJo2barBgwdr4MCBypUrV0aGDAAAAAAWDRs21IQJEywDvlauXCnpn0lE\n2bJl0/Dhw+0qPyoqSh06dND169fN8vLkyaPu3btrypQp5kubyMhIvfrqq9q5c6c+/fTTFK0ilBxD\nhw5V0aJF1a1bN0VERJhttkmTJunIkSNaunRpmgzac7S1a9eqXbt2io6ONr+z/Q7HjRunxo0bO6wu\nwzC0dOlS+fv7648//rAkX/jjjz9Us2ZNhYaGqkGDBg6rE8DjrWfPnnJ3d0+T1fAiIiL0+eefa8qU\nKeY9yzAMXbt2LcnjmzRpoh07dqh58+Y6f/68eS81DEP79+9X5cqVFRQUpNGjRytLliwOjxcAkDnF\nxsZq7ty5id4JxcfHa8iQIfLz89PLL7+ckSGm2MWLF/Xjjz/aff6lS5ccGA0AID0cO3bMsv3000+n\nqrwNGzaYPxuGIX9/f3N7/fr1unz5stneqlq1qgoVKpTiOnLmzKmuXbtq1qxZMgxDpUqVkp+fX6Lj\nbO3B+Ph4hYeHK3/+/JLu9Q1Onz5dkZGRkqTLly9r9uzZGjhwYIpjAYDMJleuXCpSpIgqV66sNm3a\nqGXLlslOev3kk09q/fr16tGjh86dOycvLy+tWLHCIUmzk2P48OFplqzg0qVLeumll7R7927LJKOJ\nEycmWgE2uVxdXbVy5Uo1bNhQe/fulWEYiouL04gRI7R+/XotXLjQYcmzAQAAADhG4cKFVbhw4YwO\nI9Vq1aqlsLAwy3e29lS+fPk0c+bMRG2dfv36mX13Nm+88Uaq4rh8+bJl29vbO1XlAcgcSLwAAAB0\n8OBBTZo0SStXrlRsbKwlq/f9K7gahqGbN29qzJgxmjRpkrp06aLXX39d5cuXz8hLSHO2AQs2ZOwG\nAAAAHl1Dhw7Vrl279OWXX5rtGOmfds0bb7xh16rksbGx6tixo3bt2mUZtPbxxx+rffv2qlSpkgID\nAxUeHm7WGxISoh9//FErV65MVfbspLRt21b58uXTSy+9pGvXrpl1fv3116pWrZrCwsJUrFgxh9bp\nSDNmzNCQIUMUGxtrfmf7nXbp0sXu5BgP4+vrqy+//FL16tWzrFxoGIauXr2qJk2aaMKECRo6dKjD\n6wbw+HFzc3N40oXr169r5syZmj59ui5dumTe2219dWfOnHnguc8++6z27NmjFi1a6NChQ5b+vNjY\nWE2cOFGLFy/Wm2++qd69e7MSKgBAISEhOn/+vPl3sbe3t8LDwxUTE6OoqCh17NhRly5d0uuvv57B\nkSZPfHy8FixYoAULFqSqnITPXgDAo+/gwYOS/ulXSm0f3MaNGy3l1apVy9w3d+5cy74OHTrYXc/A\ngQN17do1vfbaa6pbt26i/X/++ael/8rX11cFChSQJPn5+alDhw5asGCB+dyaMmWKXnvtNbm5MSQU\nwOPvt99+k4uLi13nNmjQQHv37lXz5s3Vv39/lSlTxsHRPVhajTc7fPiwWrZsqbNnz1reXwUFBSko\nKChVZWfPnl3ffPONGjdurH379pll79ixQ+XKldOIESP05ptvysPDw0FXAwAAAABS06ZNtWfPHt25\nc0eRkZHy8/NToUKFVKtWLTVv3jzRIkgzZszQ8uXLLe94GjVqpKZNm6YqjuPHj1u27RlzCCDzsa/X\nCQAAZHoRERFauHChatSoIX9/f4WGhiouLi5R0oWOHTuqbdu25vcJ90VERGjOnDmqWLGi/P399d//\n/lcXL17MyMtKM/evJujo1WoBAAAAONaCBQv09NNPJ5oskzt3brsm1UdEROg///mPmczBNrCsc+fO\nat++vSSpQ4cO+u6771SwYEFJ/wzAPnbsmCpXrqw1a9ak/sLuU6tWLX333XcqVKiQpb32888/q1q1\najpx4oTdZafVRKO4uDj1799fAwYMSDLpQvv27VM9UephKlasqPXr1yt79uyWa7St1BQcHKyGDRvq\n7NmzaRYDAOcUExPzwH1nz57VkCFDVLhwYb3zzjvmKgy2/jjDMJQ/f361bt36oXXkz59fP/zwgwYM\nGCAXF5dE/Xnnz5/XoEGDVKRIEX3wwQe6deuWQ68xrVy9etWy7e7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CGkasrMzMTNmzflsgFVkP/Dhw/s/J04GIYpcSK6uNzd3fH777+Dz+ez9ZuamuLcuXNiBzoS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\nA3rvvfdUpkwZBQYGqmPHjmrWrJlatGiRKfUHAKQuq8fMstrt27cVH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dJR47duxYWFlZwcPDg7unXrJkCQYMGID27duXehzLsrh9+zbXQSheuYBhGNSoUQNTp07F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\nX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QKJFwB4pZiYGL344ovatm2bOQ4uf/78Wr9+vQICAiRJjzzyiIKD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\nYUOLAXUmk0n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dMrG5fPlyrMkhzMcEBgYmKXlEWFiYxXEmk0m3bt2S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TqF18Ha2hoJCQm8yW+NGzdGTEwMtLS0FF4+IYSQ\nstGkSRORi2hKMnnyZOzbt49rnwwMDKSKdq0obdu2xdevXyU658mTJ3j16hWvz9K9e3elTBCjSWmE\nEFL+5OXl4cyZM4iIiEBkZCSSk5NhaGgoFHhBQJqI5oXv/+Kcr4io6YQQ8rPJycnBixcvFJL3+/fv\neccvXrxQyA6mwPdgpooMhECBFwghpGLLzc2Fo6Mjb2FoRkYGatasKbcyWrdujXv37nHHV69ehYmJ\nCT58+ADge/+ldu3aiIqKQrVq1bh05ubm3CTCVatWITs7u9RnYPn5+Rg1ahQXSEIwlrdlyxbUqVOH\nSzdz5kwEBARwE7dNTEwQFRVFwRcIIURMFX3sydvbGzt27ODaChUVFQQGBvLaCoEPHz5g5cqVvLZS\nHMp4niPJ38XFxYXbOVfeCgoKSuwXCnZcJISQn5WNjQ0vMJy8COZnyEOfPn1w6tQpueRFCCE/KhcX\nF8TExCAxMREODg5YvXo11NXVSz1v0aJFuH79OubOnVtiMIEJEyYgJycHM2bMQH5+PgYPHoxdu3bJ\n8xKkUjQgXUUvhxBCKgonJyfepnguLi7cZ2vWrMGZM2dw/fp1MAyDly9fwsLCAsePH5e4nAcPHiA3\nNxcMw4BhGLRv3x6qqqpyuw5xbdmyRe5Bwi9evCjxXIaXL1+ifv36cq0HIaR8oMALCmBvb89NJGMY\nBm5ubqhRowYvTVZWFpYvXw47Ozs0aNBALuVu3boVixYt4nUiOnXqJNcOlJWVFTQ0NKCtrY1+/foB\nAOrVq4etW7fy0mVnZ6NVq1bcpMMvX77g999/x6hRoyQqLz8/H4aGhtwxwzAwMTHB/v37ZbyS7z8q\nYmNjuYXB9evXh62trcz5lpXr169jyJAh3KIwwaSQSZMmwcnJScm1I4QoW7Vq1XDs2DF07doVHz9+\n5NoGLy8vtGzZEtbW1gor28nJiQtABHy/P9WsWRMxMTHQ09NTWLmEEELKv+TkZAQHB/MmNTs6Oiq7\nWjyenp4Spc/Ly0Pr1q151zR48GBERUUpqIaEEEIqgk+fPiEmJgYRERGIjY3Fp0+fAIB76FTcYh1p\nJ1yLu5MeIYQQ+bl9+zbv+YWisCwLU1NTheTNMAyePn2Kxo0bKyR/QPSuEMqYeEEIIUQ6np6e+Pff\nfyXawVsWsbGxGD16NL59+wbgezuoqamJsLAwNG3alJd2wYIFiIyMRE5ODhiGwfr168EwDNauXVts\n/idPnsTly5d5Y3mzZs3C6NGjeenWrFmD+Ph4vHz5EgzD4NSpUzAzM0N0dDQFECKEEACPHz/GL7/8\nIlZalmVhbW1d7BwFY2NjnD17Vp7Vk0lERAQXdEjQVixdurTYBU+Ojo5IT0/n2sbOnTvDxMRE7PIU\nMYfCysoKAQEBxX4uaNPd3d3h7u4u9HlUVBSGDBki8twLFy7AxsYGO3fuRJcuXaSqX//+/dGwYUO4\nubmhYcOGUuVBCCE/A3n1u+gZEiGEKIeamhoCAwORkpLCrbsRh6amJo4ePSpWWisrK9SpUwfe3t44\ndOiQVONWp0+fLjaYztWrV3nH/v7+OH36tFC6hQsX8o7Lqr2hdo0QQr6Li4tDfHw8N57Vr18/dO/e\nnftcXV0dwcHB6Ny5M759+waWZXHixAmsXbsWixYtkqisW7du8Y7LYs5ESWRtC6TtLwnGDQkhPy4K\nvCBnERERiIuL4xqrdu3awcbGRijdxo0bsX79emzatAk2NjZwdnYWGRVbXBs3boSDgwPvpt28eXNE\nR0ejcuXKUucryoQJE0pNo6mpidWrV2PixIncdzFnzhz069dPot0v1q9fj8TERC6PypUrY8OGDbJU\nn8MwDPbv34+OHTti9OjR8PT0hLa2tlzyVrTY2FiYm5vjy5cvAP5rsP/880/s3r1bybUjhJQXv/zy\nC0JCQjB06FDuPsGyLGxtbdGsWbMSI6FKy9PTE+vWreMFXRAMAv76669yL48QQkjFsn79euTl5XHt\nRNOmTTFy5Egl10o2u3btwpMnT3h9seXLlyuxRoQQQpQlOTkZEREROHbsGM6ePYu8vDwA/wVbAP4b\nwxE1Xte1a1eRu4KXZs2aNVi8eDHX52vbti1u3rwp28UQQggRG+2oUzJRbRstWCWEkIrh5cuX8PDw\n4I17KTJY3KVLlzBs2DCu7RDMDzhy5AiMjY2F0nfp0gV+fn6wtLTk8ly/fj20tbWxdOlSkWUMHDgQ\nycnJWL9+PXx9fdGtWzeR8w90dXURERGBHj16IDs7GwzDIC4uDtOmTaPn8YQQUkhJ7UJFXOQZHx+P\nCRMmgGVZbhxv9OjRWLFihcj0MTEx2L17N9cv1NDQQGBgIFq2bFm2FS+GqO+9pL9LSRPGc3Nz4ezs\nDF9fXxQUFGDEiBH4559/JN5wauPGjYiPjwcAHDhwAPb29nB2dq4wc/YIIRXLiBEjcOzYsTIv19fX\nF3PnzpVLXvIcd5R1HLNwUCJCCCHAhg0bcPLkSbHS+vr6KrQuVatWxZgxY8RKa21tjeHDh3PHZ8+e\nxapVq0o9j2VZBAUFCb3PMAymTZuGRo0aKW2jog4dOiilXEIIKQ/y8vIwf/587ve6ioqKyA3oWrZs\nCR8fH0yfPp1Lu3z5cvTq1QtGRkZil3f79m0A/40jGRgYyO1alEHafhL1iwj58VHgBTnKzs7GvHnz\neINLGzduFLqZfv78mVuUmpOTA19fXzx69EjqAT4nJycuP8HNXldXF7Gxsahbt67M1yWt8ePHY9Om\nTdyOEWlpabC0tBT7Oi9fvowVK1bwvs/FixcL7WQhi8aNG+Pff/9FrVq15Janovn5+WH27Nm8CScM\nw2DgwIEICQkRmrC4atUqvHr1CmvWrKGHVIT8hAYOHIiVK1di2bJl3GKf3NxcjBo1ChcvXkSrVq3k\nVtbu3buxaNEi3oIiVVVVBAYGomfPnnIrhxBCSMX07t077Nmzh/f7XjDYV1FlZ2dj1apVvGsaMGCA\n1Dv8EELIz6ikBxcVaRFraGgo9u7dyx2X1r5VqlRJbmULdoIVlFulShW55a0IP8rfnBBCgP/u94ro\n15TFIqWy6I8VFBQIvUeBFwghpGJwcHDA169feWNfBgYG+N///idRPvv370dBQQGXh4WFBa8tqFev\nHoDvwehGjx6NkJAQAIC2tjYiIyPRq1evYvO2sLBAYmIifHx8uHquX78ekydPFtpB++DBg4iMjOSO\ng4ODYWxsXGy71LFjR+zYsYML7MCyLEJCQmBvb4927dpJ9B0QQggp/2JiYjBy5EhkZ2dz/bFu3boh\nICBAZPqPHz9i2rRpQs+9ykvQBXlTVVXFzZs3UVBQAIZh8ObNG5iamuL8+fNibwr18OFDLoAsAGRl\nZSE0NBR2dnY0p40QohCKHLsTRRFBCRiGQc2aNaWa48yyLB4/fsybW66vrw9VVVWJ8/ry5Qtev34t\n8XmEEPIjS0xMRHR0tLKrIRGGYdC3b1+F5F2lShUMGTKk2M/37t2L0aNHo2rVqmLl9/79e6iqqqJG\njRryqiIhhPyQ1q1bh7t373JrhSZOnIj27duLTGttbY2IiAhER0dz64rGjh2LxMREsTfZFgReEDA0\nNJT5GqRhZGQEd3d3mfPx8vLCx48fwbIsdHR0YG9vL9H51atXl7kOhJDyiQIvQH6TfdetW4enT59y\njdWYMWNETgJYt24d0tPTucGsKlWqiNxFoTTZ2dmYNm0aAgMDeYN1urq6iI+PR/PmzSXOE/j+UGPx\n4sVYunSpzAEJ9u7di06dOiErKwssyyI6OhouLi5YuXJliee9efMGo0aNQk5ODvdet27d4OzsLFN9\nRKlIQRcKB9kA/huo7dmzJ8LCwqCmxv+/9OvXr7FmzRp8/foVkZGR2Lp1K0xMTJRRdbmgyffkR6Xo\nRSeLFy/GuXPncOLECa6N+vTpE0xMTHD58mW53Af9/f0xY8YM3v1JRUUFe/furfA7mSsCLTQihJQn\nZXVP8vHxQWZmJtdW1KpVC1ZWVnLLXxk2b96MlJQUXn9s+fLlEuVx5MgRzJw5kzs2NDTE8ePH5VZH\nUagdIoSUFyXtmirOjqr//PMPgoODRX727t077vWnT5/g4OAglKZFixawtbWVqM45OTk4ffo09u3b\nx73Hsiy3A2rRRbIMw6BLly5o0aIFbwxPnoEXMjMzecfiTlRQBln/5oQQIm+y/Dbu3LkzFyBZ3ry8\nvLBw4UIA3++JiYmJFXaBZ15entB7yrzPU3+IEFKelOd7UlBQEI4cOSJ0z+7Rowe8vb3Fzufbt2/Y\nu3cvl4+2tjYvaF1hDMMgICAAr1+/RmJiImJiYsQKcLpu3Tr8888/OH/+PFq0aIGIiAihoAsAcOPG\nDW5XPkEAiNKekVlYWOD8+fPw8/NDgwYNEB4eLrc2uTz//QkhPwdZ7kN169bF9u3bi/08NjYWR48e\nBQBuwnePHj1EphUE4FGm8PBwjBs3Drm5udx7+vr6OHr0KDQ1NUWec/jwYbx69Ypr45o0aYJly5aV\nSX1L07VrV2RnZwu9/+7dO5w8eZKrc5s2bUS2aw0aNBB6T0VFBaGhoejcuTOeP38OALh58yZmzpxZ\nbHCKwgoKCmBpacmNZbIsi1q1aiE2Nha6uroSXZ8AtZeEVGxl9XtYmnEoaQOiynvMSzA/eO7cuRLP\nQwCAlJQUNGrUiPfenTt3xA6YU9iBAwcwbtw4ma6R+kCEkPJEnvekwvOmy6vSdvMu6f5eWrsoTttw\n5MgRTJkyBY6Ojpg/fz5mz55d4ryGe/fuwdTUFA0aNMDJkyehoaFRahmloXaIEPIjevToEdzd3bn7\nfOXKlXnBCB4+fCgUJHTXrl1o27Yt0tPTAQAvX77kNtkODw/nxn2K888///ACkcbGxiIuLk6s+pqb\nm0NPT0/CqxStU6dO6NSpk8z57N+/Hx8/fgQAaGlpYfHixTLnSQj5Mfz0gRfkNdn3/v378PDw4BqP\natWqiZxwkJKSAl9fX14j4+rqCn19fYnq/eTJE4waNQqJiYm8zlq1atVw8uRJtGnTRqL8CrOyssKB\nAwdw5MgRHDx4EF27dpU6r1atWmHNmjWws7PjrnnVqlX45ZdfMHHiRJHnZGRkYNiwYdwCJpZlUb16\ndYSEhPzUuyB9+fIFfn5+QkEXjI2NERUVJXLC/uLFi7ldSF6+fIkRI0bgwYMHUgflKAvF/X+NJt+T\nH1VZLDphGAb79+9Hx44deYtDHz9+jBEjRiA+Ph7q6upS5+/v749p06ZxA0+CoAt+fn6YMGGC1Pn+\nqGihESGkPCmre1JGRga2bt3K6wfZ2tqicuXKuH37NhwdHWXKX1IaGhqIiIiQKY8vX75g7dq1vGv6\n/fffYWRkJFE+WVlZeP/+PfcdCwbwFIXaIUJIedG7d+9iF6u6uLjAxcWl1Dzu3bsnfe3q0gAAIABJ\nREFUVjDTjIwMken69OkjVuCFt2/fIjo6GpGRkYiLi8PXr1+5zwr3gwSBFtTV1dG7d2+YmZlhxIgR\n0NPTQ2xsLAIDA7nzpJnUVpxv377xjstr4AV5/M0JIUSeKtJv47KY8JWcnCzxsypJFG4zq1SpopAy\nfH19MXfu3GI/r0h/c0LIj68835MePXqEWbNmiSz73r17EuWVnJzMO27cuHGJ6dXU1BAWFoYXL16g\nbdu2YpWhqqqKgwcPYsGCBdi8ebPcd8Hz8fHBt2/fsG7dOrlNCCzPf39CyM9B1vuQtrY2pk+fXmz+\n4eHhvOPevXtjypQp0lS1TISEhCAnJ4d75qOnp4eYmBjUqVOHl279+vVo164dBg4cCCsrK/j7++Pi\nxYtQUVGBv7+/XIOtymLmzJm8oN8C58+fx8mTJ7ljMzMzuLq6ip2vjo4Ojhw5AmNjY+Tk5IBlWQQG\nBqJ79+6YMWNGiee6urri8uXL3HdcqVIlHDt2TGjyv7jE/TdMCCmfyur38JQpU9CzZ0+JzomMjMSZ\nM2e48h0cHFC/fn2J8hC1YZ8yvHnzhnespqYm1+dTkqA+ECGkPFHEPUkwX6AiKulZ/aBBg7jFtAzD\n4OTJk+jbt69E+efm5sLJyQkMw+D9+/dwdnZGRkZGsbuUx8XFYcyYMcjIyMCzZ88wbtw4HD58WOZ5\n9KJel/YZIYSUdzNnzkRWVhaA//ovDRs2xJUrV7BixQrExsbir7/+Qr9+/bhzdHV1sWXLFowdO5Yb\npzlx4gRu3ryJrVu3Ij4+vsQyBfdKwbnz588Xq64Mw8DQ0JD3nCUgIEBksO6kpCTe8dChQ4U2qh48\neLBc5p4XDroq+C4JIQT4yQMvyGuyb35+PiwtLZGdnc11mjw8PERG5HZycsK3b9+4hsbAwEDkjnsl\nOXr0KKZMmYJPnz4JRZ/LyMiAvb09Vq9eDUNDQ4nyBYBVq1bhwIEDYBgGL168QK9evbB+/XrMmTNH\n4rwE5syZg9jYWMTExIBhGBQUFGDKlClQV1eHubk5L+2XL18wcOBAXgQkFRUV7N69u9RJGPL09OlT\nPHnypMzKK0nPnj2hoaEBLS0t2NnZYeXKlQC+/+gYNmwYQkNDRUZXv3jxIgICAngLwaytrct10IXT\np08X+5midg4jRJnKctFJ7dq1ERoair59+yI/P5+7L6SlpeHVq1do0qSJVPk+fvwYM2bMEFpstGnT\npnI9caKwtWvXSnVeadH8RKGFRoSQ8qQs70murq5c/wUAqlWrxi2GSU9Px4kTJ0qNrC1P8pj4tnbt\nWrx79473wEWaXSbKErVDhBAiGQ8PD0RGRuLKlSsoKCgAAC64QuGJEyzLomrVqhg0aBDMzMxgYmKC\natWq8fIqev+V58S2jIwM3rGiFrMSQsiPhH4bF08Rk8qK9vWUMXGN/uaEkPKkSZMmxd6TSrpflSQj\nIwNpaWnccd26daGtrS1xPrm5uTA3N8eXL18A/HcPF/SD7t69K1F+z549414zDCO026ooN2/eRG5u\nrtAiodJYWVnh2rVrxX5eNAjEjRs3oKqqKlbelpaWuHv3rljXX7NmzRJ3WbK0tISlpaXIz6hNIoSU\nBUX/Ns7IyMDp06fL9LmPrHx8fBAbG4svX75AV1cXp06dEgoIkJSUhMWLFyMvLw8DBw6Et7c39u3b\nh7Zt28LW1ha9e/dWUu3LVufOneHq6opFixYBADfBfvjw4cUGKDp37hxWrVrF/ZtQVVVFUFAQunfv\nLlUdSvp3WtLcM0JI+VCWYzSmpqYSn5OamsoFXgCASZMm4bfffpNbncrS69evece1a9dWSj1oXI4Q\nUp4o4p4kmDsQGBiIcePGyVpFuXN2dpZqnnRmZibOnDnD69tJ08fbtGkTHj9+zOVTr149ODk5FZte\nW1sbOTk5XHlHjx6Fra0ttm7dKnHZALVDhJDyqaT7qbj32t27d+PUqVPc/bVRo0ZYsmQJnj9/jh49\neqCgoAAMw2DOnDm4desWL3DBmDFjcODAAYSHh0NLSwuHDh1C+/btAfCf5Yuqi6RtQUljhM+ePeP1\nv0RhWRbnz58XylNem0pUqlSJq19ZB16oKGOnhPysfurAC/KyevVqXL16lZt43aVLF9jY2Ailu3Ll\nCoKDg7lGQ11dHbt27RJ7gll6ejrmzp3L5QFAaJI3AMTHx6Nr164YMWIE3N3d0aZNG7GvRbATuiDf\nvLw82NnZISEhAf7+/lLvmBcaGgojIyPcu3ePy9fCwgLfvn2DlZUVgO+DfMOHD+cFXWAYBmvWrMGf\nf/4pVbnS2rdvHxfgQJkYhsHTp0+5oBP29vbw8fFBRkYGpkyZAj8/P5H/fvLz84X+DdaqVQseHh5l\nUm9CSPnUo0cPuLu7c5FDzczM4O/vL9XEO4HmzZsjLCwM48aNw9evX8EwDLy9vUW2g+WN4P7p7Ows\nUx7U4SGEkJI9fPgQGzdu5P3Gd3Z2Rq1atXjpFHU/Lfp7WR7l3Lt3D56enrx24I8//pB4twxCCCGy\nK2lcrfACodLOzcvLE1pQtGTJEu5Y8J6gLSs6Njd69Gjs2bOn2Lrk5eXxjuW5+93Xr195dZGlj0cI\nIUR206ZNQ2hoqEx55Obm8o6NjIygoqIiU55OTk5c21aSshjrUkQZtAsRIeRnd/DgQUybNo073rVr\nl1QBsj09PXHjxg3eWF7lypWRmZkJAHj16hXS0tJQt25dsfK7d+8e77hZs2alnjN8+HB8+vRJ4rpL\ngmVZmZ4PlaRPnz44deqUQvImhJCKIDo6Gjk5OTL/Rr916xYOHDgglzo9ffq0xM8bNGgAW1tb+Pv7\n49SpU2jdurVQmgULFiAvLw8MwyAuLg6HDh3C8uXLsWPHDrAsK9UcCUdHR7lNEi9LCxYsQFRUFM6d\nO4dff/0VO3fuLDbowqdPnzBhwgQUFBRwvy18fHxgZmZWxrUmhJCfT0pKCu+4YcOGSqoJIYT8HH60\nucSnTp1Cdna2TH271NRUuLq68sYaPT09oaWlVew5RkZGCAoKwujRowF8/1537NgBPT29cr8pEiGE\niKPwfbXoPbakzwpLSkqCnZ0d7/66ceNGVK5cGY0bN8aUKVOwc+dOAMCDBw/g5eXFBdEU2LZtG5KS\nkrB//35eMOnC61TL6hm8qHIKt6uKrEfhzZMUEXhBnHmLNNeBkPKJAi/I6NatW3B3d+c1VkZGRtix\nYwdycnKQk5OD3Nxc5OTkICwsjDeB29nZGe3atROrnAMHDsDOzg5paWlCE7tNTU3x4cMHnDt3jpf/\n0aNHcezYMUyePBmurq6oX79+qeVs3boVLVq0wKJFi5Cfn89NJD98+DCSkpIQEREh1QMfbW1tREVF\noWvXrnj79i0YhkF+fj6mTp2Ke/fuwdzcHGZmZkKBH2xtbbFgwQKJyxMYMGCA0ARFUcaOHYsZM2YI\nva/MxktU57t69eqws7NDXl4eVq1aVey5vr6+uHXrFu+79PDwQM2aNRVZZUJIBeDo6IhLly7B2NgY\n8+bNk0ueJiYm+Pvvv2Fqago7OzvY2dnJJd+y8KMNdBJCSHlkb2+P3Nxc7rd1w4YNhdoKRf/uLjwQ\nKA/Tp0/nTRrU0NDApk2b5JY/IYQQ8ZS0S+gvv/yCJ0+egGVZNGjQAM+fPxdK8/XrV4SFheHo0aOI\njo7G58+fRQZXKxxsoWnTphg+fDi0tLS4MUFxZGdn844LP7SRVUZGBu+YAi8QQohyZWVlcQFKAdnH\nn1iW5Ra7SqpwO1W0LSpKRUWlxIlu0hI8Kyv8vELaIN+l0dDQUEi+hBBSkcg6BjZz5kyEhYXh+vXr\nYBgGgwcPRqtWreDj48OluXLlCkxMTMTK7+7duwD+61eJu2kEBb4mhJCK6+jRozLnwbIsgoODERwc\nLIcafVda27JgwQJYWFjg119/Ffrs5MmTOH78OJdH/fr1sXDhQgDfd0KfM2cOduzYIXF9LCwsKmTg\nBYZh4O/vj6CgIDg5OUFdXb3YtNbW1njx4gV33rx58zB79uyyqiohhPzUbt++zb0WPOMihBBCxBUT\nEyPy/b///hu1atXCb7/9Vmoec+bM4eZhAEDPnj0xfvz4Us8zMzODr68v5s6dy/XDVq5cifr168Pa\n2lqyCyGEkHKkd+/eyM/PF/mZv78//P39S80jOzsb5ubm3BwChmFgYmKCYcOGcWlcXV0RFBSEzMxM\nsCwLd3d3TJ48Gbq6ulyaunXr4vbt2yI3gGBZFi1atMDDhw8lvUSeoKAgWFhYFPt5y5YtRT5vunnz\nJm88aeDAgVBT4y+B7tChg0x1Eyg8d6G0ORW5ubkljoMVdfr0aZHvN2nSpNh/B4SQ8oMCL8goJSWF\nW0Qk6BD4+voWm17ww79Dhw5YtmxZqflfuHABCxcuxMWLF3m76wHfb+4+Pj5c5+HYsWNwdnZGUlIS\nl4ZlWezZswchISGwt7eHk5NTqZOv582bhxYtWmDChAnc5ECGYXDnzh0YGhri4MGD+P3338X6fgpr\n2rQp4uLiMGDAALx79477Pry8vODt7c37fhiGwfTp07Fx40aJyyksISEBOTk5JT44YxgGhoaGMpVT\ndEdEeSzqKi6P5cuXQ1VVtdjzHj58iOXLl/PO79KlC3UyCSGcsLAwuefZuXNn3L9/H9WrV5d73opE\n0eEIIUSxIiMjERsby/ud7ObmBk1NTS5NSQOJstqyZQscHBy4/AV1MDY2ljrP7du348KFC7xrmj9/\nPlq2bCmvahNCCCkDCQkJ6N+/P3JycgBAaNytcF+hXbt2GDFiBMzMzNC+fXsAQEREhETlFX0wU6VK\nFVmqz/Px40fecbVq1eSWNyGEENlJO/4krx0cxH1m0ahRI3z+/FnqcoozdepUoQkiL1++pPaKEEIU\nRNZn1bVq1UJ8fDx69eqF9PR0BAQE4Pjx4wD+a48uX74sduCFwpsFABA78ELh8uSpLHZIomdPhJCf\nWWZmJmJiYirkvVBHRwc6OjpC7xcUFGD+/PlCz7pEBVYtbt5Y0fan8LGenh7S0tLkcg1Xr17l7VAo\njVGjRkk0p8TFxaXUNIW/Dy8vL3h5eZV6zsSJE7Fv3z6x60EIIUTYzZs3AfzXTxQVXIgQQggpTnR0\ntFDfZsuWLTh27Bj09fVx9erVEp/1REdHIywsjOsDaWhoYPv27WKXP3v2bDx+/BgbNmzg8pg1axbq\n1auHoUOHSn1dhBBS0Tk4OOD27dvcvbFq1apCay91dXUxZ84crF27FgzD4Nu3b3B1dcWWLVt46UQF\nXShL48aNw7hx44Tet7KyQkBAAHccGhoqss05c+YM+vbtK1GZK1aswPLly7njoptTZGVloVKlSkLn\nffv2Dfr6+hgxYgRsbW3FCkBECKnYKPCCjPr06QNNTc1io9qIepCiqamJffv2lbh4PjExEW5ubggP\nDxc58btr167Yt28fWrRowZ0zbNgwmJqaYvfu3Vi2bBnS0tK4AbOsrCx4eHhg9+7dcHNzg7W1dYkP\nuYYNG4azZ8/CxMQEqampXB3S09MxaNAg7Ny5s9gdBUvSvn17/P333+jXrx/evHkj9B0J6rto0SKs\nXr1a4vxFKS7oQkmRzI2NjeHk5CRW/n5+fvjw4QNXVuXKleW623vRRcwl/bthWRaTJ09GVlYWd1yp\nUiXs3buXS5OcnKzUaOnyiHpFCCmfFBV0gWEYPHv2TKyOnSQTOFiWhYqKCl68eCHVrkmjRo3CpUuX\nJD6PEEJ+Frm5udxENIHffvsNkyZNUnjZOTk5mDVrFvbs2cPbZZZhGDg6Okrd13j9+jWcnZ1519So\nUSMsXbpULvUmhBBSdvT09Hi7bxcNqgkA69evx4gRI+QyjlJ4rIZhGLkGXvj06ROvbdq1axeioqLk\nln9RTZs2xaFDhxSWPyGE/ChYloWenh5evXol8bleXl7c7qkMwyAxMRHt2rWTKI/8/Hyoq6srfcFT\ncnKy0Hv//PMP+vXrp4TaEEIIEUf16tURExOD5ORk6OjoCC3g/Pvvv8XKJysrC7du3eK917Zt21LP\nEzx7lzdnZ2esXbsWwPf2NTY2Fv3791dIWYQQ8rMKDg5GRkaGQjesKWuenp7cZHbg+256kydP5qXR\n0dHh7SKemprKbRTEMAwaN24sNN+BYRhUqlSJt9mTtOS1SZCgXoX/V9ZyC4+9Fi1D2jwJIYSUrqCg\nADdv3uQ996KFQYQQQsR19uxZJCcn89oRlmW5tU2PHz+GpaUlwsPDRZ7/5csXzJ49mxeczsnJCa1b\nt5aoHl5eXnjw4AG38VNeXh7Gjx+P8+fPizXOSAghP5rMzEzuXiy4v65ZswZNmjQRSrto0SJs27YN\nGRkZYFkWu3btwrx589C8eXOZ6zFx4kQEBwcD+B64QBEbPEii6LrUkj4vqmrVqrzjT58+iQy8EBUV\nhbdv32Lnzp3YuXMnFi1aBA8PDxlqTQgp7yjwgowqV66M3r1746+//hL5WY0aNZCamgrgvwcDLi4u\n+N///icyv7i4OHh6eiI+Ph4Af7I3wzCoU6cOPDw8YGVlJfJ8hmFgbW2NsWPHws3NDRs3buQ9yHn7\n9i1mzJiBLVu2wMfHp8TIPh07dsT58+cxYMAAPH78mHtfU1MTHTt2FO8LEkFTUxMGBgaIiooS2Xhp\naWlBX1+/2ChBkpD24VS/fv3EmvR37do1rFmzhvejZfbs2XILGiGp1atX49KlS7z6rFq1Cq1atRJK\nSw+pCCEViTzvWQUFBdxrNTU11KtXT6p8Cu/WTgghRJi7uzsePXrE+21aFr+Tnz59itGjR+P69eu8\n/lSNGjUQEBAAU1NTqfO2tbXlFrcKrmnDhg0idzUihBBSvrVo0QKVKlVCdnY2GIaBmpoa8vPzeQ9f\nHBwc5Fbe169fecdFH9rI4v3799xrlmXx6tUrqRb5FkeSidGEEEIUQ5qgoeWFoF9Y2OXLlynwAiGE\nlAPXrl3D6NGji/1cX18fRkZGaNOmDapXr47Pnz+DZVn8888/yMzMLHVM7Nq1a8jLy+PagWbNmonc\nSVxZKnL7Sggh5VVpu5cmJCSgZ8+epebDMAw8PDzg6Ogol3oV3SVPXA8fPsTKlSt5z4U2b94s1MdZ\nsWIFVqxYwR0bGhri+vXr3PHt27ehra0tdf0Bfrul6PE5cfMXJ52kdaWxR0IIkY+EhAR8/vyZd1/t\n2rWrEmtECCE/ppJ+vxoYGPD6BWWhpA0yJfmtXXjTz8L9oe7du+P8+fMAgGPHjsHb2xvz5s0TOt/e\n3p4L3AAArVu3xpIlSyS4ku9UVFRw4MABGBkZ4d69e2AYBhkZGRg2bBiuXLmC2rVrS5wnIYRUZJUr\nV0ZISAj69++PgoIC9OnTB7a2tiLT1qhRA7a2tlxwgLy8PCxduhQhISEy10MegUTlTdBWFVev4oJ9\nFh2z+/z5M3R1dYXSHThwgHvNMAyMjY1lrDEhpLyjwAty4O3tjZSUFNSsWRM1a9ZEjRo1UKNGDaiq\nqmLfvn2YPHkyd3Pu2rUrFi1axDs/NTUV+/fvR0BAAO7fvw/gvw6K4MaupqaGWbNmYeXKlahWrVqp\nddLS0sLatWsxY8YMzJs3D5GRkbwADrdu3UK/fv0watQoeHt7o2HDhiLzadq0Kc6fP49Bgwbhxo0b\nUFFRwb59+6SKfJqUlITVq1cjJCQE+fn5vEVQAoLO0IwZM7BkyRLY2NjAxsYGenp6EpcHAN++fRP5\n/sqVK7kHY7Jwc3PjHVerVg1OTk4y5Smtc+fOYcWKFbzObc+ePUtcJECTSQip+N68eQNzc3OFdFzu\n3bvHO3Z3d8fOnTvlXg4AhIeHo0aNGgrJu6jCnSZVVdUyKZMQQn42Fy5cwOrVq4XaJ1EBweQpLCwM\n1tbWQsER2rdvjyNHjsi0Y7mfn59QpNjBgwdj+PDhcrwCQgghZYVhGHTu3Bk6OjoYOXIkTE1Noa+v\nr7AI3EUDL1SpUkUu+bIsi48fP3LHosbb5KFwm169enW5508IIeTHlJ6ejufPnwv1DePj47F48WIl\n1YoQQgjwvS+RmpqKI0eOFJumQ4cOAL73B4yMjBAbGwsAyM3NxalTpzB06NASy/j777+51wzDoFu3\nbrJXnBBCSLl17do1XLt2jfccpbC5c+diy5Yt2Lx5M2xsbJRUS8lYW1sjOzubu55JkybByMhI4nxK\nGq8zMzPjje+JkpCQgJSUFADf29ShQ4dCS0tLKF2tWrUkrltRs2bNwuDBg2XOR1YtWrRQdhUIIT+I\nw4cPIzk5Wez0V69e5R0HBASgfv36Yp/fv39/qeZXy5uoHchDQkKwYMECJdSGEEJ+TAMHDkTNmjW5\n419//ZX3eXlZlGpsbIzs7GzuuHPnziWmz8zMxJEjR0T27ZYuXYrFixfjxo0bYFkWzs7OMDY2Rpcu\nXbg0UVFR2LNnD3e+iooK/Pz8oK6uLlX9tbW1ERkZCQMDA3z8+BEMwyA5ORlmZmY4deqU1PkSQkhF\n1adPHzg5OWHDhg3w9/cvMa29vT28vb2Rk5ODrl27YurUqXKrR3lajyhor4KCgtCmTRuhz42NjfHl\nyxeR5xadB/fp0yehNBkZGYiJieHatvr162PIkCHyqTwhpNyiwAty0KZNG5E35kePHmH27NncjVVb\nWxtBQUG8zoeLiwtWr17NBSIoGnBBQ0MDVlZWcHJyQuPGjSWuW7NmzXD06FFERUXBzs4Oz54943WA\nDh8+jOPHjyMiIqLY3YXq1KmD06dPY/jw4RgwYADMzMzELv/r1684dOgQ/P39kZCQAAC86xQcV6pU\nCVlZWbz33r9/Dzc3N6xatQrdunXDsGHDMHz4cIUv1hLXnTt3EBkZyetUOjo68jrQZeX9+/eYMGEC\nbxf3KlWq8KINCmhoaHCTZGTx9etX/Pvvv7zrb9u2bamLmJs0aSJz2YSQ/2RlZeHs2bMKWVxTdNAv\nKSkJSUlJcs1fcP/IyckpNp2gDTU3Ny8xv9DQUKHFTEXl5eXxjtXU6KcQIYTIW0ZGhtBvU0XLzMyE\nnZ0ddu3axbVdgjbGysoKW7ZsgaamptT5JyYmwt7eXuhhmLe3t0z1JoQQolyCsaqyUDSgQ9WqVeWS\nb2pqKgoKCoTG2hRB0LZS4AVCCKkYysNEh6IT1QXt1YULF8TaKZ0QQkj50atXL8TGxnL9jYiIiFID\nL5w4cQLAf32J7t27S1V2bm5usRsuSKLwBHPg+/NuURPopEH9JEIIAbZt28a9Ljo+5enpiQcPHoBh\nGNja2iIvLw9z5swp6ypKZOvWrTh37hzvWpYtW6aQckpjZmbGBV4AAF9fXzRr1kyu9UhKSsKhQ4cw\nevRo9O3bV6JzFy1ahMaNG8PS0lJkQAhCCFGmHTt2ID4+XqpzWZaFj4+P2OkZhsGmTZuUHnghJycH\nhw4d4towwZico6Mjbt++jZ07d0JDQ0OpdSSEkB/B2LFjMXbsWKWVX3R+QHHPhYYOHVrqOF5hR44c\nQUZGhsjAEerq6ti3bx8MDAyQk5ODvLw8jB8/HomJidDS0sL79+8xffp03vxwOzs79OjRQ+prZBgG\n+vr6CA4OxtChQ7m1VhcuXMCMGTOwZ88eqfImhJCKzMXFBd26dSt1nWmdOnXg6uqKtm3blotAm4rW\nsmVLkf0xVVXVYufTFd08VtSmTUFBQcjKyuLaxilTppSL4EqEEMWi1YYKkpubi3HjxuHr16/cD/4t\nW7YI7bA6a9Ys+Pv7IyUlhdf5qVy5MqZOnQpHR0c0aNBA5vqYmJigf//+WLVqFdatW4fc3FyuvN9+\n+w29evUq8fxq1arhr7/+EmuB6qdPn3DixAlERUXh6NGjXFSgop06hmHQoUMHbNiwAa1bt8aqVauw\nfft25OTk8NIIOkYXLlyAk5MTmjRpgs6dO6Njx47o2LEj2rdvj/r165d5o7VixQpeEAtdXV3Y29sX\nm/7jx4+ws7PDr7/+CicnJ7nVIz8/HyNHjsSLFy94k+z19fXRtGlTofT16tXD9evXZS73/Pnz6Nmz\nJ++9c+fOQVtbW+a8CSGSk3YideF7Z9E8SsqzpPPEJcl5Ojo68PPzKzHNX3/9VWrghczMTN5xacFi\nCCGESG7mzJlITk4us9/nFy9exOTJk3lBwQCgZs2a2LFjB0aNGiVT/hkZGRg9erTQxGwAaNiwIe/4\nyJEjOHz4sET5F91d49GjRxg3bpzE9dy8eTN0dHQkPo8QQkjZKPpQRl4TkIu2IwzD4Pnz53IZTxQY\nNmwYoqKiuGNaUEQIIeJhGAbv3r0T2uFIHOnp6bzjESNGSB1MTtROs2Wl6KR2QX8tJycH0dHRMvfX\nCCGEyKa09qHw5wMGDMDixYsBfL+fR0ZGltjGfP78GZcuXeKN1w0cOFCqeu7btw/Tpk2T6tzisCyL\nkSNHyiUvhmGQmZlJC5cIIT+1N2/eICQkhGsX6tevzwsUUKdOHTx8+JA7trOzQ35+fonzrJTp1q1b\nWLBggVA796MFFUhOToarqysOHTqEu3fvgmEYdOvWDa1btxY7jzt37mD9+vVgWRaLFy/G5MmT4e7u\nTvPHCCFEifz8/JCamipyzvb+/fvx8OFDHD16FLq6usqsJiGE/PB69eqFevXqKSTv169f4+rVq6UG\nXZBG4eBwHTt2FFp38r///Q9ubm5wdHQEwzB4+vQpHBwcsHPnTkyaNAmvX7/m6tW6dWusXr0aCQkJ\nUFFRkSgAQ3p6Otq2bYs///wTFhYWGDhwIFasWIHly5dzY44BAQFo3bo1HB0d5XPxhBBSQaipqYkd\nVGfhwoUKrk3FVjTwwsePH4XSCDYFZFkWKioqmDp1allVjxCiRBR4QUHmzJmDa9eucdFsxo0bh4kT\nJwql09XVRVBQEPr06QOGYdCyZUvY2NjA0tJS7pOYNTU14erqirFjx8La2hqXL19GvXr1EBYWBnV1\n9VLPLy7oQmZmJq5evYoLFy4gNjYW58+f53YUF1y/ILKc4LhNmzZwcXHhTapuWUVmAAAgAElEQVTz\n8fGBg4MDXFxcEBQUhPz8fF4nUNABe/78OZKTkxEWFsZ9pqqqijp16kBXVxd6enrw8vJCmzZtpPqe\nxHH9+nWEh4fzovG5ubkVuztTZGQkZs6cidTUVKirq6Nfv34wNDSUS13mzp3L7XavbOVhBy1Cfkay\n/P9f1H1WkeeJUpb3r6KBF2TZ/ZwQQoiwwMBA3sQ6RcrNzcWSJUvg7e3N/SYX/G+vXr0QGBgol0Wn\nU6dOxePHj3n5F+fOnTs4cOCAVOUI2tZ3795JnAfDMFi7di0FXiCEkHLs7du3vOOqVavKJd+igRc0\nNTXlGnQBgNAOsBR4gRBCxJeXl4cHDx7IlAfLsnj27JlU5xZe7KoMcXFx3OvC/TYAOHjwIAVeIIQQ\nJWIYBkOHDsWxY8fESt+pUyfUrVuX69ukpaXh5MmT6N+/v8j04eHhyMvL48bSmjdvLvPO3PIICi4v\nipjUTgghFdmqVau4Z/GCXd/c3Ny4zydPnow+ffrA3d2d6xfMnz8f6urqsLW1VVa1RcrIyMCoUaNE\nBuSu6Aq3WyzLYv/+/QD+m98HCI8FlmblypVcf+/Lly+4dOkSBV0ghJQ7ks5fkOe8uLL27ds3rF27\nllfvKlWqIDMzk7tfX758GQYGBoiIiECnTp2UWFtCCPmxeXl5yT3P7Oxs+Pj4wMPDg/fMhWEYaGlp\nYfbs2TLln5CQwAVTVVdXh5mZmcgNP+fNm4ewsDBcunQJVapUQZcuXbB27VrExMRw9VJTU8PEiRMx\nYMAAJCQkoE+fPjh16pTYdTl48CBev36NrVu3YuvWrTA0NMTly5dx+fJlREdHc+UsWbIEffr0QZcu\nXWS6dkIIqUiePn2KCxcuyCWvnj17onHjxnLJqyKqVasW7zgtLY13fOPGDVy/fp0bPxs0aNBP/X0R\n8jOhwAsK4O/vDz8/P+7HvL6+PrZt21Zs+p49e2LHjh1o1qwZfv/9d4XXr02bNrhw4QI2b94MAwMD\n6OnpSZXPpk2bsG/fPty8eZMLtADwH8YU7swBQJ8+fTB//nwMGTJEZJ6NGzeGv78/1qxZg/3792Pv\n3r24f/8+L6/C5QgUFBTgzZs3eP36NXJzcxUadAEAli1bxlt01bZtW0yZMkVkWpZlsXz5ci6CbF5e\nHsaPH4/ExESZJ9mvW7cO27Ztq3CDu4QQ+WnSpAny8/OlOnfbtm2wtbXl7iEWFhbYu3dvqefNmTMH\nW7ZsAfD9XrxkyRK4urpKVYeyVnSX2aIR6gghhEjv3r17mD17drF9AUWUt379el556urqWLFiBZyd\nneVSxubNm3H48GGuDPrdTQghRFrPnj3jTXwo+tBGWo8ePeId6+vryyXfwj59+sSrO/WjCCFEfMoI\nmFpe/Pvvv7h58ybXhmhoaMDAwAAXLlwAy7KIjo7Ghw8fULNmTWVXlRBCiJhMTEywZ88erl0KCAgo\nNvCCILCo4Jm6uDsvlabwZg/Sni9QEdtXQggpj16+fImdO3dy99X27duLXPDi6uqKlJQU+Pv7c/0E\nOzs7VKpUqVztEjd16lQ8evToh2knPnz4gLi4OBw/fhyxsbG8z0Q900tNTRU778uXLyMsLIz7e6qq\nqmL79u3yqzwhhMjBX3/9JVH6hQsXcgtlGYZBYmIi2rVrp4iqKcTixYuRkpLC21hi06ZN+PDhAxwd\nHVFQUACGYZCSkoLevXsjODgYpqamyq42IYQQMRw4cACLFi3C8+fPec/vVVRUMGnSJHh4eEBXV1em\nMtatW8e9/uOPP4qdG6CiogJ/f39MnDgR+/btw4cPH7iNaAXtz9KlSxEfH4+EhAQAwJkzZ3D//n38\n+uuvYtUlODgYwH/9lu7duwMA9u7diw4dOnDt3aJFiyjoAiHkp3P27FlYWVnJnA/DMAgJCfmpAwnU\nrl0bwH/tTdHNlQTrgQXtm4ODQ9lWkBCiNBR4Qc4uXrzILWJlWRaampo4cOBAqZGcra2ty6iG/5E1\nol2rVq1w7do1oUALhR/GMAwDXV1dTJgwAdOmTUPLli3FyltXVxcLFizAggULcOXKFYSGhuLkyZO4\ne/cul0bU7hEMw2DFihUyXVdpzp07x4vGxzAMb8GXqDrt2rUL3bp1Q0FBAViWxZMnTzBr1iwEBARI\nXY/9+/fD2dmZ993/KA/9CCFlIyMjg3csrx1Xy7MPHz5wrxmGoYndhBAiJ69fv8aQIUO4tkXw27RS\npUrIyspSePksy6J+/fqIiIhA586d5ZLniRMnMG/ePIkDSUjzm1zWCd/UDyCEkPLt8ePHSExM5L3X\noEEDueR9/vx57jXDMGjevLlc8i2s6C531I8ihBDxsCwLPT09vHr1SuJzvby8sHDhQgDST/DOz8+H\nurq60voLQUFB3GuGYfD7779j/PjxuHDhAhiGQVZWFvbs2YP58+crpX6EEEIkN2bMGOzZswfA93Yu\nPDwc79+/h46ODi/dy5cv8ddff/EmgI8dO1bm8gVjjqtXr4aZmZnE53t5eWHnzp0AvrdNO3fuhLGx\nsVR1sbKywsWLF6U6lxBCfjSurq7Izs4G8P3+OmPGjGLT+vn54c2bNzh+/DgYhkFBQQFmzJiBSpUq\nlVV1S+Tp6ckF5C7tudCGDRvw8eNHkZ8V7Qd6eHgUe40NGzaUe+CJGzduICYmBsePH8fly5e5zTyK\nPvMqHNCobt26GDlyJP744w+xy5k3bx4vHxsbG3Ts2FGu10IIIcpWdJ7ys2fPMG/ePHh7e6Np06bK\nqVQxjh07hk2bNvHartatW8PS0hIqKiqoXbs2pk2bhtzcXDAMg69fv8LMzAxeXl6ws7NTYs0JIYSU\n5MqVK3BwcMDFixe53++C3+BGRkbYsGGDXObLJSUl4fjx49zxmDFjhOaZF9aqVSv8888/AL7Ps6td\nuzbevHkDAOjVqxeWLVuGgwcP4tSpU1zbtHnzZm7jv5I8e/YM58+f513rzJkzAQA6OjoICQnBoEGD\nsHXrVkyaNEnqayaEkIqu8BiWJOcAotdkiqvwxrEqKipS51Me1KlTh3eclpbGvX779i0CAwO577ld\nu3bo169fWVeREKIkFHhBjh4/fozhw4cjOzub+4Hv7e0NAwMDsfPw9vbmOiCKoqqqisDAQJnzGTBg\nAAYOHIgTJ05w7wka4Jo1a2LYsGEYM2YMBgwYIHVDmpaWBl1dXdjZ2cHOzg7q6upISEjAqVOncOXK\nFTx8+JC3kKtt27YYNWqUbBdWisKLrxiGwaBBg4rdyUOgc+fOcHBw4AI0sCyLwMBAmJqaSlXf0NBQ\nTJkyhTumoAuEEGkIFs8I7iFaWlpKrpHivXv3jndMO7USQojsMjMzYWpqyoumzTAMLCwskJycjDNn\nzii8DgzDoFOnTnILunD9+nWMHj2aGxwU9/e2i4sLXFxcJCorKCgIFhYWXP5du3bFhQsXJK80IYQQ\npXvy5AlSU1PRsGFD1KhRA5UqVcL9+/cxffp0bgchAGjevHmpQVrFUVBQwC1eFbRVHTp0kDnfoijw\nAiGEKJ8skx6UIS8vD7t27eK1USNHjsSIESNQuXJlZGVlgWVZbN68Gfb29lBVVVV2lQkhhIihX79+\nqFOnDvesJSsrC9u3b8eSJUt46Xbs2IH8/HyuD9SsWTN07dpVbvWoV6+e2Bs+FFarVi3eccOGDaXK\nBwCqVKki1XmEEPKj+ffff7F3717ut7+Ojg4mTZqEU6dOiUyvqqqKgwcP4vfff8eVK1cAAGpqatDS\n0io2iEFZCQwMhJOTE29OmIqKCgoKCkSm9/X1RXJycqn5siyLtWvXFvt5t27d5Bp44c6dO7znZYUX\nZgn6Z4L/1dHRwahRozBmzBj07t0bDMPg9OnTePjwoVjlCBZ+Ad//jgYGBoiIiJDLdXTv3l1o4jsh\nhCjboUOHMH36dHz69Am3b9/GuXPnZN5ZXF6uXbsGCwsL7phlWaioqGDHjh3c/O1JkyZBR0cH5ubm\n+PbtG9cmODg44NGjR9iwYUOFXzRFCCE/kpcvX8LJyQkhISEAwPst36BBA6xduxbjx4+XW3lr1qzh\nnkdpaWlh5MiR2Lt3r1jnDhw4EHfv3oWtrS3i4uIQFBQEhmHw559/onbt2nj//j1YlsXevXuxbNky\n6OnplZifr68vL1Bcnz590KpVK+5zY2NjPHr0qNR8CCHkR1Z4DKusffnyhXutqalZ5uXLU9G25O3b\nt9zrTZs2cWtWGYbhgpASQn4OFHhBTt6+fYshQ4ZwnQKGYTBu3DjY2Njw0n358gVJSUlISkrC/fv3\nkZSUhGHDhsHS0hIAkJCQILcHEMVRU1OTKPBCbm4uoqKiYGpqCjU1/j+ZZcuW4cSJE2AYBq1atcLg\nwYNhYmKCXr16cRPl2rVrh9evXwP4HslIEMlOHBMmTEB8fDx3nJSUBHNzc5ibm3PvPXv2DPfv38f9\n+/dhaGgodt7SCAoKwtWrV7mOq4aGBnx9fcU619XVFeHh4Xj8+DF3vo2NDYyNjSXq9AUHB8PS0pJ7\nsMeyLNTV1TF8+HChqOuEEFKS1NRU3nHdunWVVJOyU3RnCQq8QAghsmFZFmPHjsW1a9d4g3e//fYb\ntm/fjsGDB4s8b8OGDTh58qRMZX/+/Jl3fOXKFZiamkqV16BBg2Brawvge//CxMQEX79+BfBf0IU/\n//wTFy9elGq3WkIIIT+HW7du4c8//xT5WeFJEMOHD5dLeTdu3EBGRgavDe7du7dc8hZgWVYo8AL1\nowghhJQmNDQUr1694tooFRUVDB8+HFpaWjAxMcGhQ4cAAM+fP8fevXvlvrsrIYQQxVBVVcWkSZPg\n5eXF9XE2b96M+fPnc7t4Z2ZmYufOnbw+kGAuBCGEkB/PjBkzkJeXB+D7+JednR0qV65c4jlVqlRB\ndHQ0unfvjhcvXiAsLAyDBg1CQEBAWVRZpBMnTnD9EkH71b9/f2RmZiIhIUFp9ZJGo0aNAIAXbEFw\nXNTMmTPh6urKe2/u3Lm4e/euWGUVnuCfl5eHyZMny1Bzfr6RkZEYMmSIXPIjhBB5yM7OhpubGz5/\n/gyGYfD48WP88ccfOHv2rNIDVicmJmLIkCHc4idBWzZnzhwYGxvz0g4dOhTHjx+Hqakp94yJYRhs\n2bIFL1++RGhoaIVfOEUIIRXdt2/fsGbNGnh5eSEzM5M3zla5cmUsWLAATk5Opfa9JHHnzh1uR28A\nGD9+PKpWrSpRHjVr1kRwcDDevHnDBSZSV1fHpEmT4O3tDYZhkJWVBXd3d2zevLnYfD5//gx/f3/e\ndc+ePVsoHQVdIIT8zMaPHy/VfOn8/Hx07NiRW0ck7SY/GRkZ3OuK3n+oVq0at3kEAKSkpAD4/rxr\n+/btXHukq6uLcePGKbOqhJAyRoEX5ODjx4/o378//v33X66zUaNGDfTv3x8eHh549OgR91/RRa4M\nw2DUqFFSl134oYiiFtvfvHkTI0eOhK6uLiZNmoSpU6dyOz90794dYWFhMDQ0RIMGDUSen56ejvT0\ndC5QgaRKi77UtGlTNG3atNhFXfKSmZmJxYsX8zpx9vb2+OWXX8Q6v1KlSvDz80O/fv2499LT0zFl\nyhQcP35crDx8fX0xf/587lgQldbf3x+5ubk4fPiwZBdFCPmpvXjxgnf8MwxCvXz5knvNsixCQ0MR\nGhoqdX4U7IYQ8rOzt7dHZGQk95udZVlUr14dR44c4SZbi5KYmIjo6Gi51EFwH05LS5MqT4ZhUK9e\nPQDff58PGjSICxYnyNvQ0BD79+/HL7/8Qvd+QgghxWrfvj0A0WNZgrajcuXKIicFSOPgwYO8Y3V1\ndfTo0UMueQt8/PiRGwcTKLpLLCGEEFJYfn4+3NzceM9SBDsaAYC1tTUOHTrEfe7u7o6JEydW+AkZ\nhBDys5g2bRq8vLy447S0NGzatAkLFy4EAGzbtg1paWlcH0JDQwPTp09XSl2Lev/+fYnHhBBCJLNn\nzx78/fff3G97bW1tsce9dHR0EBcXhydPnqBv374i0yQmJiIoKEgudX3y5EmJn0+ePJkLIAEAjRs3\nRnBwMEaOHFnieSXNaSv8LKmkdPLelbB69eqoVq0atzAY+L4Jx8iRI9GsWTMsWLBApjKLnivJM7OS\nxk0JIaS809TUxIkTJ2BkZITnz5+DYRjcu3cPI0aMQHx8vNCmdmXl9OnT+PPPP7mNKwT3VSMjI3h6\neoo8p1evXoiLi8OQIUPw4cMHLvhCREQEBgwYgGPHjqF69epldg2EEEL4LCwsEB4ezt2fBc9aRo0a\nBU9PTzRu3FjuZTo5OXGbgjIMI9N4niDogoCdnR02bdqEvLw8sCyL3bt3w9HRsdjr2LFjB28Dilat\nWsHMzEzq+hBCyI9IXV1dqvlbERER3AYKDMNg2LBh3PpQSRQOvFCe+g4PHjzgNhEXYFkW+fn5QvPf\nCqtfvz6ePHkClmWRnJwMANi0aRPevXsH4HvbuGDBAqirqyv2Aggh5QoFXpADc3Nz3Lp1ixfF+cOH\nD7CysuKlEzRMAoIBrqKdBkkeboj7kEba/AHg6tWrAL5PmvD09MTFixdx9uxZ7vMRI0aUeH5OTg73\nWppGpqTI22XJ1dUVL1684Oqhp6eHZcuWSZRH3759MXXqVOzevZvrCJ84cQLbtm2DjY1NieeeOHEC\n8+bN4y1qYxgGvr6+GD9+vFIjvxNCKqakpCTe4lF9fX0l10jx/v33X+61stsVQgj5ERSOnM2yLNTU\n1BAUFIRmzZopsVaSEbSD+fn5MDU1xcOHD3ltRJMmTRAZGVliIAlCCCEE+N6nqlq1Kr59+yY0YZhh\nGKipqWH37t1o0qSJzGUVFBRwO04IxogMDQ3l3l59+PBB6D0KvEAIIRWDshav+Pn58QKVA4CjoyP3\nun///ujUqROuX78OAHj+/Dk8PDywYsWKsq4qIYQQKbRs2RKDBw9GTEwM1x9Zu3YtrK2toa6uDk9P\nT14/xdzcHHXr1lV2teHp6Yldu3bxnrXb2NhAX18f3bp1kzi/wotzCSHkZ5SWloaFCxfy7vkODg4S\nTbRu0qRJseNk8thEoaiSAmsPHz4cfn5+AL4vrD18+HCpY2BPnz4t9jNDQ0Ncv36d+24+fvwIbW1t\n6SsvocaNG+Ply5cYOXIkxo0bh759+4JhGJw/f16s80ubSyG4rpImrJd0buEyaN4GIaQiqVevHuLi\n4tC9e3ekp6eDYRicO3cO1tbW2Lt3b5nXZ8OGDVi4cCHy8/N57zdv3hxHjhwpMRhE165dcfr0aQwY\nMIALnscwDBISEtCrVy/ExsZyG1gQQggpW4XX3gBAu3btsHnzZvTs2VMh5Z05cwbHjx/nfuMbGhqi\nY8eOcsu/UaNGsLS05MbmcnJyMG/ePJEbjn7+/FlofHHJkiVyqwshhPzsNm3aBOC/sZ3Cz/ElkZKS\nwo3pFA24owyCdmPChAlSnd+4cWM8fvwYAJCamor09HRee1S3bl3MmjVLnlUmhFQAKsquwI+gWrVq\nIt8vLcKzYKCqYcOG3Hvh4eHIz88X678BAwbw8jp58qRY52VnZ0t0fYLAC4KGtVevXhKd//XrV+61\nlpaWROeWF0lJSfDx8eF14nx8fFC1alWJ81q/fj03ICnIb9GiRVxUpOL07NmTe0ApqIObm5vcdkkk\nhPxc3r9/j5SUFN57rVq1UlJtys79+/e517RzAiGEyM7NzQ3t27fnfp9u2bIFQ4YMEetcQX9Ilv/k\nmZ+qqirq16/P5ceyLGrVqoXo6GjUqVNHrt8bIYSQH1fv3r3RoEEDNGzYEA0bNkSjRo3w22+/YcqU\nKbh27RrMzc3lUk5MTAxSU1N5740ZM0YueRcmagdYHR0duZdDCCE/IoZh8Pr1a6ioqEj8n2DXcOB7\n36RDhw4S56GhoVHiwiJFSEtLw9KlS3nl9u3bF507d+alc3Z25r4jlmXh6emJBw8e/J+9O4/LKfvj\nAP65rURESWFIdilhrNmyZ4mxR1RjHZEY+1hTQpYGWWfG2CK0aIzs6y+yr2XfGWuRivbn94fXc3V7\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XEdH3oly5ct/M/TKvBzk8e/YMHTt2xJs3bwB8adt69+6N0aNHf/X8ChUqYMmSJRg1apTY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\nAdHR0ZIFLGJiYjB9+nSsXr0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27RtnfUnRttr73SRm4qXo/9cKFiyo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7/boEZchcX/4sULvH79mh04dOvWrcjrvH79OtLT09myXbp0AQDMmjULs2bNEjuexYsXw83NjY3N\n1dUVCxYsELseQgj5GwwfPrysQ5Apf39/zkSNoG8pOHElOC6YgEFVVRVNmzZFy5YtoaenB319fbRs\n2ZJdBFEUd3d39OnTB1lZWSJNXObfNYphGKioqODgwYO/3QM3QgiRtwYNGgDIu186OztLtJOCYKFw\naZkwYQL27t0r8fkxMTGwtLRkX9AVJF3w9vaWOOmCgKOjIx4/foyTJ0+CYRhkZWVh6NChWL9+faEv\nDhNCCMlz+/ZtDB48mN1ljmEYWFtbY/v27YWWd3R0REREBO7fv4/u3bujR48ebBK1Vq1a4fjx4zKL\n7cGDBxg6dKhY55TXucbyGjchhJS1tLQ07Ny5k3Mf1dLSEpr3s7CwQN26ddmXjLKysuDi4sIutC6P\n9PT0APzqQ+7fv1+W4RBCSLnC5/Mxf/58eHh4CC3WXblyJWxsbAo9r7S+t8tyQfLy5cuRlpYGIC/+\nZcuWsckjBNeso6ODly9fsmX8/PyQnJyMwMBAaGlpySwWQgghefT09Njv84Xx9PRkf9bR0RFp3dfr\n169FavvZs2ec49JKvKCrq8s5fv78eaHlrl+/jqVLl3KSLkycOFGqF4gIIYTIX3JyMhITE9kxU4MG\nDVCxYsUyjkrY0qVLsX37duTk5IDP58PHxwerV68us6RzhBBC/g6pqanw9vbmjHOk2aDJyckJ/v7+\neP/+PVunra0tOnfujH///VeqWKtUqYIqVapIVUd+5XEdRFlsnltYHYRIixIvEEIIIeVY7969/8hs\nXOHh4QB+LVTo3bt3kWVPnz7NKdu9e/dSiZEQQsifp23btgB+7bxUMMGCAMMwaNiwIfT19dl/enp6\naNy4MRQUFMRut0uXLvjf//6HVatWISwsrMSdiATxqKmpwcrKCvb29mjRooXY7RJCSHk3fPhweHh4\nwMDAANOmTZOqrvLwkGL79u1YsGABsrOzAYBNurBjxw5MmjRJ6voZhsGRI0fw5csXREREsP3hkiVL\ncO3aNfj4+EBbW1vqdggh5E9y8OBB2NjY4OfPn+zc1MCBA3HgwAFOuVOnTsHNzQ07duyAgYEB/P39\nYW1tDX9/f/j4+LDl1NTU0KRJE5nF9/37d7HKh4SEyKxtQggh5cO2bdvw5csXzmK1cePGQVVVlVNO\nQUEBM2bMwPLly9my3t7eWLRoUam9aCRr+vr6nGPBruyEEEJKNnLkSPj5+QklXbCxsYGjo2MZRyc7\nt2/fxsGDB9m+r0OHDuxGEPn5+/tjxowZOH/+PJvUOzIyEqampggLC0OtWrXKIHpCCPl7paSksD/r\n6OjItO4nT56w/YK2tnapPTdp1KgRFBUVwePxwOfz8eTJE6Ey7969w+DBgznrDQwMDLBt27ZSiZEQ\nQojkLly4wP7MMAyMjY3LMJqi1ahRAyNHjsShQ4cAAJ8/f2afeRFCCPk9CObpVqxYgQ4dOoh8Ho/H\ng5WVFTvf179/f8yYMUOstlesWIG7d++KdY4o1q1bh69fv7KxtWvXDu3bt5e4vkqVKsHd3R0jRoxg\n60xNTcW4ceMQEREhk5ilxefzoampiaioKJkmE3B2doa/v7/M6pOVwjZILA/rOcmfjRIvEEIIIaRQ\nt27dQq9evdgvrDVr1sS9e/dKpW1B4gVB27t370bfvn1Rr149obJBQUGcBYE9evQolRgJIYT8eYyM\njNifBX2QtrY29PX10apVK06ShQoVKsi07datWyMoKAg5OTmIi4vDp0+fkJGRAR6PxynHMAxUVVVR\nvXp1NG3aFEpKNKwnhPy9OnXqhLp162Lr1q0ymWiX54S9NHWnp6dj6tSpOH78ODv2AQAVFRUcOHAA\nI0aMkFmcKioqOH36NAYOHMgZlwUGBuJ///sfNmzYgHHjxsmsPUIIKc++fv0KR0dHoaQLx48f5yRk\ne/PmDaZOnYovX77AyMgICxcuhIuLC6Kjo4USt2VmZoq8u54oEhMTZVYXIYSQP8+PHz/g7u4uNFYp\nKrHdlClTsHr1ajYZXFZWFlauXCmUcEie3r59K1Hi06LkH2NdvXpVZnVHRUWhU6dOMqmLEEJ+R3p6\nevDz8wPwa95r3rx5cHNzK/IchmHQs2dP9OrVS66x5eTkYNmyZTKpa/78+WyiboZhiqxXTU0NQUFB\nGD58OLt+gWEYPHr0CGZmZoiIiCh0rQMhhBDZy8jIwPfv39l7sYaGhszq/vr1K168eMHW3bJlS5nV\nXRJlZWXo6uri2bNnAPLmHNPS0tjry8zMxMCBA5GUlMSOczQ1NeHn5yeUWI8QQkjpcHZ2RuXKlUUq\nGxUVBeDX+Orly5eYP3++RO2+fPmSM+cla0OHDsWhQ4fYOcXQ0FBYW1tj7dq1+Pr1q8j1FCz78OFD\nLF68WOx4pk2bhsaNG4t9HiGE/OmMjIzQr18/kcsX3Ay2fv36Yp0PAFu2bBGrvCjevXsHT09PzvtC\nixYtkrreYcOG4cCBAzh37hxb9+XLl7Fy5Uo4OzvLIHLpKSoqCiURl5a0yQlDQ0PRrFmzQuc6W7Ro\ngTFjxohdZ2xsLJvkEPj1fUhPTw+tWrUSuz5K2EBkhd7QIIQQQkih7t69i5SUFPaLZ2kuEBs1ahTO\nnTuHjIwMMAyD27dvw8jICL6+vujWrRtbLjY2Fk+fPmUfaBkbG6N69eqlFichhJA/S/PmzTFmzBi0\naNECrVq1goGBAWrXrl2qMSgpKaFFixal2iYhhJRn+/fvR+fOnaWuRzBhv2XLFjRq1EgGkeX59u0b\nrK2tJZ7QDw0NhY2NDV69esXWwTAMqlSpAl9fX3Tv3l1msQqoq6vj7NmzGDduHHx9fdk2P3z4gAkT\nJsDT0xMODg6wtLSUeduEEFKeaGlp4erVqzA3N8eTJ08wYcIE+Pj4cO75PB4P1tbW7O4Lubm57ELn\nwl7svH37Nho0aCDTOOW5uI4QQkj5tmHDBiQnJ3MWq/Xp0wfNmzcvtHzVqlUxZswY7N27lz3nyJEj\nsLGxEWvXJGnIerFU/j7yd0/oRwghvxM7OzucOnUKd+/eBcMwsLOzK3ZBsuD+2KlTJyxYsECusWVm\nZsok8cKOHTsQGRnJ3tdbt25d7HyYsrIy/Pz8MGzYME7yhZcvX8LMzAwXLlygl4EIIaQUPHnyhP2Z\nz+fj+vXrcHR0xKpVq6SuOzo6mnOcmJiIqVOnAshLwuPp6Sl1G8XR19dHXFwcgLxru337NszMzMDn\n8zFmzBjcvHmTHaspKChg//79Mn3mRQghRDSCe/H+/fslriM6Olqo3xE3Bnnp1q0bFBUVwePxwOfz\n2aQRO3fuxLt378SuTzA/9+zZs2KT+RVGkOCPxlqEEPLnWrFiBX7+/Mn2bXp6ehg6dKhM6vb29oaB\ngQG+fPnC9t8uLi5o3Lgxxo8fL5M2/jSLFi3C48ePYWVlhblz56JLly7s7/r27Yu+ffuKVV98fDyM\njIw4310E/y3evXuHgIAANGzYUGbxEyIOSrxACCGEkELdunULwK9FECYmJqXW9qBBg3Dp0iVYWVmx\ni/4+f/6M3r17w9XVFXPnzgUA7Nu3jxPj4MGDSy1GQgghfx4FBQUcPHiwrMMghBAihq5du8q0PlNT\nU7Rp06bI32dnZyMlJYU9rlSpEipUqFBk+c+fP0sUx+fPnzFv3jwcOXKEXaQtWHDQvHlznD59Grq6\nuhLVLQplZWUcO3YMrVu3xooVKzg7+928eRMDBgxA06ZNMXXqVIwZMwbVqlWTWyyEEPI7q1GjBiIi\nIrB//34sXbpU6PcrV67EtWvX2Pt4mzZtYG9vX2R99KImIYSQ0pKYmIhNmzYJ9T1OTk7Fnmdvb49D\nhw4hJycHQN7zmdmzZyMmJqZc9mP5d8+hpAmEECI6JSUl7Ny5EyYmJnB0dMSKFSvKOiSZio+Px5Il\nSzjJiVxdXUs8T0lJCb6+vhg6dCiCg4PZeb23b9+ic+fOCA8PL9Xd0Qkh5G/09OlT9mfBfdzR0RFp\naWki3cuLc+HCBfZnPp+P+Ph4PH/+HACgoaEh98QLbdu2xcmTJ9nj6OhomJmZYdq0aTh58iSn33Jx\nccGgQYPkGg8hhJCiiZMUu+Duzr97Mm0NDQ3Uq1cPL1++BAAkJCSAx+OVcVSEEEL+RJcvX8ahQ4c4\nYx1HR0eZ1V+rVi34+Phg8ODBnPV506dPR7169WS+LlFcfD4fycnJMq3zx48fEp+bmJiIhw8fgmEY\nBAYGIjAwENu2bYONjY3EsQwZMgSpqamc70CC70YpKSmwsrJCdHQ0NDQ0JI6bEElR4gU54fP5sLS0\nlOrB/J07dzjHQUFBePbsmcT1KSoqIjAwUOLzCSGE/F0uXbrE+QJb2gMHIyMjREdHw8LCgv2Cnpub\ni/nz5+P27dvYsGEDDh48yMaoqKiI0aNHl2qMhBBCCCGEkL9LZGQkevbsyR57eXlh5syZMqv/x48f\n2Lp1KzZu3Mjujg78WmShoqKCx48fl/qODQWTPzAMg7i4OCxatAhLlixBx44d0b9/f5iZmcHY2BhK\nSjTtTAj5e1SvXr3QpAsHDx7E+vXr2funmpoaDh06BEVFxSLrMjY2xrVr12QW282bN9GuXTuJz79w\n4QKn3/udnDlzBv369SvrMAghpNxatGgRMjIyOIvVBgwYUGwiOgCoV68epk6diu3bt7Pn3rp1C66u\nrliyZInc41ZVVYWpqanM6nv//j0ePHjAXouGhgaMjY2lrldLS0sG0RFCyO/N2NgYV65cQceOHcs6\nFJni8/kYP348MjIyAOTNg/Xp0wfdu3cX6XxlZWX4+/vDysoKoaGh7LxacnIyunbtirCwMBgaGsrz\nEggh5K92/fp19uf8zzTc3NyQlpaGHTt2FHt+cQnZAgMDOWOopk2b4smTJyXGJKsEbx06dODUd/ny\nZXz8+BF79uzhxDV16lQsXrxYJm0SQgiRnDj3f0GfVV4Sg9arVw8vXrwAAOTm5uL9+/cAxO/z8ieZ\nkOS6y8PfihBCiGSysrIwffp0zmeGhoYYOHCgTNsZOHAgpk+fjl27drHzeFlZWRgyZAiuXbuGpk2b\nyrQ9UTEMg9TUVNSsWVMudUuS6CksLEyoHkmf2fH5fIwaNQr37t3jjK8FfXu1atXw4cMHPH78GKNH\nj0ZQUJBE7RAiDVoBKyc8Hg/nzp2TWdY5Pp+Ply9fspnhJCHKgmcnJyc8evSoyN/nn5QEgO3btyMk\nJETsWB48eMA59vLyQnBwsNj1FGXmzJkwMzOTWX2EEPI7mz17NrZt2ybXNvh8PvvwRtbGjBlT5O7i\ndevWxdWrVzF48GBERESwk4oHDx5EcHAw0tPTAeR9ae/bty9q1KghlxgJIYQQQgghBAAyMzMB/HqA\nr6qqKrO6vb294ejoiKSkJKFEB+rq6tiwYQNu3LjBJqArTcOHD4eBgQHWr1+PtLQ0zmJFPp+Pq1ev\n4urVq6hduzbu3r0LbW3tUo2PEEJ+N5cvX8a0adM4C57d3NzQvHnzsg5NIrRwjRBC/iynT5/G8ePH\nOfd3cXYJWrlyJfbt24efP3+yfd3q1athZWWFZs2ayStsAEDVqlWFFnZJ4+HDh9DX12ePs7OzERAQ\ngIoVK8qsDUII+ZP9aUkXAMDV1RXXrl3jbACxceNGsepQVlbGqVOn0KdPH1y5coXtc798+YL79+9T\n4gVCCJGjy5cvsz8zDAM1NTX8+PEDDMNg165dyMzMZBMVFKaoz69fv474+Hj2+Y2+vj7++eefYhMv\nNGnSBFOmTGGPGzVqJOFV5enQoQNUVFSQnZ0NADh//jxnnTrDMOjZsye2b98uVTuEEEKkI7gnP3/+\nHA0aNCi27KtXr9C0aVPk5OSw592/fx8tWrSQuH0zMzNcvXpV4vNFUalSJc7xjx8/kJCQIFYd8fHx\naNy4Mdv3DhgwAKdOnZJZjIQQQsq3tWvXIi4ujjPe2bx5s1za2rJlC65fv447d+6wY76vX7+if//+\nuHLlilySH5RHoaGhnON///2X84xNHHPnzsXp06fBMAw6duyISZMmYcqUKex/62HDhuHx48eIiIjA\nmTNnYGdnBxcXF1lcBiEio8QLciaLpAulKSIigjPxWJj8E4sxMTGIiYmRqk0+n4/o6GhER0dLVY+A\n4OVbSrxACPnbyGMBtOCLa1lmUdXU1ERISAgmTJiAY8eOsbEIdn8V9LXz588vk/gIIYT8+V6+fFlk\nkqCyoK6uTjtUEEJIGUlJSQHwa6xUcEGBNF69esUmXcif2KBNmzY4cuQImjRpgokTJxY7Nst/niwI\nrrNJkyaws7PD5MmTsXz5chw4cAA8Ho8z96mgoIBDhw5R0gVCyF/v6dOnGDJkCLKzs9n76IgRI2Bj\nY1PiuT9//sTTp09lFsurV69kVpeo84Pi7E4kyU5G5WW3J0II+V2lpqbCxsaGvZcK7qujRo0SeXFU\njRo1YGtrC1dXV7aezMxMjBgxAjExMTJNUCdvLVu2RN26ddmF4ZmZmThz5gxGjBhRxpERQggpC9eu\nXYODgwNnjcSECRPQsmVLseuqUKECzpw5g86dO+Pu3btQUFDAzp07MW7cODlETgghBAA+f/6M+/fv\ns/fxxo0bw9PTExYWFsjNzQXDMNi/fz8yMzNx6NAhKCgosOdWq1YN/v7+7HHBF2W3bt0K4NcYysrK\nCv/73/+Kjadjx44yTVKkpqYGExMTXLx4kdNXCf63U6dOOHXqFOe6CCGElJ4KFSpAQ0MDANiXNkvi\n4OCA7Oxstry5ublUSReAvHVl+eOQR78g2DRPoLy9M0UIIX+Dnz9/Ct2vi5OTk8M5zs7OFut8Pp+P\n3NxckcsXJzo6Gi4uLpzxzogRI9C5c2eZ1F+QmpoagoKCYGRkhI8fP7J9+IsXL2BmZoYLFy6gXr16\ncmm7OL/Tugg+n4/w8HDOf5O+fftKVNfy5cvh5eUFIC+Z05EjR3Dp0iUAv66ZYRjs3bsXenp6SE9P\nx8aNG1GhQgU4ODjI5HoIEQUlXpAjWdzgJFl0VhRZLrgmhBDye5H1l+qCu5eWdfIFJSUlHD58GLVr\n18amTZuE4mzWrBm6du1aJrERQgj587148ULkHf9KQ9WqVSnxAiGElJHk5GTO8T///COzuteuXYtb\nt24hLCwMDMNAXV0dK1euxKJFi6CoqAgAxb4M5ePjw9lZafLkyRLvNnv06FHcunWLPRY8PKpWrRp8\nfHywaNEibNu2DYcPH8a3b98AAIsXL6ZxGSHkr/fkyROYm5vjy5cv7GeNGzfG7t27SzyXz+fjzp07\naN68uTxDlJhgVwFra+siy2zZsoVN7s0wDI4fP17ky7cfPnzAtGnT2PlGCwsLTJ48uci6jx07hhMn\nTkhxBYQQQgBg4sSJbMI3gSpVqsDNzU2seuzt7XH8+HEkJCSwz5AePHgAW1tbkfq934mlpSW2bdvG\n/k0OHz5MiRcIIUTGBPfYuLg4nD17Vq5tZWVlSXTemzdvMHjwYHYXcQDQ0dHB+vXrJY5FsMlEp06d\nsHjxYs6u54QQQmTv1KlT4PF47MurlpaW6NWrF/bs2YMJEyYAyOuTjh07Bh6Ph8OHD7PPXypUqIDB\ngwcXWu+zZ8/g5+fHjn0UFBQwZcqUEhMvyMOAAQNw8eJF9liwnq99+/YICQmBurp6qcdECCEkz7lz\n58Qqf+/ePRw9epSzPnvOnDkAgD179rDlKlasiJEjR4pcb8HdqGWNz+dzEh0BeesICtq0aRNGjRqF\n2rVrS9Xex48fZbougxBC/nSC+7M4fUf+cwV27dqFXbt2Sdy+pNLS0jB69GhOEoeKFSty3iGShzp1\n6uDkyZMwNzdnk1AwDMMmXwgPD0eTJk3kGkN+fD4fWlpa7CZRsmJjYyPRf9fY2Fh8/vyZ8/+R/v37\ni12Pg4MDO9/KMAx27txZZFKLunXrwtXVlU0ov3r1amRlZcHZ2VnsdgmRBCVekBNFRUWpM/WMHTsW\nR44cAZB3M5k7dy42b94si/CKVVov1coyqQQhhPzNBg8eLJRlWxrnzp1DRESEUNIFIK9/W758uUx3\ndRUnO+uGDRtQoUIFrFmzhn1Ixufz8eTJEyxbtkyqRQ+EEEIIIYQQUpK4uDjOsSyzWTMMg3379kFP\nTw9dunTB1q1bUadOHU6ZXr16oVevXoWe7+XlxRm/LVu2DA0bNpQoloKLMerWrcs5btasGTw9PbFh\nwwY267STk5NEbRFCyJ/i0aNHMDc3x4cPHwD8WvTcr18/VKxYsdhzy8szkiZNmsDKyqrI3/v5+XGO\n+/fvX+Ri79evX3OOdXV1i6379u3bYkRKCCGkMG5ubggMDGT7HUFftWnTpkIXRxdHQ0MD27dvh6Wl\nJYBfC+n27t2LVq1awdbWVubxy8vIkSOxbds2AHl/k9DQUCQmJqJWrVoi1xEUFAQVFRX06dOn3PTr\nhBBS2vh8Po4ePYqjR4+WdShCMjIyYGVlhY8fPwL41Ue6u7tDR0dHqrpr1KiBe/fusTvOEkIIkR9f\nX18Av+7jgvHK2LFj8f79eyxdupRdb3bixAnweDwcPXqUTb5QlBUrViA3N5c9t2/fvkLPTUTx6NEj\naGtro0aNGuJfHICUlBQEBASwx4LnQW3atMH58+dL7GuSkpKwYcMGTJs2TWi9Ho1jCCGkdOXk5GD8\n+PGc92lat24NCwsLAMDUqVPZz//991+JXp4tKDIyElWqVClyowdR7dmzB8nJyWzfUbVqVaE15WfP\nnsWSJUvg4OAAW1tb2NnZoUqVKmK3deLECUyaNAlbt26lRHaEECImSb7jF0yYUBbjBBsbG7x8+ZLz\nLpOTk5NYz2wkZWpqil27dmHSpEnstTMMg7dv36Jz584ICwuDgYGB3OP4HRVcS6iqqooePXqIVcec\nOXPY9Y0AMGnSpBIToU+fPh2BgYEKpGXtAAAgAElEQVQIDQ0FwzBYt24dMjMz4erqKt4FECIBSrxA\nOPJnQpWnnj174sKFCwDyOqHDhw9j1KhRpdI2IYT8abp164Zu3brJpK7k5GSsXbuWM1Dp2rUrLl68\nCIZhkJubi7t373Ie4pQmPp+PiIgIzrEg1o0bNyIuLg6HDx+m7N2EEEJk7ndaaPA7xUIIIX+bO3fu\nsD+rqKigfv36Mq2/Zs2aePjwodiL7j5//oxXr16xfUSlSpUkTroAAG/fvuUkcSgqwYS6ujqmTp3K\nWfhBCCF/o7t376J37974+PGj2Ls3LFy4EAsXLgSQl4zA0tISDx48AMMwMDExwcmTJyXeyefbt2+4\ndOkSe6yioiJRPYQQQsq/ixcvws7OjpN0AQC6d+/O7vwqrv79+2PEiBE4ceIEJ1n2ggUL0LBhQ/Tr\n109W4cuViYkJ6tati4SEBABAbm4utm3bhrVr14pcR0BAAA4ePIg6depgxIgRsLGxkWnSdEIIIfI1\nZswY3Lt3jzMf1qdPH1hbW8ukfkq6QAgh8vfmzRtcunSJvZdXrlwZZmZm7O8XL16MpKQkuLu7s+MX\nPz8/8Pl8HD9+HAoKCoXWe/78efj7+3PW0i1fvlyiGE+ePAlnZ2cMGDAAkyZNQq9evYpst6BHjx7B\n0tKSfQFJQLCpX+XKlUusIy4uDh4eHvDw8ICRkRHGjx+PWbNmwcnJCY6Ojmw5UWMihBAiOXt7e9y9\ne5fTv3h6enLKSLtjeEG+vr7Yvn072rZti0mTJmH06NGFbsIn6Gc2btwIDQ0NNG/eHGpqauDz+bhz\n5w6OHz/Oibtv376c83k8HpYsWQKGYZCZmQk3NzdUr16dfRYmKmdnZ6xatQpA3kuXubm5mD59uoRX\nTwghfxdJ1xiX9dpkT09PHDlyhNMHdunSBXPnzi21GCZMmIDPnz9j8eLFnOQLHz58QLdu3RAUFART\nU9NSi+d3ERYWxv7MMAy6deuGChUqiHRuZmYmJkyYwD5PBIAePXpg586dIp1/7NgxdOjQAXFxcWAY\nBm5ubvj27Ru8vLygrKws/sUQIiJKvEB+C7IcFBJCCJEMj8fD5MmT8eXLF/YLbcuWLREWFgZzc3NE\nRkYCAE6fPo2lS5diw4YNpR7junXrEBUVxZm0E/wvAAQGBsLMzAynT59G7dq1Sz0+QgghfyZzc3Pk\n5uaWdRiEEELKWFpaGm7fvs2OPwwNDeXywEmSnY5Onz7N/swwDAwNDaWK4c2bN5xjSXZuIoSQv0Vg\nYCDGjRuH9PR0dp6KYRjweDyx6rly5QqGDBmCT58+gWEYTJkyBdu2bYOSkuSP8ipVqgQrKyuJzyeE\nEPJnuH//PgYPHiw0v6WpqQlvb2+p6vbw8EB4eDg+f/7M9oE5OTkYPnw4zp49iy5dukhVf2mZMmUK\nHBwc2OdOO3fuxPLly1GxYkWRzn/9+jUYhkFCQgLc3NzQunVrSrxACCGFKK3F2+KsQ8vJyUFsbCwn\nNg0NDezYsUMeoRUq/yJuQgghkvH09ERubi47LrGyshJKILB582a8fv0aAQEBbDl/f39YW1vj6NGj\nQuVTUlIwffp0zvq0AQMGoEOHDhLF+P37d2RnZ8Pf3x/+/v5YvXo1HBwcSjzv5MmTmDx5Mr5//y70\nEi6fz4enpyfGjBlTYj0vX74EkNffxMbGQlVVFbNmzQJAyRYIIaQ0BQQEwNXVldO/jBo1Cp06dZJr\nu4LnTzdv3sStW7dQo0YNDBo0SKicIKYGDRpg69atQr8vmABo5syZnN/v3r0bjx8/Zq+vUaNGmDNn\njlixZmRkcDYI5PP5mDlzJng8HmxsbMSqixBC/jaC+/jChQthZGRUqm07OzvjwYMHEp176dIlLFy4\nkNPPaGpqYv/+/TKKTnQLFy7E169f2QTdgvHjly9f0L17d6xfvx4LFiyQawwMw+Dr169yGauJm9zp\n+/fviI6O5nx3sbCwEOncpKQkDBw4EDdu3GDPb926NU6ePAlFRUWR6tDS0sLZs2fRoUMH9nnk7t27\nce/ePfj5+eHff/8V+VoIEQclXiCEEEIIAGDy5Mk4d+4c5wvxli1boKioCB8fHxgaGiIjIwMA4Orq\nip8/fxY6qSYv165dw5o1azjxTZ48GQEBAZwFfbdv30b79u0RFBRU6oNFQgghhBBCyJ8rICAAOTk5\n7JgkKSkJQUFBGDBgQFmHhuPHjwP49fBMmp1lU1JSkJ6ezj7IqlatGlRVVWUSJyGE/GnWrVsHe3t7\nAL/uwSNHjkR6ejonKU5x+Hw+nJyc4OTkBB6Px/Yzu3fvxu7du2UW64QJE7B3716Z1UcIIaR8ePPm\nDfr27Ytv376xnwn6rP3790udHOCff/7BoUOHYGFhwS7SYhgGGRkZsLKywpkzZzi7zP6upk6dCicn\nJ+Tk5AAAvn79CldXV6xevVqk8+Pj4znHenp6sg6REELKPYZhMGvWLNja2sq1naysLBgYGIhcXklJ\nCba2tli2bBkAsDvNllYi0pCQkFJphxBC/mTp6enYu3cvZ01ZUS9lHjx4ECYmJrh37x671szX1xcK\nCgo4evQoW47P52Ps2LFISEhgn5eoqKjAxcVF4jgF4zJBfdWrVy+2fGZmJubNm4ddu3ax5+TfoEhw\nHBsbi8jISHTu3LnY+l68eMH+zDAMWrVqJdF1EEIIkdy1a9cwZswYzsuONWrUwObNm+Xe9qdPnzjt\nlrS5nbm5OTw8PDif8fl8zhzg4sWL0b59e/b3Hz58wPLlyzl98qZNm8TejVpdXR1hYWEwNTXF06dP\n2fpmzZrFJmEghBAiLP9YwdTUtNQ3adi9ezcePnwoFEtJXr16heHDh7MJxAV9iIeHR5ltFuTk5ISM\njAy4u7tzNuDIzc3FokWLcPXqVezbtw+VKlUqk/hKU1hYGLtmUsDS0rLE86KiojBq1Ci8e/eO7cvr\n16+PkJAQaGhoiBWDrq4uAgIC0KNHD2RlZYFhGFy/fh2GhoY4evQoevbsKfZ1EVISSlFJCCGE/OV+\n/vyJcePG4cCBA5zJrvnz58Pc3BwA0KhRIxw7dgwKCgrs7z09PTFkyBB8/fpV7jHGx8dj4MCB7II3\nIG9Sz9vbG5cvX0bt2rU5k3lJSUno0qUL/P395R4bIYQQQggh5O/g6enJOX79+jUGDRqEdu3a4fz5\n82UUFXDr1i38999/nIcbhe1MISrBjkcA2J0sCCGECEtKSsL27dsB/HrwP2PGDBw5ckTkBWRPnz5F\n586dsXr1avB4PM78lqz/EUII+fu8fPkSXbp0QVJSEvuZoM+ys7OTatyQX58+fbB69WrOwm2GYfD9\n+3f07t0bQUFBMmlHnqpXr46xY8eyfx8+n4/NmzcjMTGxxHNTU1Px9u1b9lhJSQnNmjWTZ7iEEFLu\nCPqIqlWrokmTJnL917hxY7Hjmzx5MlRVVcEwDMaOHYvx48fL+k9ACCFEjtzc3PDlyxf22MjICMbG\nxoWWrVixIoKCglC1alX25VEFBQWh8osWLUJISAhnLd3q1avRtGlTieMUrLET9IvF7cp5584dGBkZ\nFZp0YcGCBVi8eDEnCcPSpUtLbF+QME7QPiVeIISQ0nXjxg1YWVnh58+fAPLux0pKSjhx4gSqVasm\n9/Y/fPjAeV5UUuIFbW1tNGnShPOZ4JlT5cqV4e7uLpSQaN68eZw+uVevXhK/9Kujo4Pw8HDUrVuX\n0+fNnj0bO3bskKhOQgj5k/Xu3Rtbtmxh/+nr65d6DFu3bsWVK1dw5coVREZGQlNTs8RzUlJS0KdP\nH3z+/BnAr3HP5MmTy3yOzs3NjZNQSIBhGAQEBKBt27a4d++e3NqXx7oRSdaO+Pr6co4NDAyKHc8C\ngIuLC7p164bExET271ezZk2cP3++xCSERTE1NcX+/fuhpKTE/v/k8+fP6Nu3L1atWoXs7GyJ6iWk\nKEplHQAhhBBCys7z588xbNgw3L17l/OgyNzcHK6urpyyFhYW8PT0ZDOGCgYMMTEx2Lt3r9yyhH35\n8gX9+vXjDKaqVq2KAwcOAABatGiBqKgo9OzZE8+fP2cHBD9+/MD06dNhbm6OKlWqyCU2QgghhBBC\nyN/h6NGjiI2N5YybBP978+ZN9OvXDx07dsT69etLfTfXgovpDA0NoaurK3F9+RMvAED9+vUlrosQ\nQv5kNWvWRGRkJExMTPDx40e4uLhg8eLFIp2bnZ0NFxcXuLi4IDMzEwzDQFtbG6mpqWzi0fr162P3\n7t2oU6eOWHE9fPgQdnZ2iIuLYz+rXr06Jk6cKFY9hBBCyrfnz5+je/fuePfuHfuZYAzTp08fODs7\ny7S9lStX4saNGwgODmYXbTEMg8zMTAwdOhQbN27E/PnzZdpmSfh8PkJDQ5Gbm4v+/fuXWH7FihU4\nePAgu6NSeno6Zs6cicDAwGLPK7ioTl9fHyoqKpIHTgghpNTp6Ohg6NChuHnzJptgjxBCSPnw+fNn\nbN68mfPcZs6cOcWeU69ePZw8eRI9evQAn8/H7t27OS/0uLu7Y8uWLZwXUoyMjESe+ytK/qR4QOGJ\nFzIzM+Ho6IhNmzYhNzeX84KPqqoqvL29MXbsWHz+/Bnbt29Heno6+Hw+YmJisGPHDtjY2BTZ/sOH\nDzn1tW3bVqrrIYQQIrqQkBAMHz4cGRkZAH7N07m4uMDU1LRUYhAk4AEABQUF1KxZs8Rz3NzcEBMT\ng+/fv4NhGOjo6KB169YwNzcXmv8KCQnB8ePH2b6mQoUKUo+vateujf/++w+mpqb4+PEjW/fs2bOh\nrq5e5i/kEkLI78TIyAhGRkZlGkOLFi3EKv/jxw/069cPcXFxQuMvLy8vWYcnEScnJ2hqasLOzo6T\nCAgAXrx4gcjISBgYGMi8XT6fj8qVK+PFixecpA/SWrhwIfseligyMjJw9uxZzpjb0tKyyPKvXr3C\nxIkTcfnyZc74s2HDhvjvv/+k3gBq5MiRUFVVhbW1NbvWhs/nw8nJCSdOnICnp6fc3msjfx9KvEAI\nIYT8hTIyMrBu3Tps3ryZ84WTYRh07NgRfn5+hWYzmzFjBpSVlWFjY4OcnBwwDIPExET07t0b/fv3\nx9q1a2U6cEhJSUHPnj3x7NkzNkYlJSUcPXqUM+lXr149REVFoVevXrh//z47aXf69GlKukAIIYQQ\nQggpkra2Nho1agQg76UgNTU1oTIPHjyAra0tZ9w0evRoXLp0iX2JiWEY/O9//0OXLl1gaWmJDRs2\noFmzZlBTU8OUKVPYukxMTGQav6urKy5cuMCJzc7OTqo6nz17xjmW9oEHIYT8yRo2bIhz584hISFB\n5B17jh8/jhUrVrCJbgTzcb6+vrh79y5GjhyJ79+/49WrVxgxYgQ8PDxgbW1dYr2fPn2Cvb09fHx8\n2BdGGYZBt27dcOTIEYl3DSCEEFL+REVFYfDgwWxCa+DXjqZ6eno4cuSIXNo9dOgQTE1N8eDBA84z\nJh6Ph4ULFyI2Nhbe3t6oWLGiXNoXePPmDfbs2YP9+/cjISEB69evFynxQoMGDTB9+nRs27aNHWMF\nBwdjz549mDx5cpHnXblyhf2ZYRi0b99eJtdBCCFEMpIuRLa1tUXFihWhrq4u44j+LILdeQkh5Hdh\nb2+Pb9++sWOQatWqYfjw4SWeZ2Zmhh07dkBLSwuDBw9mP9+xYwcWLFjA1sfn86GjowM/Pz8oKChI\nFWtSUhLnxZOCz1/CwsIwZ84czktHgmc/DRo0gK+vL9q0aQMgL2nQwoUL4ejoyNa5fPlydOvWDc2a\nNRNqm8fj4enTp+yxioqKRDvg8ng8sc8hhJC/naenJxYuXMg+uxHc2+fOnYuFCxeWSgzv3r3Djx8/\n2P6lXr16IvVr/fr1Q79+/Uos9+nTJ0yaNImzbmHlypUyWWvQuHFjnD9/Hl27dmUTQPB4PEyZMgUV\nKlQQqd8nhBDye5o5cyZiYmI445/q1avj1KlTUiW4/vr1K+dY2rHckiVLUK1aNcyYMQNZWVkA8p4H\nOTk5wdbWVqq6i8MwjMzfhSpsbWRxzpw5g4yMDM5zv6LWxuzatQtLlixh+2vBdwI9PT2EhYXJbM3K\noEGDcObMGQwaNAhpaWkA8v5WcXFx6N27N4YMGYItW7YUmuyQEHFId+cghBBCSLmSmpqKTZs2oUmT\nJli3bh0yMzMB/JrI69u3L8LDw1G5cuUi65g8eTJOnz4NTU1Nzrlnz56FoaEhBg0ahJCQEKkzq334\n8AFdu3bF7du3OV+8t23bhh49egiVr1atGi5fvgxjY2MoKSnhxIkTEr3U9OnTJ86xoqKixNdACCGE\nEEII+b3Z2NggLi4OcXFxePr0qVDm7djYWJibmyM1NZX9rEWLFti3bx+ePHmCpUuXQlVVlf0dwzAI\nDg6Gvr4+pk+fju/fv8Pb25v9J8sdF06cOIHly5dzHmy0aNECQ4YMkare+/fvA0CRC/8IIYRwGRoa\nipx0QZBE4dWrVwDy+o3Zs2fj8uXLqFWrFvr27YsrV67g33//BcMwSElJwZgxY9CjRw/cunWr0DqT\nkpLg4OCARo0aYdeuXexOeOrq6ti6dSvCw8Mp6QIhhPxFDh48iB49ehSadKFly5a4cOECtLS05NJ2\npUqVEB4ejubNmws9I2IYBseOHYOBgQGioqJk3nZWVhb8/PzQp08fNGzYEE5OTkhISADDMGIlenBy\nckLVqlXZmAU76MXGxhZ5Tnh4OIBff+cuXbpIcSWEEEKkJUiUml9hm04UZGxsDD09PXmEJFMpKSki\nXY+8FPz7KinRvl+EkLJz7do17Nq1i7OuzNHREcrKyiKdP2nSJE7ShU2bNrGJuIG87/iKioo4duwY\n6tatK3R+/vvxjx8/kJOTU2Rbubm5nHuolpYWKlWqBAB4/Pgx+vfvjz59+nA2JxJc06BBg3Dr1i02\n6YKAnZ0dmjZtypZLTU1F//798fHjR6H24+Pj2XWCDMPA0NBQ5L+TAJ/Px/v37zmfSfsCEyGE/Mk+\nffoES0tLzJ07Vyjpgo2NDTZv3ixSPYJzpZF/8wWGYdC4cWOp68xv4sSJSE5OZo/19fWxePFimdVv\naGiIgIAAqKiosH/D3NxcjB07FmfOnJFZO4QQQkqXvb09dHV12fGPhoYGzpw5g9q1a0tV7+PHjznH\ngrGXNCZMmID//vsPVatWBcMwsLW1xfLly6Wu93d34sQJznHNmjVhZGTE+ez+/fswMTGBjY0N0tLS\nhDYFvnz5sszXrJibmyMsLAza2tqc9hiGwcmTJ0Vew0NIcWjmmxBCCPnD8fl8XL16FUeOHMHhw4eR\nnp7OfqkULAJTVFTEokWL4OzsLFKigT59+uDu3buYOHEiIiMjOQvogoKCEBQUhDp16mD48OHo27cv\nzMzMxHpYEx8fj/79+7MZvAVfhBcvXoypU6cWeV7lypVx4cIFREZGonfv3iK3J5CTk4OrV69y/jby\nWoBICCHk93Tnzh2cPHmyrMOQSJ06dTBt2rSyDoMQQv4Iubm52LJlC+zt7dlM1Xw+HxUrVsSJEyeg\nqKgIdXV1rFu3DpMnT8a8efNw7tw5dhzB4/Gwe/dudlfz+fPni72ArSh8Ph/r1q3DqlWr2Pb4fD6U\nlZWxb98+qerOysrCuXPnOGOigskoCCGESG7OnDm4f/8+9u7di3r16sHHxwfm5uacMgYGBrh+/Tom\nTZqEsLAwAEBERASMjIxgaWmJ2bNno0ePHoiMjISXlxcCAwORk5PDzvcBgIWFBdzd3Sl5DiGE/EV+\n/vyJuXPnYvfu3ZyXfwTf65s1a4bw8HA2qYC8VKtWDeHh4ejWrRvi4uIA/HoZiWEYvHz5El27dsWU\nKVOwbt06aGtrS9xWdnY2QkND4evri9OnT+Pbt29sO/nHNNnZ2SLXqaWlhR07dmDYsGFsPT9//kT/\n/v1x+fJloZ1jExISEBkZyXmWVbBvJ4QQUroE4ygBdXX1Mk1UIEu5ubmIj4/nfFaaL7xmZGQgLCyM\n0+8JNuwghJDSlpWVJbSGzMDAoNh1ZcVZsmQJNm3axEm6wDAMPDw8Ct0gCAA0NDTYn3k8HqKjo2Fq\nalpo2YsXL7I7jTMMA11dXQB545URI0bgwYMHnLaBvJeDtmzZgokTJxZap4qKCnx8fNC1a1fweDww\nDINXr16hS5cuOHXqFGf8cunSJc65kmxmFBYWhuzsbE6/Sv0AIYQU7tChQ1i6dCnev38v1LfMmTMH\nW7ZsKfZ8FRUVdk7ry5cv7LmSKtgPyDLxgpubG86ePcuOE1RUVHDw4MESk7QJEgKJqnv37jh06BBG\njRrF/j2ys7MxbNgwBAcHF9lfE0LIn+bFixdwdXUt6zAk1qRJE8yfPx8A0LBhQ1y6dAkmJiZISkrC\nyZMn0bZtW6nqT0lJwY4dOzjzVzVr1pRF6DAzM8P169exZcsWuLu7y6TO0vbhwwfOcXH9dVpaGkJC\nQjh/SwsLC06Z3NxcTJkyBTdu3BD6zjN27Fjs3LkTampqsr8QAB06dMDNmzcxYsQI3Lhxgx1La2pq\n4ujRo3Jpk/xdKPGCjNjb22Pt2rUyrzf/AMnd3V3mN+b9+/dj3LhxMq2TEEJI2fv06ROioqIQGhqK\nwMBANpNowcVmggc5Bw4cQMeOHcVqo379+rh48SI8PT2xatUqpKamcup9+/Yt3Nzc4ObmhooVK8LU\n1BRt2rRBmzZtYGhoiPr16xe6CCAsLAyjRo3C169fOV/SZ86cifXr15cYV4UKFQpNuvD69WtcuXIF\n1apVQ9WqVaGjowMtLS1oamqCz+fj+fPnsLe3x/Pnz4V2jCWEEPL3uHv3rlzGdqWhQ4cOlHiBEEKk\nxOPxcPz4cTg7O+PJkyec8VOFChUQHBwsNEbQ1dVFcHAwQkJCMG/ePDx//pwdx6SlpWHZsmXw8fHB\n5s2bhR4+iOvmzZuYPXs2oqOjhR5WuLi4CGWUFtfSpUvx7ds3tm4VFRWpH2gRQgjh8vb2Rtu2bTFm\nzBjO4uz8atasiZCQEBw5cgQLFizAp0+fAADBwcEIDg5G5cqVkZqaCuDXfB+Qt7h8/fr1EiUkJYQQ\nUn7du3cPo0aNwuPHj4WSLjAMgyZNmiAiIkLmu8kUpWbNmrhw4QLMzc3x7NkzzsJwwRjL29sb/v7+\nsLOzw8yZM1GhQoVC60pLS+Mc5+bm4uzZszhx4gSCg4OF+sP8u8IyDAMFBQWxdzIaMmQIpk2bBm9v\nb7aejx8/onPnzjhz5gzat2/Plt24cSP7ghPDMDA2NpZ7cgtCCPlbvX37Ftra2lBXVy+yTHBwMOzt\n7Tn9Yf369UshutLh4eHBmbsD8jankMb379+xfPlyaGtro0qVKqhcuTIqVaoEDQ0NqKurQ1lZGT9/\n/kRcXBx27tyJxMRETvuU8I8QUlbmz5/PjoEEYwB3d3exX0rNyMjA6NGjERQUJPTcxdHRETY2NkWe\nK0ieIIhhypQp2LVrF9q3b8+OcX78+IGrV69i2rRpnFg7dOgAAFBWVsb58+dhamqKV69esfWZm5tj\nz549qFOnTrHxm5iYwM3NDfPmzWPjf/LkCdq1awdXV1eMHz8eHz58gIuLC6f9rl27snX8+PED58+f\nh46ODrumTl1dHerq6lBUVERGRgYuX74MGxsbzt9XS0tL6n6IEEL+NNevX8fcuXMRExMjtF5bRUUF\n27Ztw+TJk0usp1KlSvj8+TOAvAQFkZGR6NKli0QxJScnY8+ePZx+QE9PT6K6CgoNDcWyZcs4da9c\nuRIGBgYlnvvgwQPOsSgbWQwbNgwfPnzA7Nmz2T4pKysLp06dosQLhJC/RlJSEnbt2lXWYUisa9eu\nbOIFAKhduzbCw8Nx69Yt9OzZs9hzz549i3fv3qF69erQ1taGlpYWNDQ0oKysjOTkZNy+fRvOzs74\n+PEjZ+wiGH/JQoMGDeDh4SGz+kpTQkICIiIiON9PqlSpUmT506dP4+fPn5y/5YABAzhlFBUVERoa\niq5du+LevXsA8tZYenh4iPSdR1r16tVDVFQUli5dyr5zvW/fPqFE6oRIghIvyJisM2Tn30H8T8m+\nTQghRLY+fvyIhw8f4uHDh7h79y6ioqLw5MkT9vf5F1/nT4ygra2NZcuWwdbWFqqqqhK3P3v2bIwd\nOxbr16+Hl5cXfvz4wWkHyHtIFRoaitDQUPY8JSUl1KpVC3Xr1oWbmxvatWuHvXv3Yvr06eDxeGy8\nDMPA1tYWW7dulThGAEhNTS0y2VDBZBQCtWrVopeMCCGEEEII+Qu8ePEChw4dwt69e5GQkMBZBMEw\nDGrUqIFTp07B2Ni4yDr69u2Lnj17ws3NDWvXrkV6ejp7fnx8PKysrNC7d294eXmxi/FEFRMTg40b\nNyIgIAAAOGM8hmGwaNEiLFiwQOi8R48eQVlZGVpaWtDS0ip0sUJOTg7u3LkDV1dX+Pn5ca7b0tJS\nqvEiIYQQYQzDYMaMGSKVHTp0KFRVVTF58mSkpaWx9//8L9rk31173bp10NfXlyiuY8eOISYmhvNZ\nQkIC5zgsLAzfv38vso6bN29yjpcsWVLkQjnBruQCly9f5iywKCg6Oppz7O3tjf/++4/zmaqqqkiJ\nWwkh5E/x8+dPODo6YvPmzcjJySk06UL37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1euXFGjRo0UHR1918Rk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\nU6HzDxw4AIC/EIoQQioTwfwuWRHcpxL8e/j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FKSgoKN3l2apIkSI6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9atcHR0VOi1EEII+TmkpKSwiVIFyjvxQuPGjREdHY0RI0YgOTkZlSpVQkhICLS0tAAAkyZN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\nHTqkpUuXmuVGR0erbdu2GjRokDp37qw9e/Zo9OjRNv0q7E1osXDhQ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1zCFzaChzqB29q7DeidEDvrcA97DpwkIzh9Yzh8yhocyh5dO7muzgya79t3+c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\nLHw9t74nfpXGGb1gEsghOdSOHOqzmc+Mnp/FG8l9eJH7YG0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PD06VPWZzk5Ofjf//6Hu3fvVqgYckIIIT/06dMHUVFRGDp0KFJSUpgx6cWLF+jduzfCw8PRvXt3\nVhI0UUTFPUtCknLSJCCqXLkyAgIC0KBBA3h6ejJleTweZs+ejdevX2Pjxo2s+iStW5HxCZQggRBC\n2Nq1a4fDhw/DzMyMde1es2YN6tevz0qcI4vHjx+jV69ezLazszNcXFzk7bZCjR8/Hm/evJGpLIfD\nwcuXL/H333/LVP706dP0fI2QXwglXiiHZH1oVppB0vy2Nm3ahE2bNslVB/Ajc94///wjcz1000QI\nIWwcDgcWFhbYvn07tLS0JC7XvHlzhIeHo2vXrsxKC3ze3t4SJV6YP38+CgsLWdfnzp07IygoCKqq\nqsWW43K58PHxwfPnz3H9+nXWhKcdO3ZgyZIlIgMyisMfZ7p06QITExOYmJhAV1cXb968QWhoqMT1\nyCI8PBze3t5CgR2zZ8+WOoFEgwYNlNFFQgghMsrOzsa+fftYE1ltbGxEBk4QQggpWzweD5MmTUJw\ncLDQ7+a7du1C9+7dmWNHjx6NixcvYvv27cy9SGFhIezt7cHlclkTjKSRk5ODiRMnIiAgQOSqStOn\nT4enp6d8JyqCh4eH0L6hQ4cK7QsODmYm+5ZH4u4hCSGkolu8eDHWr18vNEb17NkTZ86cgaamZrFl\nt23bBg6HAy8vL6Ycf2W8VatWYfHixUrtu4+PD2u7WbNmIscZQdu2bWNt0wrehBCiOLIEYUtaprjr\nddHECyoqKkLH9OrVCzdv3sTIkSNx+/Ztpq7nz59DV1cXZ86cgZ6ensR9J4QQUjG0bdsWbm5uGDZs\nmNjjqlevjjlz5sDS0hKDBw/GkydPWDECmzdvxowZM1CrVi2x9aSmpsLJyUno3mrMmDE4cOCAyDGq\nZcuWOH78OGbOnMm81+e3e+3aNQQGBmLMmDESn7OKigpmzJiBZcuWlRif0bNnT4SEhGDz5s1YsGAB\n67MvX75g48aNYlcg5HN1dcXjx49Z581fmVbU+KqiogJbW1vo6uqib9++QvEgTk5OzAIdhBBSkYma\neKqmpobatWvj7du3SmmzRYsW8Pb2LvH5mKqqKsaOHYuRI0dixIgRiIyMZI1BDx8+xLFjx2BpaamU\nfhJCCFGuTp064dKlSxg8eDASExOZa/zHjx9hY2PDLFJaVr9zy9Kuu7s7qlWrBldXV1biuC1btuDj\nx4/w8/MTKqOlpYUuXbooosvFSklJYa24Tggh5CcjIyO4urpi0aJFrHuievXqKayN0hzLZJkvKkv/\neDye1O/QBMvSMzVCfj2UeKEcyMvLY22LW5FcEI/HYya2SlNOEWTNpidYvujgR4MMIYQoBpfLxcaN\nG+Hk5CRT+SZNmmDhwoXMzRb/5c7FixeRl5eHypUrF1v25s2biI6OZl3T+QkVJJkwo6Kigh07dqBr\n166scSI3Nxe7d+/GkiVLSqxDU1MThoaGMDExgbGxcaknLsjMzISDg4NQYIetra3USRcIIYSUP56e\nnvjy5QtznedyuTInkSOEEKI8PB4PkydPZlY44u/jcDhYsmQJbGxshMp4enoiMTER4eHhrJXmJk2a\nhIyMDMyYMUOqPjx79gzjxo3DnTt3RCZdWLhwIVxdXeU7URGuXr2Ky5cvCz1rE5XsVVVVlZIbEEJI\nKcvIyICdnR2OHz8uU9IFPk9PT3C5XGzbto21WsXSpUtx69Yt+Pn5sVY3VZTU1FScOHGClYxu0qRJ\nYstcvnwZ9+7dEzrfouR99ySqPnr3RAghbNJeF4s7vmjiheJiFRo3boxLly7B2toax48fZ671HTp0\nUHoANiGEkNLF4XAwa9YsbNiwQarnTY0bN8bJkyfRsWNH5ObmMvvT09Nx/PjxEu831q5dy7y3EVwY\nws/PT2TSBUFbt27FnTt3EBMTw7pfcXV1lTjxgra2NgICAtCzZ0+JjuebN28eHj16BF9fX1Zcxv79\n+0tMvPDp0yds2rRJ6B5rz549JSY1ateuHfz8/GBqaspq9/Hjxzhx4gRGjhwp1XkQQkh5w782amtr\nw8jICMbGxhg0aBDWr1+PlStXKry9bt264eHDh6hatarEZapUqYLAwEDo6OgIJcKhxAuEEFKxtW7d\nGtHR0Rg0aBASEhIA/BiTIiIiwOVyYW9vjwEDBogsGxAQgCtXrgD4MZ45OjqiZcuWJbYZFRWF4OBg\nppy1tTV0dXWLPf6vv/6S6pxWr14NVVVVrFy5krnn0tDQKPZerXv37jh16pREdd++fRsvXrwA8GNS\n8PDhw0Uel52dDTU1NWY7MDAQVlZW0pwGIYT8VhYsWICbN28iKCgIWlpaCAkJQe/evcu6W8V68+YN\nWrRoIfYY/hiUnZ0tMgbu4sWL6Nevn1AZSd+LUVwBIaQoSrxQDhRNvCBuQqu4cqUVJK2IwYQGJEII\nUZ4NGzZAW1tbrjqsra2xaNEi1r7MzEzEx8eLDYTbvn078zP/RsXMzAxdu3aVuO2OHTvC1NQUJ0+e\nZAUKHD58uMTECw4ODli6dGmpJiMqyt3dHcnJyayxrnXr1tixY0eZ9YkQQohipKenMyvS8se50aNH\nQ0dHR6LysmReJYSQ3wH/+ljcdVLa6yc/WcKBAweEgo///vvvYgOXuVwuAgMD0b9/f9y6dYsVeDxr\n1izExcVh27ZtEt1v7NmzB46OjsjMzBRKusDlcrF27VosXLhQqvOSlDKSOZR3Jf0foTGYEFJe3L17\nF6NHj0ZCQoLQGGViYoKAgACoq6tLXJ+Hhwc0NTWFVho6fvw44uPj4e/vj+7duyv0HHbv3o3s7GxW\nMrqJEyeKLePs7Mz8zOPxULt2baGgh7NnzyIzM5M5jz59+qBOnToK6XNJQRqEEFJRtW3blnWNVabO\nnTuztosmXhAX46CmpoagoCDMnz8fbm5uaNWqFUJDQ1nB0oQQQiq2KlWqwM/PD9bW1jKVb9WqFezs\n7LBz507Ws7TIyEixiRe+ffuGPXv2sMpwOBx4e3tLFH/H5XLh4eHBTEriPwuMi4vD9evX0atXL7Hl\n9fX1cfz4cdSsWbPEtkRxcXHBgQMHWONqcnIyHj9+jD/++KPYcl5eXsjKymK9rxo8eDBGjx4tUbvG\nxsYwMjJCREQE67vbsWMHJV4ghFRYqqqq6NOnD4yNjWFsbCz2OqpIdevWlalc9erVYW9vj3Xr1rHe\nR929e1fBPSSEEFLatLW1cenSJQwcOBA5OTm4ePEiE9M9ZMgQDBkyRGS5uLg4JvECAIwaNUqiSbJ5\neXlM4gUAGDRoECZMmCDnWbAtX74cqqqqWLJkCTQ0NBAWFgYDA4MSy2VmZmLcuHEYOnQoHBwchD7f\ns2cPdu7cCQCoU6eOyMQLjx49gqGhIdauXQs7Ozu5z4UQQn4Xvr6+yM7OxpYtW9CqVauy7o5Eiptr\nKm4BB3GLPnA4HMyYMUNhcQdF+fr64s2bN0qpmxBS9ijxQjkga+IFwSzf0pSTh52dXYkvlUrTtm3b\nWDeYhBBCIHfSBQBo2LAhmjVrhqSkJNb+Dx8+FFsmLy+PtVIf39SpU6Vu387ODidPngTwM7jh0aNH\nePXqldhA6Xr16kndliJlZWVh8+bNQoHzW7ZsoVVkCSHkF7BhwwakpqayxrpTp06hYcOGEpUvGpAO\nACNHjlTYGNG1a1eJM4YTQkh5UfT+oaTtkmRlZcHS0pIVNMz/vdzQ0BD79+8XW15dXR3h4eEwNDTE\nw4cPWZNYd+3ahcePHyMoKKjYQLrPnz/D3t6eWQmcj/8CqFatWjh06BCGDh0q1XlJ6sGDB0IB0+XV\ngwcPcPv2bZiYmMh9L1c0uL64z0RtE0JIadmxYwccHR2Rm5srNEZNnz4dW7dulekatXr1aujo6MDB\nwYGpm8Ph4NmzZ+jduzeWLl0KZ2fnEld6lURhYSF27NjB9JPD4UBXVxdNmjQptsypU6dw+fJl1oSg\n5cuXY/bs2azjWrduzaz+xD+voskZCCGEsLVr1w4uLi5l0rYsMQ6bNm2CtrY2jI2NoaWlJXFbycnJ\nOHPmTIkrnpeVgoICcLlcutcghPzW5syZI3fc2ogRI5gJN4IJEMQJCgpCeno6636jb9++0NPTk7jd\nHj16QF9fH1euXGFdy48ePVpijNyZM2fkOu/GjRvjr7/+QmxsLKvtuLg4sROG9+3bJzTuLF68WKq2\n582bh4iICAA/v++oqCikpqaidu3aUtVFCCFlbeHChfD09ES1atXKuitSGTBgANatW8fa9/79+zLq\nDSGEEEVq1KgRLl26hKysLIXEdJcH//zzDzQ0NNCxY0eJki6kpqbC2NgYN27cQGhoKLKysuDk5CTy\n2OKeqyUlJWHo0KFISUnBpEmT8PjxY6xfv16u8yCEkN+FhoYGMx9HGp8/f0ZycrLIz54+fcraTklJ\nwf3790UeW7t2bTRt2lTq9hVt5syZaNOmjVLqjoqKosQLhPzCKPGCgm3fvl3qgenRo0es7alTp0JD\nQ6PEcvn5+azt2NhYGBkZSdX2zJkzYWJiIvHxnTt3FlrNoiyFhobi6tWrZd0NQgj5JdWrVw+JiYms\nB1qpqanFHn/lyhWkpaWxjq9evToGDx4sddvDhg2DmpoacnJyWPsvXrxYrleo8/f3x5cvX1iBHYaG\nhjA2Ni7rrhFCCJHThw8f4OHhwZpgBAAZGRnIyMiQqi7Bld2/fv2qsD5++vRJYXURQkhpMDAwEJmU\nhs/W1ha2trZS1eni4iKUdAEAdHV1cfz4cYkCoevWrYtLly7ByMgIsbGxrOQLly9fRseOHeHl5QUL\nCwtWud27d2Px4sVCSXr45Tt16oTjx4+jefPmUp2TNJYvX860V1xG75J8/vwZQUFBCAwMhKmpabHB\nD/I6fPgw1q9fDw6Hg549e8Lc3BxmZmbo0KGDVPW8evVK7Ofi/o8RQkhpePfuHaZMmYLTp08LjQ8q\nKirYsGEDHB0d5Wpj4sSJaN68OSwsLJCWlsaMAQUFBVi5ciVCQ0Oxfft29OjRQ652QkJC8ObNG9Y4\no66uXuzxPB4PixcvZp13o0aNMG3aNLn6oQw0WZYQQqRT9PdsSSedzpo1S+I2YmJi4ObmhuPHj6Ow\nsBD9+/dHy5YtpepnaTh8+DCcnZ0xceJETJo0qVwEEhJCSGlTxGJBXbp0EdonbmEIADhy5IjQvsmT\nJ0vd9rhx45iFf/j3O+Hh4diyZYvYcoo679jYWNY+ced948YN5r6Mr3nz5hJNfBI0YMAANGjQACkp\nKcy+goICREZGwsrKSqq6CCGkrIlLVlOeNWrUqKy7QAghRInKeiE5ZZDm2Z6amhrU1NSYe5f58+cj\nPz8fCxcuFDpWcCVzvvfv32Po0KF49+4deDweuFwuWrduLXvnCSGESCQgIKDE6z3/ur17927s3r1b\n5DHW1tYlLpDEV7VqVQwbNkzkZ/n5+Th37hzzzI7L5Yqcn0SJRAkhikaJFyD6F3VJPhPl0aNHiIyM\nlKs//Bc5kuL38cOHD1K1zeFwYGZmJlVb5c2BAwdw4MABuepQ5L8/IYRIqzxfg0StgicuMVBUVBTz\nMz/w2sDAQKbV9NTU1NCzZ09cunSJFTBw/fp1TJw4Uer6Ssu+ffuE9s2YMaPU+1Ge/18RQkhFtWrV\nKnz//l1oIq+86LpMCCGKtWLFCjx48IBZrQ34EbwcHh4udmJoUTVq1MC5c+cwcuRInDt3jpV84ePH\njxg9ejQsLS3h5eWF5ORkODg44MaNG8wxwM9rPIfDgbW1NXbu3Ak1NTXFnrCAmzdv4uTJk8yLJmll\nZGRg9OjRuHDhApPwNTU1VWmJF86ePct8V7GxsYiNjcWePXvw8uVLpbRHCCFFlcbzk3379sHR0ZGV\nrJRft5aWFvbv3y91Qu3iDBgwANevX4eFhQUeP37MGofu3LkDPT09TJo0Ca6urqhTp45MbWzYsEGq\n4/39/REXF8dK1LB06VKoqqrK1L4y0fM0QgiRTtFFIhQx8VRQXl4ezMzM8PnzZ2YM3b59OzZu3KjQ\ndhTB398fiYmJWLlyJVatWgUTExOcOHGCkvoQQsq98vY7sKjg6G/fvhV7fF5eHq//7CYAACAASURB\nVKKjo1nXWw6HA1NTU6nbNjU1xfTp01n7Xrx4gZSUFNSvX1/q+qQh7XmfOXOG+Zl/n2Vubi51uxwO\nB8bGxti7dy/rO7x06ZLEiRfK2/8hQkjFQtcQgMvlCu3T0tKSuh76LgkhpGwkJCTg48eP0NXVLeuu\nlEvq6uqIiIiAsbExc++2ePFiqKurY+bMmWLLvnnzBoaGhkhISGDuezw9PTFlypRS6j0hhChXRfgd\nXtb4M35ZadSrVw+nTp0S+VlaWhpq1arFbFeuXLnYYyuKivDvTwgBhJ/a/Gb4wdCCQdGSfCZpvdIq\njQskveD/SVn//oQQIomSrjFlff359OmTUPviMrAWXYUBAPT09GRuv3v37kL74uLiZK5P2f777z/E\nxMSwvrMaNWooLHheUuX9/xUhpPyhBzglu337Nnbu3MmaKFX0fkHSP0XJWo+k9YvD4/Ho35gQ8sup\nUqUKjh07ht69e4PD4aBbt244f/486wWMpNTV1REeHo5p06YxL5MEJ7EGBQWhbdu26NmzJ5N0gY8/\nVjRo0ADBwcHw8/NTatIFAFiyZAnzsyzjgqamJhITE1FQUMCUjYuLU8p92OfPn3H37l3WPg6HA0tL\nS4W3RQghoij7+cnTp08xZMgQTJo0Cenp6UL3Er1798a9e/cU/tyobdu2uHXrFuzs7FjnwP/bx8cH\nbdq0wdq1a/H9+3ep6o6KikJsbKzE30teXh7+/fdf1vHNmjUrl0Fx9K6IEFIWKvozOWUnXqhcuTLG\njx/PbPN4PPj6+iInJ0eh7cjr/fv3OH/+PDNOCD43lERF/39ACKm4yuPvwKKu8TVq1Cj2+Bs3biAr\nKwvAz2tmx44dZXoO2KRJEzRr1kxo/82bN6WuS1rSnnd0dLTQvv79+8vUtr6+Pmubx+NJfM7l8f8Q\nIaTioGvID2/fvmV+5t9LdO7cWep6JI1FIIQQ8pO8z2QKCgpgZWWFfv36wd3dXZFdK1M8Hg/Pnz9X\nWH1Vq1ZFeHg4unfvzsRbzJ07F0FBQcWWef/+Pfr164eXL18yK5tv3boVDg4OCusXPZMjhJSlinA/\nJBhnIE1/y7rffCVdy4ODg6Gvr4+0tLQS65oxY4ZCk4JXhH9/QsgPlcq6A2VJW1sbBQUFIj8zMDAo\n9jNJ8R+ESXosIH1SBMHBgC6q0rG1tYWtra3Iz/7991/8+++/pdwjQsjvRtw48+rVq1LsibBv374J\n9YHL5aJjx47Flrl//77QWCTLyyA+wbb4wWpPnjyRuT5li4iIYMZ+/t/Dhw9XeLBhScrz/ytCSPkj\neN0W9QCnuM+kcfHiRYlXxlEW/gshae7R+PLy8jBx4kTm+srj8aChoYFnz56hYcOGUtX18uVLtG7d\nmjVWXLx4EX379pWqHkWQ9N+eEEIqoipVqiAkJATTp0/Hzp07UbNmTZnrqlSpEry8vKCvr4+pU6cy\nk1T513H+CxjBazv/7/Hjx8PDw0OmYG9pnTlzhpnsU7Qf0rCyshKaJHvo0CG4uroqtL/nz58X2b9R\no0YptB1CCCmOsp6ffP/+HS4uLnB3d0deXh4r4QLwY7xwcnLCunXroKKiInM74qipqcHHxweDBg3C\ntGnT8P37d9Yk0LS0NDg7O8Pd3R0LFizAjBkzoK6uXmK90o4FLi4ueP36NWtMWr58OSpVKl+vJqOi\noor9zNfXF76+vqXYG0LI70Lc7+myPpd5/fo1HB0dZe2SSJMmTUL79u3RunXrEo+tXr26zO2cOHFC\n5ErdkydPxtatW5ntL1++4PDhw7Czs5O5LUU7ePAgk7yOP95NmzZNorLK+H9ACCGSUHa8nKzevXvH\n/My/h2rSpEmxx9++fZu1zeFwRC7uIKmuXbsiMTGRdQ1++PAhTE1NZa5TEoLnzSfuvO/cuSM0Tsh6\n3l27dmV+5o9ljx8/LvG5IsXcEULkUV7HobJw7tw5oX0DBw6Uqg5xzzJ/p++SEEKkoYh4uWXLluHm\nzZvMe5/o6Gj4+vrKFZtQHjg6OmLHjh1wdXVV2LNGdXV1hIWFoVevXnj9+jX69++PwYMHF3t8gwYN\nYGpqih07dkBFRQU+Pj6wsbFRSF+A0omXJISQ4ijifiglJQUzZ84s8TgXFxf88ccfUvfRzs4OZmZm\nIj978OABzM3NmWvkzJkzMW/ePJHHVqtWTeq2FaXoAhWC9u3bB3t7exQWFmLgwIE4c+YMtLS0RNYz\nZcoU7N27F8CPZKj79+9H7dq1RbYlCXqmRkjFUr6im34h/BcQYWFhJa5Y1KtXL2aV8CpVqiAzM1Oi\nNq5fv44+ffowF+mVK1fC2dlZbJnNmzdjwYIFEtVf1MqVK7Fy5UqZypaFJk2aIDExsay7QQghFVJU\nVBQKCwuZl+vAjyQKxQVhf/36FR8+fBC6cZAkGK84LVu2FNqXnp6OjIwMaGpqylyvsly5ckVon56e\nXhn0hBBCJFMaAQ08Hg+vX7/G69ev5a5LXoJjmjRWrVqFhw8fsoKnlyxZInXSheKURZZscZOHxE06\nIoSQikRLSwuBgYEKq8/Kygq1a9eGqakpc69UFH+sqFSpEvz8/Eot8VBBQQHmzp3LemkkS9IF4Gfi\nBcF6AgICFJ54QVQwYePGjaGrq6vQdgghpLQUFhbC19cX//77L969e8dagUDwmly7dm3cu3cPw4YN\nK5V+NWzYEC9evGDdD/H79vnzZyxatAjr1q3D5MmTMXPmTJErvALA5cuXce7cOYnHmHv37mHDhg2s\n43R0dIoNIiCEkN+NuOdusj6TS0tLQ0hIiKxdEsLhcNC3b1+0b9+e2S6KP7bIev9Rko4dO6J79+64\ndesWMwZ5eXmVq8QL/v7+rO1mzZpJPM4r4/8BIYRUZDdv3mRtczgc/PXXX8Ue/+DBA6F9HTp0kLn9\ndu3aCY2lL168kLk+SfEnawkq7ryTk5Px5csX1vHVq1dH48aNZWq7bdu2Qvuys7Px9u1bsckfCCGE\nyC8tLQ1+fn6sa7q6ujqsra3LsFeEEPLrU0S8XGJiIjZu3Mh6Px8SEoJu3brhyJEjQonRoqOjMWDA\nAKn7qq+vL3UZHo8HOzs7iZ6fjR07FocOHWK2PTw8sHXrVnA4HMybNw/nzp3DgQMHhCaYyqJevXqI\niIiAl5cXNm/eXGKSbi8vL2hoaKBv377FTv6VBU14JYT8Cr59+4bg4GCxMdEcDgezZs2SKfGChoYG\nNDQ0RH728eNH1naNGjWKjTEoK7GxscjPz2e2GzRowPy8ZcsW1pzau3fvYuDAgbh69apQooi5c+di\n7969zHh/6tQpdOnSBYGBgejVqxcAICgoCDk5OUyZunXrKuWcCCFlgxIvKJkkk2jS0tKYAa9GjRoS\n1/3t2zfWdp06daTunywqQva2spi8RAghvxI/Pz/WNofDwd9//13s8W/evBG5X54bqaZNm4rcn5yc\nLNNNoLLFxMQIjZGdO3dmbaempuLw4cM4ffo0Hjx4gE+fPoHH46FevXpo1KgR+vfvDxMTE/Tp06c0\nu04IIRVC0WtsafzOf+/ePaxfv57VdosWLYrN0EoIIeTXdPbsWXh5eSE8PByFhYWszwQnsfL/zs/P\nh729PSIjIzFp0iT069dPqf3z8vLCkydPWJNhNTU1wePxhJ4flqRVq1asiU3Aj8CRq1evKvQ+JTIy\nUmhC8qhRoxRWPyGElKZjx47B2dmZuRYLXt+AH2ODiYkJTp06hdTUVJw/f75U+9emTRvMnTsXixcv\nRnp6OmtyLIfDwdevX7Fp0ya4ubnB3d0dM2bMEKpj6dKlzM8lvSMqKCjAxIkTmWAGfntubm7gcrkK\nPDNCCCGiKOJdvrzP3QTLi+uPJAkbJk+ejFu3bjHbd+7cwZ07d9CtWze5+qgI9+/fx/3791n3YlOm\nTCnrbhFCSIUlKoGQuFVQExIShPbp6OjI3H6LFi2E9iUlJclcnyQePXqEly9fsgLl27Vrh0aNGok8\nXvCc+cfLc85qampo0KABUlJSWPuTkpIo8QIhhChRXl4ebG1tkZKSwrqfWLFihVQx5IQQQspGs2bN\nEBAQAFtbW2RmZjLvhl6/fg19fX3s2LFDZOIDSZ7bSfpcTVxZWZ4Pvnr1CgsXLmS9P4qIiECXLl1w\n5MgRmRahCw4Oxvv371n72rRpg507dzLb8fHxzM/Z2dnw8vJitrW1tZGYmMjaBwDjx4+Xui+EEPK7\nkHWBul9JvXr1hPbxeDzMmTMH27ZtE4pX+/vvv4WSLgDA9OnTcf78eTx69Ig5Njk5Gf3798fmzZsx\nc+ZMaGlpKf18CCFlhxIvlANpaWnMzzVr1pS43Pfv3wH8vNiXVuIFwTbLq/LcN0IIKe+ePHmCEydO\nsK6lqqqqYjNqv337VmhfjRo1oKqqKnM/RN30AD+SF5Q3eXl5QqtdcDgcdOzYEcCPVQ/XrVuHdevW\nMROfBL/fpKQkJCYmIiYmBuvWrYOuri42btwoU8ZaQggpbxT5u7k8L5eklZ2dDTs7O6HJQl5eXnKN\nb4QQQiqGtLQ07N27Fzt27MDz588BsMcewaAFHo8ntIp4VlYW9u/fj/3790NHRwd2dnYYO3asXIHQ\nonz8+BErV64UGhcXL14MDw8PfP/+XeoXalZWVqyJTQAQEBCgsMQLT548QVJSklCfLS0tFVI/IYSU\npgULFmDz5s3FJlyoW7cuPDw8MGbMGFSuXFkogU9p4HK5mDZtGkaMGIFZs2bh2LFjIoPv6tSpg7Fj\nxwqVP336NK5cuSIUgFCctWvXCk1CtbCwgImJiYLPjBBCiDitWrWCtra2VGW+ffuGmJgY1j41NTWR\ngdV37txBXl4eeDweqlSpgq5duwL4MYE0PT0dwI9xpmfPniU+xxO3ap6VlRWcnJyQnZ3N7Nu1axd2\n7Ngh8Xkpi2CQOACoqKhg0qRJZdQbQgip2N6/f4+wsDDWmKGhoQFTU9Niy7x580ZojCkuYYEkBFe/\nA37c+xSdJKRou3btYm1zOByR92V8RRfE4HA4cp0z8OO8379/z/oulX3ehBDyO7t//z5mzpyJq1ev\nsp6fDR8+HE5OTmXdPUIIIRKysLBAs2bNYG5ujg8fPjDX87y8PEyaNAkPHjzApk2bpEpIXTQGoTTn\nw7Ro0QJhYWEYN24cUlNThSaYenh4YOrUqVLVuWnTJsTGxkp8/Ldv3zBr1iyxx3A4HAwYMECqfhBC\nyK+Gy+WKTBSQm5uLvLy8MuhR+ZaZmQkrKyuEhoayYh4qV66MPXv2wMbGRmS5Nm3aIDY2FpMnT8aR\nI0eYsTE/Px+zZ89GTEwMdu/ejapVq5bm6RBCShElXihjPB4Pnz9/BgCpkycIJmwAUKqJF/iTSZW9\nYp88atWqVdZdIISQCsnJyQmFhYWsh3e2trZiX9iLSoYgLlBOEurq6qhUqRIKCgpY+79+/SpXvcrw\n8uVL5Ofnsx501qhRAxoaGsjKysKIESNw9uxZJghfcGIWn2DZ2NhYGBgYYO3atVi0aFGpnQchhCga\nfwzZu3ev3HXVqlWLtTrrs2fPJJ686uXlVeKLmaImTpyIBw8esMbDCRMmYOjQobJ0nxBCSAWQkZGB\n0NBQBAUF4fTp08jOzmZNpAXYwQ4qKioYNWoUHj58iMePH4tMwAD8WIlu2bJlWLZsGVq0aIEhQ4Zg\n8ODBGDRokNwrF82ZMwdfvnxh9bF58+ZwcnKCh4eHTHWOGTMG8+fPZ86Dx+MhKCgIW7duVUhwR2Rk\npNC++vXrU+I5QkiFNHfuXPj4+DDvawTHARsbG7i5uTHvKoqOKaWF32aDBg1w9OhRhIeHY/78+Xj2\n7Bmrv97e3kIrMhQWFmLBggUSJ114/vw51qxZwzqmevXq2Lp1q6JPixBCSAkmT56MhQsXSlXm/v37\n6Nq1K+s63rhxY1y7dk3o2AYNGuDjx48Afqw+xz+mb9++uHr1KnPcpUuXULlyZVlOAcCPccTCwgL+\n/v7M/cnhw4exefNmaGhoyFyvvLKysnDo0CHWs0Nzc3M0bNiwzPpECCEVmaurK/Msjn9dnTJlitjA\n5ZSUFKF9xS3uIIm6desyP/P78eXLF5nrK8l///2H3bt3s8bdKlWq4H//+1+xZRR9zgD7vPmUed6E\nEPK74PF4+PbtG9LT0/H06VPcu3cPISEhuHr1KpOcVTAub9euXbTgHCGEVDA9evRATEwMjIyM8PTp\nU+a6zuFw4O7ujkePHiEwMBCtW7fGtm3bxNb1/PlzeHh4MPciderUwYoVKyTqR3h4OCIiIphxpGXL\nlnB0dCyxXJs2bVjbgwcPxo0bN2BqaoonT56wkkk4ODjg5s2b2L59u1TP+iQZ20QlCyeEEFK8Fi1a\nMAmwBS1btgxr1qyRq+6UlBRmsbriPheUkZEhcgFXQfXr15erT/J49eoVRowYgbi4OFbMQ/Xq1REU\nFARDQ0Ox5dXV1XH48GH06NEDixYtQkFBATPWHzp0CPHx8Th58iSaNm1aGqdDCClllHihjCUmJiI3\nN5e5gEszoCQkJLC2iwbEKVu/fv0oWI4QQn4xvr6+OH36NOsBlqamJpYvXy62nOCLd/5DME1NTbn7\nU61aNaFEQ1lZWXLXq2jJyclC+6pXrw4AGD16NJN0QfDBalGiJmgtXrwYqampWL9+vRJ7TwghFYO2\ntjbi4uKY7aSkJKlWDZfm5czatWsRGBjIKlO/fn24ublJXAchhJCKIS0tDSEhIQgKCsLZs2eRk5MD\ngP17uWDSNA6HAzU1NUycOBHz589H8+bNAQABAQFYvXo1Hj9+zCrDr4P/9+vXr7Fz507s3LkTKioq\n6N69O/T09NC5c2d06tQJf/75J6pUqSJR30+fPo2AgAChybDr16+HqqqqzN9Jo0aNoKenh+vXrzN1\nf/jwARcvXlTI6hFnzpxhfub3eeTIkXLXSwghZaFx48bw8PCAra0tgJ9Jqz09PdG3b1/Wsbm5uWXR\nRSEmJiYwMjKCj48PXFxc8O7dO4wYMULktdjb2xvx8fGs51q9evXCtWvXRN5jtWzZEjY2Nti7dy9z\n/Lp164RWjhWH31b//v3lOU0AQFhYGIyNjeWuhxBCiLCMjAzmZ/77EFGKJqGWxeTJk+Hv789sf/v2\nDYcOHYK9vb3cdcvq8OHDSE9PZ42HM2bMKLP+EEJIRXbr1i14e3uzrqlqampMYlBRcnNzkZmZKXRf\nIm5MKomolQJFBbEryrRp05CVlcW637K3txcbOyhqQQx5zhko/fMmhJBfWZcuXfDgwQOxxwi+f/rz\nzz+xevVqmJubl0b3CCGEKAE/IamZmRmuXbvGihE+c+YMdHV1cf36dUyfPl1sPUOGDAHw8/354sWL\nSyzDN3LkSLRp0waZmZng8XhISEhA48aNMXz4cKnPp0WLFoiJicGYMWMQGRnJOp+9e/fiyZMnOHbs\nmEQJ4AwMDIpNUhofH4/nz5+zFrNTVVWFkZGR2Do5HI7c90CEEEKKp6uri8TERLHHCD6P8/DwELsw\nEIfDwd27dxXWP2lcuHABY8aMQWpqKjPWAD/G7rCwMPz5558S1+Xk5IT27dvDysqKtYDg/fv30aNH\nDwQHB6NPnz7KOhVCSBmhxAtl7Pnz56ztJk2aSFz2xYsXrG1tbW2F9IkQQsjv6cGDB5g1a5bQpB1X\nV1c0atRIbNmiyRD4k5HkJWqyUF5entz1Kppg9j7+91a9enW4ubnh1KlTzHdao0YNGBkZQV9fHw0a\nNEBOTg7evXuHCxcu4Pz588jLyxNaOXDTpk3Q09OjiUiEkN+etrY2K0ghKSlJ4rLDhw9H69atme3G\njRsXe+zJkyexfPly1njI5XLh5+eHmjVrytBzQggh5UlOTg6uX7+OqKgoREVFITY2lrnHEJdsAQCa\nNm2K6dOnY8qUKahduzar3rFjx2Ls2LGIiIiAu7s7zp07V2w9/L8LCwsRGxuL2NhY5hgVFRW0adMG\nnTp1Qrt27dCoUSPWn3r16jHllyxZItTfPn36wNLSUu7vydLSEtevX2ftO3LkiNyJF3JychAdHS0U\nFD969Gi56iWEkLJkY2MDX19f3L9/Hy4uLpg+fXq5X5WHy+XC3t4eEyZMwNatWzFu3DihY758+YIV\nK1awghC6dOmCKVOmiFz5HPgxju3evRuNGzfGqlWr0Lt3b0ydOlWmPsrzHRZ9vkYIIUSxCgsLmYmi\nHA5HIYm4xTEwMICOjg4SEhKYcWnXrl1lmnhhz549rO127dopJFEdIYT8br5//44JEyYIrfy9fPly\nsTEKggmABGloaMjcF1FllZVAb8+ePQgNDRVKAO7i4iK2nKjzlueciytfXhIHEkJIRVPcYjx8/Gds\nI0aMwLRp0zB48ODS6hohhBAlqlmzJs6fP4/x48fj2LFjrEUa+vfvj1q1aokt7+/vj3PnzjHPvRo2\nbChx0gUAaNiwIRYtWsTEu/F4PNjb20NPT0+mVcY1NTURFhaGadOmwcfHh5V84dq1a9DV1cXDhw9L\nvBdZt26dyP3Pnj1Dr169WEno+O0eO3ZM6v4SQghRHMGEOMWRJOF2SXUo24YNG+Ds7IyCggIAP585\n/vXXXwgNDZVpfBw2bBhiYmJgZmaGly9fMnV++PABgwYNwu7du2FjY6PoUyGElCFKvFDG4uPjAfy8\niLds2VLisoKJF+rVq0fZ2wghhMjs8+fPGDVqFJNAgT8uDR06FA4ODiWWF5UMQUVFRe5+Vaok/KsK\nP+iiPBG1skRGRgZWrFjBbDs4OGD16tUiJ+3OmzcPL1++hIODA86fP8/s5990TpkyBQMHDkSNGjWU\n0n9CCKkI+CuK8yUnJ0tctkmTJhIlufv69StsbGyYB3788XDBggUU9EAIIRVURkYG7t69i+joaERF\nReH69evIyclhPi8aBFc0UYKKigqGDRuGKVOmwMzMrMRJnEZGRjAyMsKzZ8/g4+ODAwcOMInaxCVh\n4CssLMSTJ0/w+PFjkfVXqlQJ48aNw759+zBo0CDcu3ePqaNSpUrYunWrJF9LiSwsLDBv3jymjzwe\nD8eOHRNafVBa0dHRQqsR1q1bFwYGBnL3mRBCypKfnx/U1dWhpaVV1l2RSpUqVbBgwQKRnzk5OTGr\nPwA/xoOtW7cKJQUXZcWKFahTpw4GDhyo0P4SQggpH4pO+iyNOIWJEyfC2dmZGZfu3LmDuLg4dOzY\nUeltF/XgwQPExMSwgsNnzpxZ6v0ghJBfwcSJE/HkyRPWs6KuXbti/vz5YssVlxhAnhgFUWWVsSjE\n7du3RS6IsW3bthLjAUSdd+XKleXqT2mdNyGE/C4kmVx04sQJ3Lt3D/369YOVlRWzyjkhhJCKS1VV\nFUePHsXs2bPh5eUFABgyZAi8vb3Flvv69Svmz5/Pes60fPlyVKlSRar258+fDx8fH7x58wbAj5hw\nW1tbnD59Wqbz4XK52LVrF7S1tbF8+XJWMglHR0eZE8B9/PgRxsbG+PLlC1OfoLy8PNjb28POzg79\n+/eXqQ1CCCHyU8QiB6WxUEJ2djaio6MRGRmJBQsWQE1NDTY2NqyFU/njq4WFBfz8/FC1alWZ22vb\nti2uX78OU1NT3Lhxg6k7Ly8Ptra2eP36NZYtW6ao0yOElDFKvFDGLl++zNrW0dGRqFxOTg7u37/P\nDARt2rRReN8IIYT8HrKzs2FmZsasFMTXrFkz+Pv7S1SHqGQIishSJ6peeQMHlCE7O5u1zePx8Pr1\nawA/g9JnzJghtg4dHR1ERETg77//xvHjx1n/Fl+/foW3tzcWL16s8L4TQkhFwU+8wL8+JiUlKbyN\nmjVrYubMmXB1dWXa6dWrF1avXq3wtgghhCheUlIS7t27x/rz+vVroYQHxSVa4H8OAJ06dcL48eNh\nY2MjU5brNm3aYP369XB1dcXp06dx5MgRhIaG4uvXr2LbLW6bX0ZVVZVJ8DZz5ky4ubkxL3HmzZuH\nLl26SN1XUZo1a4bu3bvj1q1bTF8+ffqECxcuYNCgQTLXGxERwfzM7/eIESNoVXJCSIXXtGnTsu6C\nQp0/fx5+fn6sQD8rKyv06dNHosQLAGSegMpvr0+fPqhTp45MdfA1aNBArvKEEEJE+/z5M2u7NBIv\n2NnZsYK8AWDv3r1wc3NTettFFU14p6mpiQkTJpR6PwghpKJbvnw5goKCWEHQNWvWxNGjR0tMoJCf\nny+0j8vlytUfUeXlrbOopKQkjBgxgkmgwL//mTFjBkaNGlVieVHnLe+CGKVx3oQQ8jsp6X0H/57m\n9evXePXqFfz8/NCtWzds27YNenp6pdFFQgghSrR161Y0bNgQAQEBOHLkiNjfrXk8HmxsbPDhwwdm\n/OjRowfs7e2lbldNTQ179uxhkvnweDycPXsWc+bMgYeHh2wnA2Dp0qVo3Lgxpk6divz8fCxatAiz\nZ8+Wqa5Pnz5h0KBBePXqFYAf9zJ//PEHHj58yBwzb9487N+/H/v378fo0aOxadOmX+4dHCGElGdh\nYWGsBY0UoW3btoiJiVFYfYWFhXB3d0dERAQuX76M7OxscDgcWFtbY9asWbh+/bpQ0oWlS5fCxcVF\nIe1raWkhKioKY8eORWhoKNMGAKxcuRLDhg1Djx49FNIWIaRsUeKFMnb58mXWBd3BwQEnTpxAt27d\nxJaLiYlBbm5uuUq88OrVK+zatavM2q9VqxYWLlxYZu0TQkhFVFhYCCsrK2ZlHuDHeKShoYHg4GDU\nrl1bonoqVWL/SsHj8YSSEchC1EoKqqqqcteraEVvMAWD0qdPn15i0gU+FRUV+Pr64ubNm3j79i2r\nLk9PT0q8QAj5rRW953n27JlS2lm9ejXu3buHiIgING3aFMHBwXIHrRFCCFG+/fv3w87OjrVPcIVu\nQUUTMfC1b98eFhYWsLKyQtu2bRXSLy6XC2NjYxgbGyM/Px8XLlxAeHg4IiIi8PLlS5F9EtU3/v3F\nqlWrmGRE2traMDMzQ0hICNq2bcskZFAUS0tL3Lp1i7UvKChIrsQLghnFYHmIjgAAIABJREFUBdsh\nhBBSvsyZM4d1vVZXV8f69etLtQ+rV69Gv379SrVNQgghkuEHR/M1adJE6W02atQIBgYGiIqKYt6b\nHDx4EBs3bhR6R6VMqampOHz4MOs9kK2trcyr/BFCyO/Kx8cHq1evZsUocLlc7Nu3Dy1atCixvKjJ\nS4WFhaxAZ2llZWUJ7VNkbEJ6ejqMjY3x7t07AD+fB+rq6mLLli0S1SHqvPlJHGSl7PMmhJDfSUhI\nCBMvx+Px8O3bN3z79g3v3r1DfHw8YmNjcenSJeTn57PGrDt37sDAwAA7duzAxIkTy/IUCCGEKMDi\nxYsxb968En+vXrJkCcLDw5nnTKqqqvDx8ZH5nmbQoEGYPXs2PDw8mDq3bduGRo0aYdGiRTLVCfxI\niFq/fn2EhYVh7dq1MtXx/v17GBoa4vHjx8wYuG7dOrx48QIPHz4Eh8PBt2/f4Ovry5z/0aNHERYW\nhsWLF2PRokXlctE+Qgj51fz5559l3QUhycnJrO3c3Fw4OTkBYMfWpaamwsfHB/r6+khNTQXwI87B\nx8cHY8aMUWif1NTUcPz4cdjb28PX15cZ29zc3CjpAiG/EEq8UIbu3buHjx8/soLAk5OT0bdvX/j5\n+YkNOr506RJr++XLl8jJyUGVKlWU2mdxEhMTSz3wTlDz5s0p8QIhhEjJzs4OISEhrIAGFRUVBAQE\nlJgESJDg+MN/YCfqBb20MjMzhfbVqFFD7noVTfCBnuANXO3atbFu3Tqp6qpevToWLVqEWbNmsepK\nSUnBgwcP0KlTJ/k7TAghFVCHDh2Yn3k8HivbtSJxOBz4+vpCT08Px44dk2mVc0IIIaXP2toamzZt\nQnx8PGu/uIQGlSpVQu/evWFubo7hw4ejZcuWSu1jpUqVMGTIEAwZMgQeHh5ISEjAhQsXcPnyZVy6\ndAmJiYli+965c2fMnTuXtc/e3h6hoaHYs2ePwp8LWlhY4J9//gHw8z7vxIkT2L59u0z1JSQk4Pnz\n56z7nFq1amHAgAEK6S8hhBDF6dOnDx49egTgxxiwYcMGNG7cuIx7RQghRBr5+flSr0ok6fH8xAv8\nQDJJJsgqwpgxYxAVFcVsf/78GSdPnpRohXBF2bVrF7KyslgxHrNmzSq19gkh5FcQHByMadOmCa08\nt27dOpibm0tUR3ETbrKysqCuri5Tv0QtLFG1alWZ6ioqMzMTxsbGiI+PZz0ba9myJUJCQiROIiTq\nvOWNy1DmeRNCyO+mWbNmJR7z9etXbN++HevXr0d6ejo4HA44HA7y8vJgb2+P2rVrY/jw4aXQW0II\nIcpUUtKFgIAArF+/npXcc86cOWjVqpVcK42vWLECp06dYt7L83g8LFmyBA0bNsSECRNkrtfIyAhG\nRkYylb1//z7MzMzw9u1b5lwnTpyIefPmwcHBAcCP+8Jq1aohJiYGkydPZuZKZWdnY/ny5Thy5Aj2\n7t2L7t27y3wOhBBCKoa3b9/i4sWLuHjxIqKiopCQkMCMaQBYzxQFY+vevHkDQ0NDhIeHw9DQEHXr\n1sXx48fRuXNnpfSTw+Fgz549qFatGjw9PTF//nx6X0TIL0Y4DTIpNQcPHmR+5l/wORwOsrOzMWbM\nGLEZ4Y4dO8Yqe/HiRZiamoqcoFoR8B8e8v8QQghRPgcHB/j7+wsFNHh6esLExESquqpXry60T9QL\nemkUFBSIDBKoXbu2XPUqg6amJmub/13a29vLtMrRhAkTRAZNFE28RAghvxNtbW1Uq1aN2f748SM+\nffqklLbq1auHuLg4dOnSRaH1ipr8SwghRDG4XC42b97MXGsFX64IPnNq06YNHBwccPz4cXz+/BlR\nUVFwdHRUetIFUVq2bIkpU6bAz88Pr169QnJyMo4fP46lS5di2LBhaNSoEdNvLpcLb29voedmw4YN\nw5o1a9CnTx+F909HRwd//PEHa9+HDx9w+fJlmeoLCwtjfubfM40YMQIqKipy9ZMQQojiLVy4EFwu\nFxwOB4aGhkzgGyGEkIqBx+PB2dkZVatWleqPnp4eK3itOPzEC3yllXjBwsJCaGLq3r17S6Vt4MdK\n6tu3b2cFxJuZmaFVq1al1gdCCKnoTp06hfHjx6OwsBDAz2dE06ZNw/z58yWuR/B9kSB5khAoKzYh\nNzcX5ubmuHbtGis2o3bt2jh16hTq1q0rcV2izlvexAsVJSaDEEJ+FTVr1sTixYvx8OFDtGvXjvUu\nq7CwELa2tvjw4UMZ95IQQogynT59GpMnT2a9++fxeNi4caPUz/OK/qlVqxYr6QL/70mTJmHbtm2l\nfq7BwcHQ19dnJV2wsLDA7t27RR7fsmVLREVFwdPTk4nL5nA4ePjwIXr16oWFCxfKHZtOCCFEWFnP\n43z69CkmTZqEVq1aoWnTprCxsYGPjw9evXol9N6qaDxgq1atMGPGDOjp6QEAdHV1ERkZiZs3byot\n6YIgd3d3BAYGlulC5oQQ5ZAsXTJRisDAQNZNTYcOHRAXF8cMWM7Oznj+/Dl2797NCiCIj4/H/fv3\nWQMbh8PB+fPnMXToUISHh4ucAFsaZB1sBQdBWZMvlPVATwghFcmcOXOwc+dOoaQLa9aswbRp06Su\nT0tLS2ifvC+BiivfoEEDuepVhqKJF/iGDh0qc30dOnTA3bt3WePbkydPZKqPEEJ+Fe3bt8eNGzeY\n7YcPH6J///5KaUswcU50dLRCV+MWnBSsqP77+/tj3LhxCqmLEEIqqsGDB8PIyAinT58Gh8OBiooK\n2rdvD319ffTr1w8GBgbIycnBf//9B+DHM7bypmfPnqyV/dLT0xEfH48vX74wL4gEcblcLFq0SGn9\nMTc3x+PHj1n3JcHBwejbt6/UdQkmXuCztLSUq3+EEFLeNW/eHImJiWXdDUaHDh3w4MGDEo/T0dHB\n8OHDceHChVKd0EoIIUQxlP3e/MWLF6zt0kq8oKWlhUGDBiEyMpKJszhz5gxSUlJQv359pbd/7Ngx\nJCUlsb7fefPmKb1dQgj5VZw/fx6WlpbIy8sD8DNGwdLSUuoJQOrq6lBVVWXq4ktPTxcZuyCJL1++\nMD/z+1anTh2Z6uLLy8vDqFGjcOHCBVZsRrVq1RAWFoY2bdpIVV+tWrWE9qWnp8vVR8Hz5pP3vAkh\nhJSsSZMmuHDhAtq3b4+0tDRmf0ZGBtavX4/NmzeXYe8IIYTIorCwEFyu+HVxjx49ChsbG9Z9EZ8i\nn+lNmDABhw8fRm5uLvMcbfbs2UhOTsa6desU1k5xMjMzMWfOHPj4+LDmS40YMQKHDh0q8VynT58O\nU1NTWFtb4+rVqwB+LOa3efNmpcUKEkLI76SgoIC1XaVKFdb206dP5Zq30rt3b6mSjSYlJWHfvn1C\n80kFkyzwt7lcLoYNGwYjIyMMGzYMOjo6rLrS09OLTfCjTJ06dZL6WR8hpHyjxAtlJCQkBMnJyczF\nv23btrh58yZGjBjBBIdzOBz4+fkhKSkJx44dY5Ip7N+/n6lHMBMdh8PB1atXMWTIEJw5c0bhyReK\nDqxFb3gMDAyEjpHEzp074eDgwNRnYWGBI0eOyN5RQgghYjk6OsLT01Mo6cKSJUvwzz//yFRno0aN\nhPZlZWXh8+fPMgc28CdDCVJTUyuV4DlpFTfmyrPKET/xgqDU1FSZ6yOEkLJy8eJFWFlZKaSuomPD\nP//8o5DA7kGDBmHKlCklHqeIF1xFVwykBHKEEKJY7u7uOHHiBHR1ddG9e3eoq6uzPp81axa8vLzK\nqHcl27RpE5ycnJjt6tWro1evXmXWH3NzcyYjN/8Z5IkTJ+Du7i5VPd++fcOlS5dY417NmjVhaGio\n0P4SQkh5I2ui6fJg7ty5MDc3R+PGjcu6K4QQQqTEf+9T3GdA8c+kij67EuXGjRvM/UHlypXRrFkz\n2TsrpbFjxyIyMpLZLigowKFDh+Do6Kj0totOfPrrr7+gr6+v9HYJIeRXEBUVBXNzc+Tk5AD4OVaZ\nmppKNOlGlLp16+Ldu3esfYmJiTK/N0pKShLap62tLVNdAJCfnw9LS0ucOnWKFZtRtWpVhIaGikyy\nWpJ69eoJ7ZM32V/RpEKAfOdNCCFEcg0aNMCKFSswd+5c5jkij8fDwYMHKfECIYRUQBcvXoSTkxMc\nHR1hZWUFVVVV1ud79+7F//73P9aCPYrGv9caPHgwBg4cCFtbW2Yfh8PBhg0bkJSUhN27dwvFUiiS\nt7e3UNKFiRMnYvfu3RLf/zVr1gzR0dFwcXHBmjVrUFBQgBkzZsDY2BiBgYFK6zshhPwOMjMzWdtV\nq1ZlbQcEBGDlypUy1c3hcBAaGgpjY2OJyzRt2pT5WfA9lqgxQ1VVVeTiP3xZWVnw8/OTosfy449z\nlHiBkF8LJV4oI66urgD+z959h0dR7X8c/8ym0UKRXqVJUQGRqtQfRXokGIpU6aAXLpdLERG4QYEI\nKETQC4JSBEOT0BREyhUUDVJUQKQjCEgRgVDT9vcHzy4ZNiSb7IYl2ffrefYxMztzzneCz5ycOWe+\n517npk+fPvL399eqVasUHBys9evX2xuJLVu2qF69eoqKilJcXJzmzp1r6oRMnDhRoaGhun37tgzD\n0M6dO9Ml+cKtW7dMMWfJksVtZQMAHo5///vfCg8Pd0i6MHToUL311ltpLrdkyZJJ7v/999/TnHjh\n+PHj9p9tHShXEhmkp6QST0hSYGBgmstMarUKEi8AyGisVqtOnjypkydPuq1MW1/IarVq586d2rlz\np8vlBQYGOpV4Ib0GvFyVUV/kAoD08MQTT2j48OEpHse90zm1a9dWgQIFdPHiRfu+06dPa/fu3apW\nrZrT5Xz99dem1TQMw1BQUJB8fXk8DcB7pEd/whm2e29q1atXT/Xq1UuHiAAA6c22enirVq1M+8PD\nw+0JnwMCAjRr1izT96dOndLYsWOT7S/9+eef+v333+3HVKlS5aH+Xd+2bVv179/ftCrgwoUL0z3x\nwjfffKOoqChTn+bf//53utYJAJnFtm3b1KZNG92+fVvSvTkKTZo00fLly+Xj45Omch9//HGdOXPG\n1G65koQgqXPvXzHPWfHx8erQoYPWrl1rmpsREBCglStXqkGDBmkqN3FCBFub5Mo1x8bG6vz586Z9\nFovlgfM/AADu9/LLL2vo0KGm53cXL17U/v379fTTT3swMgBAau3evVu//PKLevbsqX/961/q3Lmz\nZs6cqdjYWI0YMcJh7rbFYpGvr69iYmIk3V2UYdmyZWka09m8ebOmTJli2tetWzdduXJFQ4YMMSVf\niIiI0K5du7R48WJVr1492XL//PNPWa1WFS5cOFXxDBs2TKdOndLMmTNlsVg0btw4jRkzJsljbb+T\npJ5JGoahcePGqWnTppo8ebKmTp2a5LkAgNSxJV6wtQ/3J16wSdxupSSt8xIkmRJ8G4YhX19f1alT\nR61bt1bdunVVu3btdL/nJy7fU3M7ADxamNnqARs3bnRYBaJHjx6S7mbeiYyMVLt27fTll19Kunvz\nrlixorJkyaKJEyfq77//tt/Qq1WrppEjR6pixYoKCQlRXFycPflCs2bNtGnTJmXPnt0tcdsGwGwe\n1LACAB5NI0aM0LRp0xySLgwfPlxhYWEulV2wYEHlzp1bV69eNe0/ePCgnn322TSVuX//ftO2YRh6\n5pln0hxjenrqqadksVgcOllXr15NcxKkpBIcuatNB4CMLPG91pUHdanh7++vQoUKuVxOfHy8Ll68\naJqgnSdPHocM52lB/wwAUu9RGSRJ3C48agzDUKtWrTRv3jxTfCtXrkxV4oU1a9Y47AsJCXFLjACQ\nEdju80WKFHEpUWdqXb16VefOnXtofScAgOc89thj6tq1q327U6dODqsJRUZG2hMv+Pr6qnv37qbv\nT58+rcOHD9u3n3zySYd6fvjhB/vPhmGoZs2abonfWbly5VLz5s21Zs0ae/v2yy+/pPtLSZMnTzZt\nP/7442rfvn261QcAmcV3332n1q1bOyz206RJE61Zs8al8ZGyZctqx44dpn2JF3dIrWPHjjnsq1Sp\nUqrLiY+PV6dOnbRq1aokky40a9YszTEmtVDFmTNnFBcXl6ZESCdOnFBCQoKpz1iuXDn5+fmlOUYA\nQOrkz59fpUqVcmjDTp8+TeIFAMhgdu3aJenuM7OrV69qy5YtOnLkiDp16qS9e/c6JF2YM2eOxo4d\nq7Nnz9rfKWratGma6k68kEJigwYNko+PjwYNGmRKvnDkyBE9//zzCg0N1ahRox5Y7vLlyzVs2DB1\n7NhR/fr1U926dZ2O6f3339etW7cUFBSkNm3aJHnMe++959T89eeff16rVq1yum4AQPKuXLli2s6R\nI8cDj30Yc9qyZs2qcuXKqVatWmrVqpWaN29ufwfn/veTnJGWeO+fm57e9QF49JF44SGLjY3VP//5\nT9OE6j59+phWA/f399fKlSvtyRfy5s2rDz74QNHR0Zo+fbrp3EGDBkmSgoKCNG/ePPvkCMMwFBUV\npTZt2mj9+vUKCAhwOfa///7btM2LPQCQcYwaNUpTp051SLrw+uuva8KECW6po2rVqtq6daup4/Dd\nd9+pS5cuaSrP9hAysRo1aqQ5vvSULVs2lSlTRkePHjXtP3PmjIoXL56mMi9fvuywL1++fGkqCwA8\nKb0eKFmtVrc90EupjOeee05nz551uZ5jx47piSeeMO2LjIxkFVkA8DBPDn5khJdgbc8dbaxWq1at\nWuV0XzIhIUHr1q0z/Z5z5sypF154we2xAsCjLjw8XO3atXto9S1YsEA9e/Z8aPUBADynePHiWrhw\noUtl5MiRI8UybC+42p7L1apVy6U606Jjx44Oyd0WLlzokBzBXQ4cOKANGzaY5mmMGDFCFoslXeoD\ngMzi+++/V8uWLXXjxg1J99qOZs2aKTIy0uW5bM8884xDuxUVFZXm8n744QeH54SpSTwq3X0O1qVL\nF33++eemuRlZsmRRZGSkS0kXJKlkyZLKlSuXrl27Zt8XGxurPXv2pCkZUuKEStLd56SpvWYAgOsK\nFSqkY8eOmdqhxPd6AEDGsHv3bodFF6pVq6br1687JF2YPXu2evbsqbFjx6Z7XK+++qpy586t3r17\n686dO/bkC/Hx8dqxY0ey8++ioqIUGxurRYsWadGiRapTp462b9+e5LFJzX2YM2dOsrEFBwdr48aN\n9u0bN26k6h2lR3WBCwB4VM2YMUMdOnTQqVOnTPuLFi2a5PG2++wff/yhwoULP7Dc1atXKzg42KXY\nfvvtN5fOtylYsKDi4+NTdU5oaKhCQ0Ml3X0+NnLkSE2cONEt8QDIuBgJfsgmT56sQ4cO2bezZcum\nMWPGOBxnS77QqlUrzZgxQ3nz5tXIkSN16dIl+zEFCxZUx44d7dtdunTRzJkz7Z0WwzD0v//9T+3a\ntUt1o5GU+180KlCggMtlAgDS3+jRo/XOO+84JF1488033ZZ0QbqbUTQxq9Wqb7/9Nk1lxcfH69tv\nv3V4INakSZM0x5feqlSp4vDg0JWJHSdPnnTY547V1gHgYTIMQz169FB8fLzbPi+++KKpzzN+/HiX\ny5w9e7bHfkcZ4YVbAMjMbBMKPPFxZQW+h6lp06b2ifC2Ptpvv/3mkHjuQb799lv99ddfku71R9u0\nacPKeQAAAEA6uHbtmlavXp2q+QE3b97Uv//9b5UoUUL79u1L9tg1a9aYxm48kXghKChIWbNmNU2s\n/vXXX9OtvqlTp9qTwEp350mQ2AgAkhcVFaUWLVro+vXrku49E2rZsqVWrVrllgWEEicasL3cdH8i\nAWedP3/eYXy+aNGiKl26tNNlWK1WdevWTcuWLTPNzciaNavWrFnjctIFmxo1ajiMLX333XdpKuv7\n77932NewYcM0lQUASLtbt2457Eu8mB8A4NF3+fJlh/H/vn37qkyZMvZtq9UqHx8fzZo1S717936o\n8XXu3FlfffWVHnvsMXsslSpV0pIlS5JNXLBz5057ogbDMFSsWDGHYwzDUL58+eyf3LlzOx2XLfGQ\nrWxnky4EBATY68ubNy8LygJACq5cuaLg4GANGTJE586dc0i8kNJCo8xzBuCNSLzwEO3atUtvv/22\nKZPdv/71LxUsWDDJ4/39/bVq1Sp17NhRO3bs0OzZs03nvvXWW/L39zedM3DgQIWGhpoatQ0bNqhX\nr14ux3/27FlTx+pBGY0AAI+OsWPHatKkSQ5JF8aNG2fPyuYuiZMi2Oo7cOCADh8+nOqytmzZ4pC5\nu1SpUqpQoYJrQaajVq1aOez74osv0lRWQkJCkitq1KlTJ03lAUBmYrvf2vpGK1eu9HBEAABkbtmy\nZVP9+vUdBtEiIyOdOn/16tUO+0JCQtwSGwAg87H19Ro2bCiLxeLS5/4xNADwBrNnz1ZwcLAef/xx\njR071rSwQ1JOnDihp556StOmTdPNmzfVoUMH3bx5M8ljDx48aBrzKVGihJ544gm3xu+M7Nmzq1Wr\nVrJYLGrRooU2bdqkdevWpVt9vXr1UsuWLWWxWGQYhoYOHeqWF4YBILPatWuXmjdvrujoaEn35ii0\nbt1aK1eudNvf6TVr1lSOHDlM+65evZqmJASJx/Vt8TZv3tzp861Wq3r06KGIiAjT3Ixs2bJp7dq1\nbl1gonHjxg77vvzyy1SXY7VatX79etOcBMMw9MILL7gUHwAg9U6dOuUwR4zFeQAgY9m5c6fDvsaN\nG2vFihUKDAyU1WpV9uzZFRkZqT59+nggQql+/fravXu3nn32WWXLlk1Lly5V9uzZH3j85cuX7Ysx\n2OYK1K9f3+G4bNmy6cKFC/bPZ5995lQ8Fy9eNCWrqF69utPX0rZtW1Odffv2dfpcAPA23377rapW\nrWqfv3XhwgX99ttv9u8Nw+D9UABIgq+nA/AWf/31l0JCQhQTE2PfV6JECY0YMSLZ83x8fHT9+nWH\nxAmVKlV6YDKFMWPG6NixY1q4cKF9gtqiRYtUqFAhFShQIE3xx8TEOGThSypjXVqQ+QgA0sd//vMf\ne8If6d4EgfHjx2v06NFur69u3boqUKCALl68aNr/0UcfaerUqakq6+OPP7b/bIu7S5cubokzvQQH\nB2vAgAGKiYmxt79btmzR8ePHU7UShiStXLlS169fNw2q+fn5qV69eu4OGwAynFatWpnujz/99JMO\nHz6scuXKeTAqAAAytxYtWujrr7827Vu9erWGDx+e4rmrVq0ytd05cuRI1cR1AIB3Sm6FJWcw9gTA\nG8XHx2vmzJkyDENnz57VpEmTUpzI/fjjj6tgwYL6/fffZbVadfjwYQ0ePFhz5851ODZxAlTDMPTi\niy+6FG9MTEyaX74dPXq0QkNDVbFiRZdicEa9evVUr149HTx4UDNmzNDAgQPTvU4AyKj27t2rZs2a\n2RdZsI31BwUFafny5fL1dd9URV9fX7Vo0ULLly839R/mzp2b6gUNPvnkE4d9nTp1cvr83r17a9Gi\nRaa5GdmzZ9e6devUoEGDVMWSkqCgII0aNUrSvcR1W7du1cmTJ1WyZEmny9m0aZP9RV/bv9Pzzz/v\ntvmAAADn7Ny5U5cuXXIYRylfvrwHowIApNb9iRdy5MihSpUqyTAMTZs2TWPGjNG6detUtWpVD0V4\n1+OPP64dO3Zo586dKbY1SSWTsCVeOH/+vObPn+9SLIcOHbL3RSQpOjpa77zzjktlSlLr1q311FNP\nuVwOAGQ0CQkJpu07d+5o1KhRmjJlimns/OTJk9q/f7/9mVCZMmXk5+f3sMMFgEceiRcegjt37igk\nJMQ0WGGxWDRv3jyHzNtJ6datmw4fPmwa6Hj33XeTnXQ2Z84c/f777/rmm2/s+/7+++80J1749ddf\nFRcXZ6/T19dXZcuW1bvvvuvUBGtn2BryFStWyGKxuFyeYRiKj493uRwAyIjeeustjR8/3iHpwoQJ\nE/T666+nS50+Pj7q3Lmzpk+fLsMw7O3WJ598ouHDh6tgwYJOlfPbb79p5cqVpnbO19fXY1lenZUz\nZ061aNHC9FKR1WrV8OHD9fnnnztdTkJCgunhoe3frlWrVsqaNavb4waAjKZQoUKqV6+etm3bZt83\nd+5cTZ482ekybt26pV27dpHQBgAAJ7Vs2VJDhw6VdG9Cd1RUlC5duqR8+fI98Ly9e/fqxIkTpuea\nbdq0YQVyAAAAIB0sW7ZMp0+fto/RtGjRQiVKlEj2HIvFovnz56tq1aqKiYmR1WrVvHnz1LJlS7Vr\n18507NKlS01/27uSeCEhIUFFihRRt27d1K9fv1QnUKhSpUqa606rihUr6sMPP3zo9QJARvHzzz+r\nadOmunLliqR749zBwcFaunSpfHx83F5n165dtXz5ckn3nlktX75cEyZMUJEiRZwq48cff9SOHTtM\n8xNKly6tRo0aOXV+3759NX/+fNMcgRw5cuiLL75Il3GoihUrqmrVqtq7d6+pzmnTpik8PNzpcpI6\nllViAeDhCwsLs/9sazubN2/Oi08AkMH88MMP9p8Nw1DNmjXtf6/36tVLQUFByY6rS3eTlNpWI0+t\n3bt3O32sn5+fU8nqEl+TJOXJk8ee0OCPP/6wJ4RzReJ+2ObNm7V582aXyytcuDCJFwB4pb/++kvS\nvXtr165ddfHiRfszM0nKli2bzp8/b38/1DAMVatWzWMxA8CjjMQL6ezOnTsKDg7WN998Y5qEMGjQ\nIDVs2DDF80NDQ7V69WrTuX379lXjxo2TPc/Pz08rV67Uc889pyNHjqhdu3aaPXu23nvvvTRdx65d\nu0zbFSpUME2QdufKQ66WdX95AOBtJk6cqHHjxjkkXXjnnXc0bNiwdK178ODBmjFjhilj3tWrVzVw\n4EDTSkgPkpCQoL59+9o7c7bYO3furOLFi6dn6G4xYsQI+4NPW/yrVq3Sp59+qm7dujlVxvjx47V7\n926H9nD06NFujxcAMqrOnTtr27Zt9nvt/PnzNX78eGXJksWp84/oeXfyAAAgAElEQVQcOaIGDRqo\nQoUK6t69u1566SU98cQT6Rw1AAAZV7ly5VSqVCmdPHnS/twtISFBa9euVc+ePR94XlJJ6EJCQtIt\nTgBAxmd7HlinTp0UJyGmxJ2r6QJARjBt2jTT2MrAgQOdOq9ChQoaP368Ro4caT//1VdfVYMGDZQ3\nb15J0v/+9z/7CkiSlDt3bpdW8L5x44YuX76s8PBwhYeH68UXX1RkZGSaywMAeNa+ffvUpEkT/f33\n35Lu/V3/0ksvKSIiIl2SLkh3VzItXbq0Tpw4Yd9369YtDRgwQGvWrEnx/Pj4ePXr189hbsWIESOc\nqn/AgAH6+OOPTecHBgbqyy+/dOpFprQaPHiw/Zmcre3+8MMP1bVrV9WoUSPF8z///HN9+eWXpjkJ\nxYoVU+fOndMtZgDIjMLCwtS/f3/lyZMnTefPmzfPtMCPTY8ePdwRHgDgIdq5c6fpudzzzz9v+j6l\n8Q7DMHTt2jUFBwenOYbEL9a6w/bt201l161b121lJ+5D3b/PhveBACB19uzZY//ZarXq0qVLprbp\n2Wef1eLFi7Vw4UL7Me5KvMA9G0BmZPF0AJndtm3btHHjRtO+qlWrmrKUPsj777+v0NBQUyeiQoUK\nmj59ulN158mTR19++aXat2+vzz77zKWEBps2bZJ0r2GtWrVqmssCAKSfd955R2+++abDxICpU6em\ne9IFSSpZsqT69u1r7zzZOmurV6/WwIEDTQkZ7hcXF6dXXnlF3333nanNyp49uyZMmJDusbtD7dq1\n1blzZ4fr79Onj5YuXZri+VOmTNH48eMd/v3atm2rZ599Nl1jB4CMpFOnTsqePbt9+6+//tLHH3/s\n9PknT56UJB06dEhvvPEGExcAAHBCixYt7H0UwzAUGBioS5cuJXvOihUrHPp3LVq0SO9QAQCZwNtv\nv62VK1e69Fm2bJmnLwMAHppt27aZFlMoW7Zsqv72/ve//60aNWrYxzcuXryof/zjH/bv33//fUn3\nxi06duwoiyXt002io6Mlyd6/OHXqVJrLAgB41oEDB9SkSRNdvnxZ0r22on379lqyZEm6JV2Q7rYj\n48aNcxif/+KLL1Jc2CAhIUG9e/fWzz//bNr/5JNPqlevXinW/dprr+mjjz5ySLqwYcOGdE26IEld\nunRR+fLlTdedkJCgDh066PDhw8meGxUVpf79+ye5kEd6/lsBQGa0dOlSPfnkk/r0008VFxeXqnOn\nTZvmkPxHkl544QW1bNnS7bECANLPoUOH7P0hm+eee85D0SRt+fLlOn36tNPHx8XFKSoqypTMoV69\neqZjbM/10vKxWq2muQeJ60ncz0ntBwC81a+//mpa+PP+5z5Dhw7V999/r3LlytkXB7epX7/+A8u1\n3Z+LFSsmi8XywE+7du24DwPIdFjqJZ01bdpUderUUZcuXRQbG6vixYtr3bp1CggISPa8Dz/8UEOG\nDDE1dgEBAYqIiHB6FVdJKl26tJYsWeLSNSQkJGjLli2mDk3Dhg0l3U0ikXjCRVocOHBAW7dutV9r\nmTJl1Lx5c5fKBABvFB4erlGjRjl0lJo3b65y5crpiy++cLmOVq1apXjMhAkTtGbNGp09e9b0MGv2\n7Nn65ZdfNGrUKLVs2dI+IS8hIUEbN27Um2++qT179jjE/95776lIkSJOxXf58mV9//33yR5z4cKF\nJPc78/tp0qRJim34O++8o3Xr1unq1av264+Li9PLL7+syMhIDR482JTNNj4+Xlu2bNHEiRP1zTff\nOHQ6S5QooTlz5qQYGwB4k5w5c6pLly72CW1Wq1VhYWHq3bu3U/2l48eP2382DEOVKlVKz3ABAMgU\nWrZsqS1btqhVq1Zq2bKl6tevn+xk7J9//lmHDx82ZU9v3bp1in0qAAAAAKk3adIkSffGVoYPH56q\n8y0Wi2bMmGGfFG61WrVs2TL17NlTFStW1Nq1a03zBV599VWX4r1/MnrRokVdKg8A4BmHDh1SkyZN\n7Mk5be1QrVq1NHbsWB05csSl8nPlyqVChQole0y3bt00d+5cbd++3fTCzqRJk3Tw4EGFhoY6jANF\nRUVp9OjR9vlwttj9/Pw0Z86cFBMQ/POf/9R///tf07k+Pj4KDw/XY489pkOHDrlw1XcTKCUXg6+v\nr2bOnKkXXnjBtP/3339XnTp19Pbbb6tHjx6mMbMbN25o9uzZGjdunG7evGmP2zAMtWzZUp06dXIp\nZgDwpGvXruncuXPJHpNUIulLly6leM8uXLiwcubM+cDvL1y4oB49euiNN97QgAED1Lx5c1WtWjXJ\nRHXXr1/XmjVrFB4erh9//NFhjljevHk1c+bMZOMBADx6vvvuO9O2YRiqXbt2qstx9YXV5FYbf+ut\nt3Tw4EG1bt1avXv3VuvWrZMta8+ePbp586Ypprp169p/rlatmuLj49MUZ3h4uP71r3/Z+25lypTR\nRx99pMaNG0u6+3sIDg7WihUr0lQ+AHgbq9WqwYMH25/z2PZJd/sYCxYssCd3279/vw4cOGA/rkCB\nAqpRo0aKdaTURtkS6gBAZkLihYcgJCREktS3b1+tXbs2xQEh6W6WOD8/P8XGxkq6O9Fh3rx5qly5\ncrrGmpRNmzbp0qVLpoayWbNmkqRGjRqpUaNGLpU/e/Zsbd261b79zDPP2FfMAAA4b9WqVQ6dJavV\nqvXr12v9+vUul28YhlMPyvLkyaOIiAg1bdpUMTExpsx533//vYKCguTn56cSJUooS5YsOnnypG7c\nuGE/xha3YRjq06eP+vTp43SM+/btU5s2bZw6NnHnzmq1pnieYRg6ceKESpQokexxRYoU0Weffaag\noCDFx8ebrn/ZsmVatmyZAgMDVaxYMcXFxenMmTP2iQ33//sFBgZqxYoVeuyxx5y6JgDwJkOGDNHc\nuXPt98yzZ89q6tSpevPNN1M8d//+/ZLutTckXgAAIGUtW7ZM1SpLERERDvtsz0kBAAAAuM/69ev1\n1Vdf2ScrFyxYUN27d091OTVr1lT37t21YMECGYahXr166fnnn9eQIUPs4x2GYahu3bp6+umnXYr5\nr7/+Mm2TeAEAMqYlS5bo/PnzDquT/vDDDy63FZL0yiuv6JNPPknxuEWLFql69eq6ePGiaXGIVatW\nadWqVXriiSdUunRpWa1WHTt2TMeOHZPkOD9hypQpTr0gNWPGDNM1S3cXXOjVq1daLtPE2XkJjRs3\n1ogRI/TOO+/YzzMMQ5cvX9bAgQM1cuRIVahQQYULF9bZs2d18OBBXb9+3WFOQtmyZbVgwQKX4wYA\nT4qMjFTPnj2dOjZxezVz5swUEx3Mnz8/xf6VYRg6e/asxowZozFjxihXrlwqXbq08ufPr8DAQF27\nds1+L05ISLCfkziewMBAbdiwQWXKlHHqOgAAj44dO3aYtsuVK6fcuXOnqgyr1ap8+fI9cGG5lCxe\nvPiB7ZXVatXRo0eVkJCg1atX68svv9SVK1eUNWvWB5b37bffmrazZs2qatWqpSm2xHbs2KERI0aY\nFm+YPHmy/u///k/t2rXTypUrJd1t28PCwvT666+7XCcAZHbXr183JZSz3V9r1KihFStWqFixYvbv\npk+fbv/ZMAynFmV1JjGQq8mDAOBRROKFhyQkJEQNGzZUvnz5nDp+8ODBeuaZZxQUFKTo6GhNnjzZ\nY5ml7x9cqVKligoXLuyRWAAAybN1lNzdeUltBrp69eopIiJCXbp00Z07dxziiouLc1htPHE9hmGo\nZ8+emjVrVpri9fT1t2jRQhEREerWrZtiYmJM1yXd7eD+9ttv9uOTuv6iRYvqiy++4GVgAF7FarXq\nq6++Us2aNVNMOlOhQgV16NBBS5YsMa1e1KlTJ5UtWzbZc/ft22farl69usuxAwAAs+XLl5v6Ztmy\nZUtV4gYAAAAAKYuLi9PQoUNNk5VHjhwpf3//NJUXFham77//XtOmTVOLFi20f/9+eyIGW/n/+Mc/\nXI777Nmzpu3ixYu7XCYAwLM8OcG6ePHi2rBhg1544QVdvnzZYXz+6NGjOnLkiH1fUuPzb731lgYP\nHpyqej09L2HSpEm6ePGi5s2bZ7oWwzAUHR2tnTt3mmJNnCzCMAw98cQT+vrrr1kIAkCm4Ym2KPG9\n21b/tWvX9NNPPyX5XVJtULly5fTZZ5+patWqDytsAIAbbd++3fTs7LnnnnvoMTRp0kRff/21fTtx\nIrzjx4/r9u3b9j5BlSpVkk26IEnbtm2z/2wYhmrVqiVfX9dePdu7d69at26tuLg4+++qZcuWCg4O\nliS999572rRpk6Kjo2W1WjV69GjlyZNH/fv3d6leAMjsAgMDtWDBAjVt2tR+f+3Xr5/ef/99+fn5\n2Y+7cOGCIiIiTG1W586dky3bdtzcuXOVN29ep2OqUaNGmq8HAB4VJF54iJxNumBTv359bdq0SWvW\nrNHQoUPTKarknTlzRp9//rmpYU3L6hgAgIcntYPx6SU4OFhbt25Vt27ddOzYMafiMgxDgYGBCgsL\n04ABA9Jc96PwOwgJCVGpUqXUvXt3e5KFlOKyDa699NJLmj59uooUKZLucQLAo+DEiRP65JNPNH/+\nfJ09e1anTp1y6rzx48dr5cqVio2NlSTdvn1b3bp10/bt2x842HPjxg399NNP9j6Or6+vnn32Wbdd\nCwAAuLtSxYkTJ0zPFFu2bKksWbJ4OjQAAAAgUwkPD9ehQ4fs4wvFihXTwIED01xewYIFdfDgQXt5\nw4cPV3x8vH27XLlyeumll1yO+48//pB0b9IeiRcAIGN7FMbnq1atqqioKHXq1Em7d++W1WpNMq77\nX4LNnz+//vvf/9pf9nHWo3DNkjR37lyVK1dO48aNMy0Kcb/7k1GEhIRo9uzZqV6JFwAeZe6+N6eU\nyKF9+/Y6evSobt68mWL9939nGIayZs2q/v37a8KECYyfAEAGdf78eR05csTUZngi8ULBggVVsGDB\nJL/btWuXabtOnTrJlpWQkKBvvvnGNNZft25dl+LbtGmTOnbsqKtXr9r3FSlSRPPnz7dvlyhRQrNm\nzVLnzp3tv89XX31V165d0/Dhw12qHwAyu0aNGmnIkCGaOXOmZsyYoX79+jkcM3r0aN26dct+jy1f\nvrwaNWrkVPnNmjXjvRYAXsfi6QCQvOrVq2v8+PFuK+/KlSupOn7y5MmKiYmxb/v6+qpLly5uiwcA\n4F62jKTp9UmtWrVqaf/+/Zo+fboqVKiQbNlFixbVqFGjdPjwYZeSLjxK11+tWjXt27dPc+bM0bPP\nPpts2blz51bHjh313XffadmyZXROAWR6d+7c0WeffaYmTZqobNmymjBhgs6cOSNJypEjh1NllC1b\nVkOHDrUP8litVu3cuTPZFYm2bdtmT9RgGIZq1KiR5hUAAQBwxaMyOTs9fPrppw772rdv74FIAMC7\nZea2BgBwd3Wit99+2zQJety4cS4/67KNh6xdu1ZfffWVqfxJkybJYnF9msmxY8dM248//rjLZQIA\nPONRmqNQunRpRUVF6eOPP1aVKlWSLbdkyZIaP368Dh06lOqkC4/SNUvSiBEjtH//fvXq1UuBgYEP\nLNff31+tW7fWli1btHTpUpIuAMhUPHE/fuONN3TixAlNmTJFdevWla+vb4pl+vn5qXr16goLC9Pp\n06f17rvvknQBADKwb775xmFfahIvJG5vbIl83G3Lli2S7o0ZpfSS7Y8//mhKkCBJ9erVS1PdVqtV\nU6ZMUYsWLezvMVmtVmXPnl0rVqxwWD29U6dOGjRokGl8a+TIkeratauio6PTFAMAeIuwsDBt27Yt\nyaQLe/fu1bx580zjPa+99toDyypbtqyaN29u/7jaZ4mPj0/zuQkJCS7VDQBplfQSoMi0fvzxR9N2\nco3f4cOH9dFHH5ka1vbt2yt//vzpHSYAIA22bt3q6RCS5O/vr0GDBmnQoEE6fvy49uzZoz/++EM3\nbtxQtmzZVLhwYVWuXFlPPvmky3U1aNDApY5ZerBYLOrVq5d69eqlP//8U7t27dKJEycUHR2tHDly\nqGDBgipRooRq1qwpHx8fT4cLAOkqJiZGGzZs0PLly7VmzRr7gIhtkoFt0CQ1D8rGjh2r1atX21fi\ns1qtmj17tgoVKqSxY8c6HG97EdTWx2nSpIkbrsw5vPQEAI8eq9XqlpeGM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pAh0dHTRu3FjZ1SGEEFJK0GAcIYRIj2VZTJw4Ueq29Nu3bzLXYeLEiVIfq6OjgwMHDshU/pcv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4/isGUAAERtz549lhMUCi8AQMwMw1Dr1q3l7e0d67xlypTRgQMHEiEq5yXkw7tP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\nN2+usLAw83fly5dXhQoVnG6ratWqO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tLT07kAprIgHJiCFtoSQgjQp08fNG3aVOT87du3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\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Wi0UDBw50uNzYsWP17rvvZvm6AOBuUu7iHRAQIC8v+74elCxZ0ub4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\nuaImTZpo69atatiwYU5/FAAAAADZcO7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uqKiowMvLC4sXLy6xjPT0dN6xqqoqbyBRVLt2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\n8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yF2HzY+hQoV0sGDBzVkyBBt3bo1yUR6I0aM0OXLl22O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\n47HvJMUFpShv3IoQQoj0Pn36hLFjx3KeW3V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ZMll+opMsuJMnE0RE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WsW537f4cOHYWxsLFM927dvx969exWKhRBCSNHOnTvH/iz60PzZs2d4+vQpGjdurKrQVIaS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\nDSZJBAIBHj9+jGvXriEsLAxnz55FXFwcAHC+w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mrVqum7\n777T1q1b5efnp2PHjlndLLt27ZrmzJmjOXPm6Nlnn1Xjxo3l7e2tBg0acNMCgNMLCgrSqlWrrG7k\nDx8+PM52WbNmVZs2bRwQYdJFRUWpXbt2OnDggNXxlCxZUufPn5dhGDp37pwaNmyoffv2KVeuXDap\nd86cORo2bJhVkgfDMLRgwQK1b9/eJnUAwNMi5hwZEBAQ76QVR5szZ47mzZvn6DAAADbUpUsXdenS\nJdn7/fnnnypSpIhNY4mOjrbZhJ2jR4+qUaNGunfvntm+duzYUaNGjXrivl5eXho5cqQ+/PBD89pp\n/PjxqlevnmrVqpXgfhaLRZcuXdKff/4Zpw/QMAzVqVNHAwYM0FtvvZXhJiYBAOzr8Qk6jki6kJzB\nf02aNNFPP/2kVq1aqW7duvL19Y13u4ULF+r48ePmMRUtWlS//vqr3NzcEiz7hRde0MWLF2WxWJQp\nUyZdv35dOXPmTHD7oKAgPf/88woNDTVfa926dbzbMqgQQGyenp46f/68zcq7ePGifHx8rM7jo0eP\nVteuXW1WR1I0aNBAy5cvV6dOnST9265MnjxZOXLkiPceV3JFR0dbjX3LaNc3FStWVKdOnbRy5Urz\nOPz9/TVkyBCVKVPG0eEBcHLBwcHy8fHRDz/8YHWuLVeunIYMGZKqsi0Wi44fP64ePXpIUrz9VzVq\n1NCgQYPUunXrFJ3fy5Qpo/Dw8FSvTB4jKioqTjLUunXr2rxfMqG6k2r+/PnmohOGYWjDhg2JJn+N\nMW3aNEn/3h/s3bt3otc/tuDr66s9e/aY10cNGza0WglekqZMmaKDBw9q+/btypQpadM0Ll68qJ49\ne1r9Fvrggw9UsWJFc5sffvhBrVu3Vrdu3TR+/HibHhcA+ypcuLDmzZuniRMn6uTJk5Ienbu2bt2q\nbdu2qU2bNvLz81OpUqXi7BsSEqK1a9fq008/NZP+SP/20+TKlUvt27dX165dVb16dZvH/sEHH5h/\np9dEpwCAp1vM4gy2GpOdXA8ePDATrwFwLiResKPevXtr3bp12rdvn9kZcujQIe3YsUM+Pj6pLj+1\nFwr2utCwWCwKDAxUYGBgotvt379fderUSXSb33//XX379tXOnTvjrGzUoEEDLV68ONmTfAHgabN0\n6VJdunQp3vfCw8Otno8fPz7emzOtWrVS5cqVzef2yDad2L4xoqKi1KFDB23dulVhYWFmuY+vbFe7\ndm0VKFBA3t7eOnHixBOT/dhKr169tHnzZh08eNAchBY7CUNQUJD8/f3l7++vgQMHas6cOWkSFwCk\nV7Nnz1ZYWJjZRuTPnz/BgcTpWVRUlNq3b6/NmzdbtUkzZ85Up06dVK1aNXNy0IkTJ+Tj46Pt27en\nKgHPw4cPNWjQIC1btswqa7hhGPrwww/Vu3dvWx0eADx1nnvuOZUoUcLRYcSRJ08eR4cAAHCw8PBw\nvf/++5o5c6bWrVunt99+2yblnjp1Ss2bN9fcuXPVtGnTVJW1ZcsW+fr6WiVdqFmzppYtW5bkMiZO\nnKidO3fq2LFjMgxDkZGR6tSpk06ePKns2bMnum/svrbs2bPL19dX/fr1U9myZRPcp1atWnrllVfM\n6zMAQMZUvXr1ZPenRURE6Pvvv7eaVPX888/r2WeftUeIifrPf/6T5G2LFi2qI0eOJDgJ5+rVqxo7\ndqxVX+SUKVMSTbpw5coVXbhwwbyXVrNmzSdOOpowYYKZ/FySqlatqubNm8fZLqXJpi5fvixPT89k\n75cQWyaaApA6rq6uNu1/i4iIiPNavnz5HNLH1759e125ckXDhw+3Gqvw3//+V7ly5YqzONCJEydU\ntWrVFNVlsVhSNDnq7NmzeuGFF1JUZ3xu376trVu3Wr2W2Hl/1KhRCggIMNveqKgojRgxQtu2bbNZ\nTACQXEFBQXr99dd18uTJOGPYsmbNmqyy7ty5o6+++kqff/651euPr/ZtGIZy5sypzp07q1evXqpQ\noUKi5Q4bNkz16tVT48aNE7wWsFXShYwsKYntTp8+rfXr11tdC16+fFnvvvuuTWIoXbq03nnnHavX\nBg4cqHXr1pl1lihRQmvWrDH/XQQHB6tz587atm2bmch23bp1T6zrxo0baty4se7evWu+VrNmTXXr\n1s18/tlnn6lXr16KiIjQxIkTlT9/fvXv398mxwogbbz99tt6++23tXnzZvn5+VmNeV63bp02bNig\nLl26aNy4cSpcuLD27dsnf39/BQYG6uHDh5L+bYdcXFzUoEEDde/eXS1atFDmzJntEvPu3bv1448/\nWvVPrVq1Su3atbNLfQAAJOSDDz5w2OJ+33//vWrWrOmQugE4FokX7GzJkiWqVKmSQkNDVaBAAS1b\ntsxMutC/f38FBwc7OMJHN/XTSlJXmLh3756mTJmi2bNnKzQ01Gq/fPnyacaMGercuXMaRQ0A6duK\nFSt0+PDhJ25nsVisso/GMAxDpUqVskq8kBbiawtcXV3l6+urwMDAOJnBXV1d1axZMw0dOlSvvvqq\n3NzctHHjxjSNOSbu//3vf3r77be1dOlSrVmzRiEhIZL+vfFTvXp1zZgxI81jA4D05N69e1q8eLHV\nb/mBAwcqS5Ysjg4tWSIiItSuXTtt2rTJ6ljGjh2rwYMHS5K2bt2qV199Vffv35dhGPrxxx/18ssv\na9u2bU8cXBGfY8eOqX379vr111+tBm9kzpxZixcvTtEgZwAAAACO9X//93/y9fXV2bNnZRiGevTo\noUqVKqlkyZKpLnvIkCH6/fff9eabb6pNmzaaM2eOChYsmOxy+vbta17HxShYsKDmz5+v8+fP686d\nO7p9+7Zu3bqlW7dumX/fvHlTt27d0o0bN3Tz5k3duHFD9+7dsyrn0qVLGjBggD799NN46469bbly\n5dSvXz/5+vrKw8PjiXGfPXtW3333nebMmaPSpUvr8OHDJDwCgAzos88+S/Y+M2fO1Pfff28+z5o1\nqw4cOJCsJAiOklASBYvFog4dOujWrVtm+1itWjW1b98+0fJiFsaI6b98UjKm3377Tf7+/lZ9nrZa\nNfXu3bsaOHCgNmzYoG+//dZm9yCHDRumv//+W/PmzUvRbx0ASKqhQ4fq999/1/z5862SL/Tt21e5\nc+dWy5Yt4+yTnIUqUrrAhb1WmP3zzz+tJncahpHovajSpUurZcuWVhNev/zySx04cEB169a1eXwA\n8CRnz55VkyZN9McffyR5jHJ8ZXzxxRfavn27Dh8+rMjISPO9xxMBGIahGjVqqE+fPmrTpo3c3d2T\nVMeKFSs0a9Ys5c+fX+3bt9e0adPsNln2aTd48GBFRUWZ7bQkrV271mble3t7m4kXIiMj1bVrV61e\nvdr895UtWzYFBgYqd+7c5j4RERH6+eefzXg2bNigAQMG6OOPP06wntDQUDVv3lwXLlwwX8uSJYsW\nLVpktV2xYsXMf4cWi0VDhgzRf/7zH7311ls2O2YAaaNFixZq0aKFtmzZIj8/Px0/flyGYSg6Olr+\n/v5atWqVChQooL/++kuSrM5zJUuWVLdu3dS5c2c999xzdo/Vz8/P6nnZsmVJugAAAACnQeIFO3v+\n+efl5+en/fv3a/ny5cqXL5/53pgxY+xe/+jRo9W2bVtVqlTJ7nXFllinZULvxby+bt069e7dW9eu\nXbOaZOTq6qquXbvqgw8+UN68eW0fNABA7u7uSW6fgoODNXfuXPOGgouLi0aNGpXsOh9fmaZp06by\n8/PT6NGjZRiG8ubNq65du6pfv34qVqyY1baxs1Y//npsScmEnZjHM5bnyZNH1apVU7Vq1TRz5kxt\n3bpVGzZs0Ndff62HDx9q4cKFCWYmBwBnsWjRIt29e9fqnJzRbr6EhITorbfe0t69e60GaAwaNEjj\nxo0zt6tUqZK2b9+uN99805xcdOnSJb366qtaunSpWrdunaT6wsPD9cEHH2jKlCmKiIiwaufy5Mmj\nwMBA1alTxy7HCgBPq0aNGunbb791WP3Dhw+3ajMAAM7r7t27ZnI1i8Wi4OBgtWzZUt9//32SB2fH\nZ/PmzeY1iyR9/vnnOnr0qM6ePZvsFaFv3Lhh9dwwDAUFBSV79db4Vv+zWCz67LPP1Lx583gHBFss\nFj3//PNatmxZsq57oqKizGtPi8WiX3/9NUnJGgDAWbRo0SLOCtZpYfbs2Ro0aJBd6wgKCpKfn59V\nv92IESMyRNKFxx07dkyurq6qVKmSxo0bp3379lndf5s5c2ai+0dFRWnGjBlW/Yl//vlnovuMGTPG\n7IOUHiUVf+ONN1J9LDt27FD37t115coVGYah1q1b6+jRo8qePXuqyj137pwWLFigiIgI7d69WzNm\nzLCaJAwAtjZ79mydOnVKBw4cMCc7RUVFqWPHjsqTJ4+8vLwcHaLNJTT+Ij7vvfee1q9fb/Xa//73\nP4f2xQJwTjt27FC7du0UHBycrKQLly5d0t69e7V3717t27dP//zzj/le7KQ7sfu5smXLph49eqh3\n794qV65csuKMjIzU7du3ZRiGrl+/roULF2rWrFnJO1hIkjZt2mQ1hiMlkjq2ML4xI25ublq1alWc\ncfl58+bVli1bVKtWLT148EAWi0ULFy5U6dKlNXDgwHjLd3FxUdu2bXX+/HndvHlTkjR9+vQ4C3y8\n9tprmjdvnt555x3zN0nXrl1Vrlw5lS5dOkWfAQDbunz5su7fv5/k7cuUKaPVq1dry5YtmjFjhm7c\nuCHDMBQeHq7Lly9bnadq1qypbt26qXbt2pIeLYZ07ty5ZMVXsGBB5cqVK8nbr127Vt98841dEnYC\nAAAAGQEzApX4ZMzUTtSUHmXdHzZsWKrLSa5Dhw5p6tSpmjp1ql5//XWNGjVKr732mt3rNQxDjRs3\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JUH+I1Dfy7g/x+Xw0bdoU8fHxEv07X7FiBX7//XcAJffsX716JVE5d+/exbBhw8S+7+/p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\n61Y9//zzSa47fPhwde7cWZMmTdKKFSussjln5iCDVq1aJcpKnR1nmpC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7F69WqcP38e+/fvR926dXVqS506dTBr1ixs2LCBuzZ//nwEBQVpbEtaQELy+SMsLAwDBw5Eeno6\n0tPTYWNjgylTpmDTpk1azWEpOBFBaEdycjIXMFUVAwYMgL+/P0xMTDB27FhERUUhJiZGaZm6deui\nWrVqAIB3794hNDRU4X4AIN8vWdq/Vfz69OnTyMrKkvFl69mzJ+evJ8nSpUu5M0Rly5aFt7e3wna/\nfv0arq6uXF0jR46UmRsqGouovyEIQhoSXhCYQ4cO8dJDhgxRmj8pKQlubm4K32dZFp8+fZLJwzAM\n+vXrx72WzC++pk6nL/2wX7ZsWXTt2lVlOU15/fo13r17x01SjYyM0LFjR8HrAYDKlSvLXNP3Idv8\nrE9bcnJyMG3aNPz111+8h5uSJUvi77//Rs+ePbm806dPR1JSElxdXbmHnYiICLRr1w7+/v6FRgGX\nIAjVXLx4EcC3icuAAQP0Wt/9+/e5vr5WrVpqH3JSh8jISGRnZ3N9WPny5eX2+coQun/W1wSLHLwJ\ngihKpKeny0RY1ZaicJD87du3CAsL47VzxIgRBdgi9dHksxVaOE9be7rWu3z5cuzduxfJyclgGAa9\nevVCZmamxnaOHDkC4NtvdPTo0Tq1iyAI4ZDcuJVe5ypRooTahzIlN4oNDQ1lyk2ePBmDBg3Sup2H\nDx+Gu7s7l9bH+psYEl4gCIIonHTv3h3Hjh3D0KFDkZWVxe3jXLt2DT179sT58+dRtmxZtWylpqZi\n3LhxPIFlIyMjHD58WDCBsO7du3Oiroqey42NjVG6dGmULl0aZcqUkfm3VKlSOHLkCGJiYsAwDKys\nrLBp0ybUqFGDE1hQ954JgiCI/IFhGKxfv55zRGMYBkZGRrh582aREF4QGnmRkYRcwzx//jyio6N5\n9qZOnSqI7YKE9rkI4vtAn2I3yiLTCYW2ditUqICLFy+iU6dOePnyJTe3i4mJQdeuXXH58mU0bdpU\nLVsikQjjxo3j9i1YloWhoSH2798PKysrrdpHEATxvSI+oCN+Xlfluy0U+hinOnXqhLlz58LDw4Pz\nw7tz5w5cXFx4e03y8Pf3B/Dtc7C1tRW8fQRBEASftm3bKjz8amRkBGNjY5iYmMDU1JT7MzMz46XF\nf5GRkXjw4AGAvDFm4sSJqFWrFszNzRX+lSxZEubm5ihVqhQntlAYkD68DxR/4QV5vhpA3n2vXr0a\nffr0wciRI/Hx40du3hsSEoKDBw/KDdSrKS4uLti5cycSExPRrFkzLhCrpkgLSBgZfTt2aGZmhho1\naiApKYl73vjzzz9x8+ZNHDt2DPXq1dO4PgrcShCa8/TpU3Tr1k3t/AzDIDs7G76+vvD19VWZXzKY\ns7r2JTEzM0O1atVQvXp11KxZE7Vq1YKVlRVq164NKysruX5sderUQZ06dWSub9q0iRNeKFGihFLf\n3eDgYLi6unLpli1bqu3rS30NQRDSkPCCgHz69AlXr17lFrosLCxw5swZpWW6dOkCQPnhfWUd97Zt\n2+Dl5QUA8PHxwaxZs7j3kpOTeQ+5QJ6qq6STuLQTedOmTXHlyhWlbdaGwYMH4927dwDy7qd9+/Z6\nqUce+h74pDf7hBxshbLz/v17jB49Gjdu3OApNFaoUAFnz56VG5F+6dKlKFOmDObOncspAaekpMDG\nxgYLFizA6tWreYq9BEEUPnJzc3Hp0iXu/325cuXQqVMnvdUXFxeHiIgIAHn9lyaTOXUIDw/nXjMM\ng+bNm2tUXp8OH99bJA+CIAh56NIPFqXFqoMHD/LSZmZmeo+WIQRdunRRuLkjzbNnz9C0aVPe/ObL\nly9aR5RlWRbGxsY8e8HBwWjVqpXKsrpuuFWsWBGLFi3ClStXsH79evz0008a20hOTsaFCxe49hsY\nGGDkyJE6tYsgiKJH5cqVNRZ+k2Ts2LFcP1K9enVuTVAfkPACQRBE4aVv3744ePAgfvvtNy6iDQDc\nu3cPN27cUHtuMWvWLERERPCesT09PQVdj7O1tUW5cuXkCiqI/1U2vsTGxmLcuHGc6ALLsoiIiICz\nszNOnz5Nh4gIgiAKMf3790eHDh1w584djB07FkuXLi0yEVLbt28vN7KcNiQmJnJrQmLEa6CGhoYo\nU6aMznXs3LmTl7awsMCwYcN0tqsJubm5+OOPPzBlypQCiX5IEETRpHXr1oL1t9J4enpiwYIFAPL2\nj86fP49evXrppS5tsbS0xOXLl9GpUydERUVxY0VcXBwOHjyoNAiTJKtXr8a9e/d4c7vVq1ejb9++\n+mw+QRBEsePTp084f/4815+am5ujZcuW+PLlCy+fdDo3N1fmmiKMjIwUHmTVh8+Ym5sbzp07xwWF\nYFkWmzZtQrdu3dC/f3+5ZUQiEQ4fPsybw8yZMwdz5sxRWR/LsnBxcYGLi4tG7fzzzz/x+++/a1SG\nIAiiuLFu3Tq4uLjAxMSE96fNOQtPT09OeAEAZs6cqbGvdGFB3pyxuAkvSPvSqfLN69ixI+7du4cR\nI0YgKCgILMuiV69eSkUXjhw5guTkZLXb9PPPP8PMzAyDBg1CSkoKdu/erVa5SZMmca+VCS/UqVMH\nt2/fhoODAw4dOsTNZZ88eYI2bdrA398/3wSwCIJQ/wyLJnMWVf7c7du3R6VKlVCuXDlUqFABFhYW\nnF9d5cqVUbVqVZQvX17t+giCIAorJLwgIHv37uUc5RiGwadPn5RGI2AYBuHh4Vi6dKnMewEBAXj6\n9CkYhoGFhYXchamaNWvCwMCAc2yTHtzUORQjrbKmD1iWxc2bN3mbRPl1MKlBgwZ62+gDgOPHj8PG\nxoYX3cLMzAxhYWGFxvnk3LlzGD9+POLj43kPVVZWVjh37hwaNGigsOzMmTNRrlw5ODg48KLMb9y4\nERcuXMDevXvRrFmzfLkPgiA05/Lly4iLi+PGJWtra70uWl28eJEnRCC08MKzZ894aU0WE11dXXnq\ndbqSlZUFJycn+Pr68sZfExMTnaLgEgRBFFV0FU4oSkqhfn5+vLlNv379tBYkyG/UfQ6QjPouWVbI\n5whd7D148ICL/qQOXbp0QdeuXZGVlYXg4GCV+UuXLo3GjRtz6UOHDiEzM5N7puratSsqVaqkVdsJ\ngsgfJAUOhEIkEmHatGmYPHmyWsIxkvz999949uwZ14+MHTtWsHbJQyQSyVyjwzMEQRCFB2tra/j6\n+mLixIlgWRZly5bF33//jR49eqhV/vTp09yalHheMn78eMyYMUPQdtaoUQOOjo5alT179iwmTpzI\n25dgGAYlS5bEtGnTUL9+fUHbShAE8b1St25dva2pJSUlwdzcHNevX0fv3r31Ukf58uXVWqvRBCcn\nJzg5Oels5+TJk3BycuL5AQB5883WrVtj586daNGihU51vH37FqdPn+aNlb///rtMcA19s3DhQnh6\nesLPzw+HDh1Cw4YN87V+giAIVRTWAAg1atTApUuX0KlTJ84vw83NTe0DqyEhIVi3bh1vHBg+fLjG\nB14JgiAIYN++fRCJRNw+TGpqKiwtLRXmF48tT58+Rbly5dSq45dffsGtW7cEaa86GBsbw8/PD+3b\nt+cCp7Esi4kTJ+LRo0dy96tPnDgh4ycMKPfnUDefvHJFxceDIAhC35QpU0YQgc7iRm5ursw1eT5p\nRRlpn0F5vhrSVKpUCVevXsW8efNw/PhxHDhwQGn+hQsX4u3btxq37dSpU2rnZRhGqfCC9PdWokQJ\n7N+/Hz/99BMWLVrEnaFLTk7GsGHD4OLiorYgIUEQ2iN+Hs+v53LxHCAgIEDpfEuf0ByEIIj8hIQX\nBCInJwdbt26V2XhXxQ8//IBVq1bJXH/58iWePn0KIE8lW14eeW0Qo2gzXlpFLT+EFx49eoSkpCTe\nADdgwAC916tvvn79iunTp/O+c4ZhMHfu3EIhupCWlgYXFxds27YNAHgbdd26dcPhw4dRoUIFlXbs\n7OxQq1Yt/Pbbb4iLi+NsPHz4EG3atMGiRYuwcOFCmJmZ6fuWCILQkH379gH41j+NGjVKr/VduHCB\nl9aX8IL4fnR1aNOW6OhoDBs2DCEhIbyNKktLSxw7dgwdOnQokHYRBEEUFF26dNFJ7CwwMJA3ZlSt\nWlWIZumF69ev4+XLl7y5TUBAgFYCAj179sTFixeFbN53g7W1tVYbSuoi7TSza9cu3vvPnz9Hp06d\ndK7Hy8tL48PbBEFohqabLcrW86ZOnYrdu3fDz88PLi4uWL58uVqHYTIyMuDs7MzNHczMzDB9+nSN\n2qUp8qIoKItIThAEQeQ/dnZ2SEhIgIeHB/755x+1BY7j4uLg6OjIW+9v27YtfHx89Nxi9UhOTsac\nOXN4YqXidvbt2xc7duxAzZo1NbabmZmJBQsWIDY2Fr6+vgqjCxIEQXxvvHnzRm9OZuL50ZcvX/RS\nB8uyakeWzU8+ffqEGTNm4OjRozJjmbm5OVavXo1Zs2YJ8pls3bqVc0oGADMzM8GFlFTx999/w9PT\nEwzD4NGjR/j555+xdetW2Nvb52s7CIIgiir16tXD2bNn0bt3b2zZsgV2dnZqlcvIyMC4ceN4+2tN\nmzbFnj179NRSgiCI4ktOTg68vb3liqbJQ1uhgYLg559/xoIFC7B+/XqurfHx8bCzs5Pra+Dp6cm9\n1ve9FfbPjiAIgih45PkT6jN4YEEgDqIr6aORmZmpMjCGoaEhtmzZAldXV7VFoPIT6e9OkW/M/Pnz\n0bx5c4waNYo7L8ayLDZs2IDevXujS5cu+dFcgvguadeuncz/1S9fvsDc3BzGxsYa2Xr9+jXq1Kkj\nZPMEpSjN4QiCKF6Q8IJAHDx4ENHR0TxnN1UI3eFnZWVxrxUJKkjmUZZPSAIDA3npWrVq8aKHFlWc\nnZ3x4cMH3vdYo0YNLF68uABblcfly5fx+++/IzIykncomGEYzJ49G+7u7hpNXDt37oyQkBBYW1vj\n3r173G88OzsbK1euhL+/PzZt2oShQ4fq65YIgtCQ9PR0nDhxgusDLC0t0b17d73WefHiRa4+U1NT\nbNmyRaPy7u7uSt8PDw/npQtCeCEwMBAjR47kIlaI+8OWLVvi5MmTgkbUJQiC+F6IiIjgXjMMg0aN\nGhVga5QjeQA/v9ViCT759bk/fPgQd+/e5Y370dHRiI6O1sqepJ2kpCSBW0sQhCQMw6BOnTpqRwqN\niYnB169f5b539uxZ7N69GwzDICcnB2vWrMGpU6ewZ88etGzZUqldNzc3bn2GYRhMnjwZVapU0fh+\nNEF6/Q8g4QWCIIj8IDY2Fh06dNDoWVUkEmHYsGFq5//69Su3LiUmMjJS4z2XI0eO4KefftKojCou\nXboEBwcHvHv3jvfca2lpiU2bNsHW1lYru0+ePIGtrS3CwsIA5EUiPHHiRKF2/iAIgshP9BUFXHKs\nKayRxoVm3759mDNnDj5//iyzx96vXz9s375dKwEheaSkpHBCReIxc+LEibCwsBDEvjqEh4fD3t6e\n912np6fj4sWLJLxAEIRODB48GC9fvtS6fEJCAi/t4OCgk/jawIEDVfoiSOPk5IRLly6pnd/ExASr\nVq1SK7ASkNffxsTE8MaBjx8/onnz5hq189KlS7CystKoDEEQRHFj7969PD/Z4rZ/v2LFChw/fhzP\nnz/nrkVHR+Pjx4+oXLkyd+369esIDg7mPofq1atj9uzZSm1fvnwZ58+fB5A37+nfv7/GgZbat2+v\nUX6CIIjiQFZWFt69e6cX258/f+al3717pzcx6tq1a+tVCKEwCS/88MMPCv1BhIRlWUHOZ3l4eGDu\n3LlcOr8FlUQiES+tzO+md+/eCAoKwsCBAxEREQGGYeDp6UmiCwSRz1y9ehXjx49Hz5494evrq3a5\nQ4cOwc7ODvb29li9ejUsLS312ErtkNyjKm4CPgRBFG5IeEEgNm/ezC1YGRgY4MmTJ2jYsGG+tkHy\n0Ia5ubncPJmZmbx0fggvXL9+HcC3SBBDhgzRe5365ubNm9i5c6dMlAtvb2+Fn31+EBMTg4ULF2L/\n/v2cQ7+4beXKlcNff/2lkROnJNWrV0dQUBDmz5+P7du3g2VZzvbbt28xbNgwdOzYEatWrULXrl2F\nvTGCIDTmxIkTSElJ4fqCESNGgGEYTJgwAXv37lXLBsuyGDhwoNI83t7ecHJywoMHD/Dp0yeuvoyM\nDJ6KtioYhuGcHTp16oSUlBSZPOHh4TxHt3Hjxqk8RNWmTRv89ddfardDGVu3bsX8+fO5xUBxHzhi\nxAj4+fnly5hKEARRHImMjOSlJedRFStWlHGw0wWWZdGzZ0+5761YsQLLly9XWPbr1684duyYTKQM\nTSiuzh6FCaHVbaWdMTX53qXrF8+hCILQP+K+NjAwUG2RgxEjRuDYsWNy3xswYAD++usvzJkzB2lp\naVwk0Hbt2mHp0qVYvHgxDA0NZcoFBQVhw4YNXP9fsmRJLFy4UGVbli1bBjc3N7XarQxxn8OyLNq1\na6ezPUXs379f68O0BEEQxQmRSKRx5HGWZfHp0yeN6pEWgYuNjVXbhniMTE9P16hOZXz+/Bnz5s3D\n3r17efMlQ0NDTJ48GWvXrkWZMmW0su3t7Q1nZ2dkZmZytsPCwtCuXTuEhoaiVq1agt0HQRBEUUSf\na0z5EUGosKyRvX//Hr///jvOnTsns8duYWEBLy8vjBw5UtA6vb298eXLF+4zMDQ0xPz58wWtQxmJ\niYmwtrZGamoq73qLFi3g5+eXb+0gCKJ4EhERgRcvXgDQXbyHZVmthJAl+/L3799rXP7Dhw94/fq1\n2mOVEHO7uLg4xMfHq10fwzByBVgJgiC+J8QRlSX7/TFjxqBu3bpy8yclJWHLli1cfgsLCzg5OalV\nV0EF4zExMcHOnTu5w4uTJk2Cl5eXjJ/ahg0bAHwbI2bPns07sCmP9PR0TngByPPZU1WGIAiCAB4/\nfow2bdrovR6WZTFo0CC92GYYBhEREYKJjMojNzdX5po8v4r8QLzmpw+k571C1/P48WO5n6U+kRbN\nMDY2Vpq/UaNGuHXrFvr374/u3btj1qxZ+mweQRBS7NixAzNmzADLsvD390f79u3h6OiostyXL18w\nd+5c5ObmYufOnTh06BC8vb1hZ2eXD63WDhJeIAgiPyHhBQE4duwY7t+/zz2QW1tb57voApA36IlR\n5MRWEMILN27c4B2UtbGx0Xud+iQlJQUTJkzg0uKFyqFDh6o8oKwvMjMz4e7ujg0bNiA1NVVGEKJr\n16743//+h6pVq+pUj4mJCbZu3YrBgwfD3t4e79+/50X6uHnzJrp3745u3bphyZIl6N69u873RhCE\nduzfvx/At35A7BCmTnRudZzppA+NXrhwQeF7qpBedHry5IlCZVHJvE+fPlWaRyw6oyuZmZmYPHmy\njPO4gYEBVq1ahcWLF+tcB0EQxPeMWHhBPH5IzqWE2vQQwlH8wIEDSE9P5zltaBJhNT4+nieWRwuA\n2jN58mS5ghx///0393tiGAaDBw9GvXr1NLYvPrz17t07HDlyhPeb0fYZR/q3XFgOFhBEcUdIsRMH\nBwd07doVo0ePRmhoKIC8A7aurq44e/Ys/ve///H6nISEBIwePRo5OTncuLFhwwZe9CFV6NJX6Htz\nXxIzMzO92SYIgiiKaDr+KMovT8hLqDqFws/PD87OzrzI4AzDoHXr1tixYwdat26tld3379/D0dER\n58+fl9nvqFatGnbv3k2iCwRBEJAfNU4IbG1tcfjwYQB545Gfnx/GjRunl7oKGh8fHyxcuBBfv37l\n+TSIxcw9PT3xww8/CFpnWloaL7CHeB8vvyKWZ2ZmYtCgQXjx4gVvnK1YsSJOnDhBQuMEQQiKtmtS\n+SEApGk7tM1bFOZ2BEEQRZWdO3fynmurV68OX19fhYF83r59iy1btnBpS0tLpcEZCgsdO3aEi4sL\nWrVqJdf/+tq1a5yQnFgI3MHBoQBaShAE8X0huZZEyCJv7bIw+MsJHTRJyN+BvLaVKlVKENuaIBKJ\neGlVwgsAUKlSJdy4caNAg9gSxPdKz549YW5ujtTUVLAsi1mzZqFdu3Zo3ry50nILFy7Ex48feeeL\n2rZtmx9N1ghJ8ZnCMI4QBPH9QMIL0G1DIysrC87OzrxNcU0PYM6ZMwdeXl4K63/y5InM4NCzZ09c\nvHiRd02ses0wjMLN//wWXggPD0dcXBw3EFeqVAkdO3bUa536xsnJSSZqVdmyZbFt27Z8b0t2djZ2\n7dqFdevWITo6mheBAwDnhNipUyfs3LlT0LptbGzw559/curpkhO9q1ev4urVq2jatClmzZqFsWPH\nwtTUVOc6aXGCINQjLi4OFy9e5PUHtWvX5t4XYsFI2kZAQAD3ukyZMgonXe/fv0d4eDhno3HjxqhS\npYpazgaSeZT1B0IuiEVHR8Pa2hqhoaG8z7N06dLYt2+f3tRkCYIg8hMhHbz8/f1x5coV+Pv7q13m\n9evXvHSjRo10bocyVI0xivD19eXl79KlC65cuaJ2vZIO6wA0OnhL8HFxcZF7/dGjR5zwAgBMnDgR\ngwcP1rqeDRs2QCQS8eb7UVFRqFatmsqyHz9+RNWqVXnOLYqEpRRBzpfE90JR+q3XrVsXt2/fxvLl\ny7Fx40bk5OSAYRjcuXMHP/30Ezw8PDBlyhRkZGRg4MCBePfuHTfGdO3aVe2oSWL0Pf7pgmTfqKnw\nQlH6zgmCKP4I3ScJuS6lTMhLF4Swc+fOHcyePRv//fcfz56lpSXWrl0Le3t7rW0rOwBra2sLb29v\nQQ7A0phDEATxffP69Ws4ODggMDCQt8fOMAzq1auHP//8E127dtVL3du3b+f5UAB5Do75AcuyGDVq\nFG7dusUTXTA2NsbRo0f1GuWQIIjChz7XaMR96oABA3Dq1CmNynp6emLBggUA8uYB58+fR69evTSy\nUaNGDcTExGhURhqh5mD6EkjVxg5luwn3AAAgAElEQVStyxEEUZjQtU/6/PkzFi9ezHuWX7hwoULR\nhaKOm5ubwvfmzZsH4Nv4a29vrzB4X0FC4xBBEMUJdYLgaUt+CNHlh8Cd5EFZMQV9YFY8Vv7yyy+C\nnG0BgNDQUCQnJwPI+1x//vlnlCxZUiebNWrUEKJpWpOdnc1Lm5iYqFWORBcIQn2EfDauV68e/vjj\nD0ycOBEMwyAzMxO//fYb7t27p/D/ZVBQEHbu3MmbT/n7+6NBgwYa1Z0fiP3zxHsZyijM8wqaDxFE\n0aN4rjBpgLKIk+pEo/T09ERERAS3Gd+vXz/89NNPWrdFnc5SUVvevn3Lva5QoYLcPOnp6by0voUX\nrl27xr1mGAZDhgzRa3365sCBA9i3b59MhCVPT898PTiVnZ0Nf39/uLm54e3bt3IFFxo3boywsDBE\nR0dj1apVemkHwzCYPHkywsLCcPPmTV79ABAWFgZHR0c4Oztj1KhRGD9+vEoFLEW/b4oOSxRnhH6I\n3rZtG3dIUBpbW1s0a9ZMYdnLly/j/PnzAMBtxMg7ACumY8eOePr0Ke7evcvVN27cOGzdulVu/q1b\nt2L27Nlc2tXVFcOHD5fJJ6/t0n2MMsT9sy6baTdu3MCIESM45zfJ6OYnT55U+rmoC02SCIIoaHSd\nD4n59OkTHBwccObMGU6UQJ2DNizL4uHDh7xn2aZNm3Lv16lTB+XKlVPrXhQRGRnJizZetWpVufOg\n8uXLK7Rx9epVhISE8MaDmTNnatSOFy9e8NI//vijRuUJ1UiPq7o8B0RGRnILy5LExsaqJbzw+fNn\nXlqb+SLNjYjvAaHGIXl2WZZF9erVdWugHAwNDeHm5oYePXrAzs4OHz9+BJC35ubk5IRTp07B0NCQ\nO4gqjhoqKeCj7j3ogj6dMaT7W02EF/T1nRMEQWiD0H1StWrVBIs8Lj5oJB5LKleujPfv3wtiWxei\no6OxePFi7N+/nyfGbGJigunTp8PV1RWlS5fWyvabN2/g4OCAa9euyRyArVq1KrZv3y6YCKq63z1B\nEIS+ISerb+zevRuOjo4q87EsCysrK7nvnTlzBv3791dZfvPmzVi2bBnS09N565LGxsZYsGABli9f\nrrYzsaYkJydj48aNvPGmZs2avDVRfTJ9+nScPHlSxt9h06ZN6NKli8b2vrffKUEUJ4rSGk1B9DXH\njx8XxE5gYCC6devGm9+kpqZqLGQqBEXpOycIovgjRJ/k4uKChIQELl29enVMmjRJwFYWDa5evYoH\nDx5wY42ZmVm+CbtpAo1DBEEUNnRZl2vdurVg+0HSSAvRPXjwQKnPd2FGJBLJXCss/fzRo0dRtWpV\nQWx16tQJQUFBXHrHjh1o1aqVILYLCunvTtVBZ31C649EcUQfz8bjx4/HP//8g6NHj4JhGLx8+RLT\npk2Dn5+fTN709HRMnDgRwLc9gkWLFhXYec/t27crDTT96tUr7nVUVJTSM7tpaWm8tJeXFw4ePCg3\n78CBA7F69WoNW6sdNB8iiKLJdy280KVLF4WTHldXV7i6uqq04ePjw1POcXd317gdhoaGMgdCcnNz\nOZU3hmFgaGjIe1/eARLxAXwACicC0sILypTa5syZAy8vL9U3oALJjt/Hxwc+Pj4625Rm/vz52Lhx\no+B2JXnz5g2cnJx4TggA0KtXL+6hQ98kJiZix44d2LZtGz58+CBXcKFmzZpwd3dHs2bNNDoULPk9\naTJBadmyJbZv347jx49j4cKFeP36Na89DMMgKSkJO3bswI4dO7BkyRKFDydXr15VWI++FigIoqAR\n+iE6IyMD27dvV5i/d+/e6N27t8Ly6enpnPACAFhbW6t0Ups/fz6AbxMvZQcLpSM9yztMm5iYKHPN\n0dERu3fv5tJnz55F3759ZfLdvn0bHTp04O7f0tJSadsVcf/+ffTs2ZNbPBLfW48ePXDkyBFBouqR\ngzdBEAVNrVq1FD5jKZsryePYsWOc6ALLspgzZw66d++u0AlazIsXL5CSksL1e7Vr14aFhQX3/n//\n/ad2GxRRu3ZtREVFcWl/f390795dIxtr1qzhpWvWrKnxIuOrV694z+6aCC8kJSXhzZs3RX5TRt8I\nKbywbNkyZGdny4zJcXFxapWXFF5gGAaVKlXSqP6IiAiF79HciCguCLEupwpNn6s1WQ/p3r07Hj58\nCDs7O1y8eJGrT1LITuzgdvLkSdSqVUtt23PmzIGdnZ1GbZfk4sWLmDlzJm/cuXXrls5iRgDw7Nkz\nDB06lHdNXUfx/PjOCYIg1EXI+ZA+0FdEVG15/PgxPD09cfDgQe45WdwmGxsbrF+/XuUcRyQSITEx\nkfv7/Pkz9/fu3Tv4+PggLS1NZs/D0dER7u7uWgs6SOPn5yfXwQRQvkdBEAQhNORkJR9F96tMXE5S\nDEgZYWFhsLe358TEJQ/Btm3bFrt27UKTJk10uwEVrF27FvHx8QXyHS9atAg7duyQEV1YtGgRpk2b\nprE92uciiKILrdHkH4XlgAh95wRBFCaE6pO6d++OS5cu4d27dwAAd3d3wSJXFyW6deuG+/fvw9XV\nFadOnYKTkxOqVKlS0M3iQeMQQRCFjaK0Lpcfc4q3b9+idu3aerMvvgeWZRVGXteVLVu2aBxESShK\nlizJS6ekpOitriNHjsj1sdeWkSNH4t9//8WePXt416V9/f39/XHjxg2t6li/fr3KNVcKTkR8T+jz\n2djb2xtXrlzhBOr+/fdffP78WSawt4uLC+fPzDAMBgwYIOMjnZ98+PBBJoCfJJLXMzIy8PDhQ5U2\nxfk/fPiADx8+yLWZX8JGNB8iiKLLdy28IAS7du3iDn7OmDFDq+jXHh4e8PDw4F2ztbXF4cOHwTAM\nmjRpgkePHim1kZCQgI8fP3IPlIqcuSWFF9R1ii7MEfbyi7S0NFhbWyM5OZl3/YcffuAdBNYnly5d\ngrW1Ned4KO18aG5uDhcXF8yfPx+mpqZ4/vy5Rp+3rt+TtbU1Bg0ahG3btsHNzQ2fP3+WiU5vaWmJ\nuXPnamybIIor+nDw9vPzk3HY0ic5OTnYv38/r0+KjY1VmF8d4QV53L9/n1eHIqU66cOQ2iqSfvr0\niXMil4xs7unpCQMDA61sSkIO3gRBFDemTJmCgwcP4ubNm2AYBikpKRg/fjwCAwOVlgsNDeVeMwyD\nDh066LupGnP79m1cvXqVNyZMnz5do7E2NjYWycnJvDKNGzdWq2xaWhqGDh2KkJAQeHl5YfLkyRrf\nw/eCUMILt27dwoEDB+R+xzExMWrZ+PjxIy+tqfACQRC6wzAM6tSpo3ZfEBMTIzNfUUXFihVx7tw5\nbNiwAcuWLUNOTo7MAZYdO3agffv2GtktX748ypcvr1EZSeQJF7Vs2VIQh0N5n1GJEiV0tksQBEEU\nTjZu3AhfX1+8ePECAHh7E2ZmZpg0aRIaN26Mo0eP4suXL/j69Su+fPmCpKQk7k+cVuVoJn0AtkmT\nJti+fTs6duyYH7dKEASRr5CTVf4iEomwdu1arF27Vmbvp3Tp0nBzc8P06dP13o6YmBhs3bq1QHwm\nFixYAE9PT5k5q5OTk1YOlcp+p7TPRRAEQRAEQeQHtra2GDZsGDw8PBAUFITffvutoJtUYDRv3hzH\njx/HpUuXKJgDQRCECmhdTjH6WLMqbELj+iA/hRdcXV3x/PlzQWwxDINOnTrh1atXOHv2rNwDz+J0\neHg4wsPDtapjwYIFSvNQ4FaCEA4LCwts3boVY8aMwaBBg+Dn5yfjf3bt2jV4e3tz/+cbNWqEAwcO\nFFCLZVE1TqgzjhSHM6wEQRQOSHhBR3r27IkVK1bAx8cHP/30E0qVKqVWx2xsbMypCAnBgwcPAHzb\nIG/QoIHcfKmpqdxrTZyi1Y0SIc4rRjLqkSZl9NEuXRg3bhweP34s44iwefNmVKtWTe/1A0Dnzp1R\nuXJlRERE8AQNjIyMMGHCBLi6uvIOGDdo0EDtyYaPjw+mTp3K3d+8efOwceNGjdtoZGSEWbNm4fff\nf8euXbuwadMmREVFcZ+Xh4eHINEVCYKQD8uy2Lx5c75OEP755x/Exsby6lQmvBAfH89LSyvoySMn\nJwdhYWFculKlSgoPL4rti/sdIRS8GYZBu3btsHnzZp1tEQRBFFcYhoGvry9atGiBjIwMsCyLmzdv\nwt3dXeniuVh4Qdxv//rrr/nVZLVZvXo1L126dGk4ODhoZOP+/fu8tKGhodrR88aMGYNbt26BYRhM\nnToVQUFB8PHxoUOuchCJRLy0sbGxxjZyc3N5Ef6k55xv3rxRy87bt295aX0qwxMEIYv4/25gYKDa\nc4IRI0bg2LFjWtXXuXNnVKxYkZsLSfYdO3bsQLdu3VCzZk2tbGuDtHCooaGhYFGesrKyZK6pK+5K\nEARBqE9hcUSLjY3Fixcv5EZ8yszMxLZt29S2peoexONnyZIlsXz5csydOxeGhoZatZsgCIIoetSp\nUwejRo2S+97du3fx+vVrAHnjycCBA2UcmgHI3bcPCQmBvb09njx5IhPYYNCgQfD29kb16tUFvBPF\nzJ8/H+np6fk+rs+dOxdbtmyR8XWws7PDH3/8ka9tIQji+0Hc55w5c0anwAYsy3IBkXRpR0GhKEof\nQRAEIQympqZYsmRJQTejwLh79y7u3LnDuyYWUFWH4OBgXvr27dsarfdJUqZMGdjZ2WlVliAIorjg\n6OiIQ4cO6WQjOzubl27fvr3OweJcXFzUGi/lzV+ERh91FPQcq1SpUry0pgE/NEXyfrX5PBVFlFfX\nlq71EwShO97e3krP67Ro0QLNmzeXe/Zl3759AL7tE7Ro0QIbNmyQa6d27dqwt7cXptFKaNq0KYYP\nHy73vZSUFJw/f57re2rXrq1UbC4hIQFXrlzh8jdt2lTh+dq2bdvq2HKCIIo7JLwgAEuXLkX//v0R\nERGBtLQ0uQ/vQkXeVMSlS5d4aUWRwNPS0rjXmhzUYRgGDRs2VHlANjo6GpGRkVyZRo0aqYzQ9+7d\nO+5Qirpl4uPj8ezZM7XbrwurVq1CQEAAzxFBzPr167F+/XpYWVnh3Llzem2HqakpvL290b9/fzAM\nA0NDQ9jZ2WHZsmWwsrLSa92aUqJECcyYMQNOTk44cOAANm7cCEtLS4wdO7agm0YQxZrjx4/j1atX\n+bqItHPnTplryiZycXFxvLSkYIwiwsPDkZmZyYn5tG7dWmHe6OhoXlqow01Cj9sEQRDFkbp162LN\nmjWYN28et0C/fPly9O3bF82aNZNb5vLly7zF/MImvBAaGsot2okXGqdPn46yZctqZCcoKIiXrlev\nnloHYFmWxeXLl7k0wzDYt28fHjx4gICAANStW1ejdhR3pIUXtDlk7OnpiYcPH3LPUyVKlEB2djZn\nOyIiQi074jmu+HdD3xVBFAy6bPKmp6fjzZs3ePfunULn7tzcXGzcuBHLly+HSCTijRfif+/cuYPW\nrVtj37596NOnj9bt0YTExEReWtNxSxnyhBdIDIggCEJ4CovwwqRJkziHDFXjqibi3fKcshiGwfDh\nw+Hh4YEaNWpo2WKCIAiiqNK1a1d07dpV7nuOjo6c8AIA/PHHHyr3fzIzM7Fs2TJs3rwZubm5vHla\n5cqV4eXlpdCRTx9cu3YNhw4dytcxnWVZzJgxA9u3b5cRXRg6dCh8fX3zrS0EQXy/aNvvCRGZrjAc\nACHhBYIgCP3g5eXF20dXl/T0dF46KioKgwYN0tjO6dOnNS6jjFGjRuHIkSMq8127dg2dO3fm0v/8\n8w9WrlypU92S49LJkydx8uRJrexYWVmR8AJBEN89GRkZSE1NlXvmRBtYlpUZu9RFsn/PzMxUmtfA\nwEBGPEAIsrKykJWVxVuXkyemKgQmJiZ6sasO0v4YSUlJapUTiUQ6+aYbGhpqHCRDJBLJ/T2oOv+m\nSGxBiMj0BEFojo+PDy+oqTwePnyo0g7LskoFg7p27ZovwgsjR47EyJEj5b5348YNnD9/nkuPHTsW\nK1asUGgrODgY7du359JjxoyBs7OzWu2gPosgCGnoFKFAtGrVChEREQo7WnnRgIRCJBJhz5493KSk\nRo0aCg91pKamcq/VdYoWT3RWr16NYcOGKc27YcMGLFq0iEuvXbsWgwcPVlrGzc0Ny5Yt06jM4cOH\nYWtrq/eB7ejRo1i5cqWMI4KpqSkyMjLw/PlzAHnR2KVJS0uDgYGBoFH/+vbti5EjR8LIyAiurq6F\n/vCOWBzCzs5OJtoiQRDC4+7uzr2WdmDWBw8ePMDZs2e5esTjkDLhhU+fPnGvK1SooFYk6nv37vHS\nylTqoqKieOkff/xRpX2CIAhCOGbPno2///4bt2/fBsMwyMrKwtixYxESEiLT58fGxuLx48fcOFK2\nbFmFAg0AEBgYiJcvX8LBwUGv9yDJmjVreOmSJUti7ty5Gtu5desWgG/jc7t27dQqxzAM9uzZAycn\nJ24uxzAMnjx5gjZt2mDv3r1aOYIUV3QVXggLC8Py5ct5G36LFy/G3r178fLlSwDg5oCqkM5X2Odu\nBPE9kpOTwwmIRkZG8iIAnThxAqVKlQLLsqhWrRrevXsnUz40NBSTJ0/GvXv3eOtG5ubm6NOnD06c\nOMH1JZ8/f0b//v2xdOlSrFixQu9ztY8fP/LSlpaWgtmWtwkv5NoXQRAEkUdhEV5o3Lgx2rVrhzt3\n7ihsg7m5OSwsLGBpaQkLCwtUqFABFhYWqFixIpeuWLEiHjx4AHd3d279TlJwoUWLFvDy8kKnTp3y\n7d4IgiCKI4sXL8bt27d1tvP06VNeet26dfDz89PZbrt27bB+/Xqd7ajixo0bmDRpEk+snGVZGBgY\nwN7eHu7u7oIK1KlCJBJh2rRp+TqeZ2RkwNbWFidPnpQrunDo0CGdoyUSBEEoQ/y8X6pUKVSvXl2j\nsgkJCZxfAcMwqF69usaHc169egWRSFTg4gskvEAQBKEfxD5r2iLun79+/aqxHYZhkJubq3XdimxK\n/pufFPRYSRAEUZwpSCE6sR11yteoUQNfv37Vuh5FTJo0SWZNMTo6GmXKlBG8roKkXLlyvLR0oAxF\nLFu2DBcuXMCUKVMwZswYtee94u+1b9++OHXqlEZt3b9/v4xQ0sKFC7Fw4ULetf/9738YP348gLzf\n4KVLl9C9e3d4eXlhzpw53PVr167R3h5BFBAFub60fft2lC5dWmc7LVu2RI8ePZTmCQ0NBfCt76tX\nr57O9SoiP85fEQRRtCDhBQHp27cvwsPDAQBz587FP//8AwAoU6YM7t69K3cSNHHiRPj7+yu0ybIs\nnjx5InfTe8uWLZg5cyZ27dqF2NhYLhK4IqUfAEhJSeHqp2h0yvn33395EwvxINq6dWv06tUL69at\n40XnlWbq1KkICgrCjh070KtXL8HadfDgQcFs5SdCPFgRBKGYo0ePIjg4mHdQUN+sWrVKJporALmH\nksRERUVx+atVq6ZWPcHBwQC+9cNt2rRRmFccXVoMCS8QBEHkLwzDwM/PDy1btkRmZiY3n1m+fDnW\nrVvHy3vx4kVeOUXRxD98+IB58+bh0KFDMDU1RceOHdGwYUO93geQN/6cOnWKN85NmzYN5cuX18hO\nTk4Od0hJPHfQZMPBxsYGTZo0wZAhQxATE8O15evXrxg6dChcXFywZs0aWvADkJ2dzfucNVUz9/Dw\nQGZmJvdZ1q9fH87OzggODsbLly/BsiyePn2q1rOWWFRE3JZGjRppcUcEQejKy5cvERYWhqioKERF\nReHt27eIiopCZGQkoqOjZQRbgLx5h+R1c3Nz3vsfPnzAypUrsXv3bpmoqT/++CMCAgLQrFkz+Pr6\nYvr06cjIyOD6jNWrV+P27ds4cOAAKlasqLf7lhTDYxhGUOGFrKwsmWu0xkgQBCE8hUV4AQDmzJmD\nI0eOoGHDhrCyskK1atVgaWnJCS2oEuB59uwZ5s+fz+2ZAd/W+SwtLbFy5Uo4OjrK3OP58+dRu3Zt\nNGjQQC/3RRAEURx5/PgxAgMDBbXJsiyeP3+uthilMvJj7pCUlISuXbsC+DZ+ised2rVrIzk5GVOm\nTBG83mXLlqFx48Zy39uwYQPCw8Nl2qMvPn36hEGDBuHu3bsydU6aNAk+Pj60lkgQhN4R9zPdunXT\nOHK2p6cnFixYwKV37tyJ3r17a2SjRo0aiImJ0aiMPiDhBYIgCP2izH+3KNcljbI5RGEZVwpLOwiC\nIAoDLMuicuXKWs1JJOdDDMPgwYMHSgMZySMnJwfGxsYF3jdL+3MDwN27d1Uesi1qiIUXxJ93fHy8\nyjJZWVnw9fVFfHw8pkyZggULFsDDwwOOjo56bau6SAdcVXQOiAScCKJg6NSpE2rUqKH3epo3b85L\ni+dEq1atEsT+lClTVI4J0kJ5v/76qyB1S1PQYyZBEIUTEl4QkJIlS6J+/foA+A+XBgYGgqvqiDv1\npKQkmYico0ePVlguIiKCe52fURyKGnfv3oW1tTWys7N512vXro0zZ85g586dSssfPHgQ//vf/8Aw\nDPr06YMxY8Zg8+bNenWqJwji+yUrKwsuLi68saB///46KXur4tGjR7woOSVLluQicaelpSEqKgo1\na9bklcnMzORFpqhTp45adUkfVlUmvPDs2TMub8WKFWmsIwiCKADq16+PVatWwdnZmeuTPTw8MHjw\nYLRv357Ld+HCBQDfnASGDBki196wYcM4caGsrCxMmjQJQUFBer0HlmUxbdo03jVzc3PMmzdPY1uB\ngYFITU3lLcxpqvTcqlUr3LlzB4MHD8a9e/d4jhXr1q1DaGgoDh48KKOgrS1FdVMkIyMDwLcF3iZN\nmqhdtn79+rh+/TouX76M9+/fw8DAAD4+PjA2NkbDhg1x5swZro6XL19yc395fPr0CZ8+feK+IwsL\nC1StWlWHOyMIQhFxcXG4cOECoqOj8f79e4SFhQHgO3arg+RBFMk0AFSoUAEA8PHjR2zatAnbtm1D\neno6b47CMAxsbGzg4+PD9cX29vZo1qwZhg0bxhPOuXz5Mlq1aoUjR47gl19+EeBTkCUyMpK7H4Zh\nUKVKFcFsZ2Zmylwj4QWCIAjhycnJ4aWNjApuO++3337Db7/9pnG5mJgYuLq6Ys+ePcjJyeGtXZYo\nUQLz5s2Ds7Oz3Ag+Bw4cwIQJE2Bubo59+/Zh4MCBQtwKQRDEd4P0HKcgyW9ntZycHJlDSeLXr1+/\nxuvXrwWvk2EYzJo1S+57YWFhWL16Ne87MTIygkgk0stn8+rVK/zyyy+IjIyUEV1YuHAh1q5dK3id\nBEEQ0gQFBXHCppoKJAvFkydPuHmVqalpgbQBkJ3bAQU7vyMIgiguVKpUCXXr1tW4nEgk4p6VWZaF\nqampxgeWxAHyhJxvNWzYEB07dpS5/uLFC87XTh5LlizhiRUVJPKCCxIEQRC6UxjW97Tl1atXMutf\nwcHBhUJ4wdraWqv5at++fbFkyRLeNenzQcrGbjGHDh1CXFwc9/mkpKQUKiFycbBfMRSAlSAKF9u3\nby/oJihFsu9XNI6psz/y5s0bXLlyhZt/1a5dG7Vr1xasnWL69Okjdw2PIAiCVvILAYoGDHnO3tJl\npk2bhvj4eG4xb8CAAWjRooVcew8fPkRYWBhXVkin6+LEs2fPMGDAAO4AMZD3XZQvXx7//POPyiiB\nb9++xdSpU7nPmWEY7N+/H+fOnYOHhwcmTJigU/v27Nkj+EGzZ8+e8dIXLlxAYmKioHUAgJeXl0yk\nSIIgdGfz5s2IiIjgxoI2bdpg+PDhehVeWLlyJW+cWrlyJebPn8+9Hx4eLiO8EBERwXN2U0eUKCsr\nC48ePeLSNWvWRKVKleTmTU1Nxfv377nPQZPDlgRBEISwzJ07F8eOHeMEE3JzczF+/Hg8fPgQJUqU\nQEZGBk6fPs0tiBkZGaFfv35yba1Zswa9evUCkPdc/t9//8HLy0uhM7MQ/PXXX7h37x7vYJCzszNv\no+TWrVtIT09XuRkUEBDAS1tZWWnlBFKlShVcv34do0ePxqlTp7h2MQyDixcv4ueff8bx48dlVGZX\nrlyJlStXalwf8G1OyrIsSpUqpZUNRfZ+/vlnjco2bdqU90wgD7HwAqC5U3/JkiVhaWmJY8eOoXPn\nzpgyZQo6d+4MADIbW8HBwUqFF27fvs1rR+vWrTVqC0EQ6vPx40eMGzdO5romkeRKlCiB2rVrIy4u\njhNN6dy5M7y9vVGrVi1ERUVh/PjxOHz4MLKysngOdQzDoEKFCvD29pZ7ILVNmzYIDQ2FjY0Nbt26\nxZWJjo5Gly5d4OnpienTp+v+QUjx+vVrntOfkJtOkn0tkOdIZ2xsLJh9giAIIg/pjX1DQ8MCaonm\nJCUlYd26dfD29ubEisQYGRlhwoQJWLlypcI9qp07d2Lq1KlgWRbJyckYMmQIVq1aJePIRhAEQShH\nWURUdcuL0VUgQNe2aFunNOo4+0nnU5ZXct4lT0goNzcX9vb2XLAHlmVhbGyM6dOnY/PmzapvQgPE\nbbl58yY3bxXXaWBgAE9PT72upxIEQUhSpkyZgm5CoQnQQE7bBEEQ+mH9+vVYv369xuXevn3L2zOp\nV6+eyj3o/MDV1RWurq4y1+3s7LB//36F5YyMjEjQhyAIgiiUJCQkICoqSmad7d9//8XixYsLqFXf\n1tDu3r2rVdnGjRvLXJf2aVdHeEH60PQvv/zC+akVBpKTk3lpEl4giKJLbGwsOnTowPXHU6dOxdy5\nc3WyqWq/R939JVV2XF1deeeVlAUpJwiC0Ae04lLA+Pn5wc/PT+a6ra0tDh8+zB0clbe4t2PHDhw8\neJCbABgYGGDmzJn48uWLzAZSSEgIxowZw3MMVxYx/HvlyZMn6NWrFz5//sxdEyvbnjhxQunhGjGJ\niYmoXr06wsPDeYN8YmIi7O3tsXfvXvz5559q2ZJHYGAg/P39tSqrCnF7Hz9+jMePHwtqm2EYbNiw\ngYQXCEJg4uLisG7dOl7/vrzISuoAACAASURBVHHjRkREROitTpZlcf/+fS7dsGFDzJkzB8uXL0d6\nejqAPOGFPn368MqJRV7E7VRHeCEkJATZ2dmck5iyiLAPHjzgpZs0aYKQkBCtDpvGxcXx0k+fPsWg\nQYM0stGsWTOKHEQQxHeLgYEBfH190apVK2RlZYFlWbx+/Rrz58/Htm3bcPLkSSQnJ3P9e+fOnRU6\n4vXo0QPDhg1DQEAAN94tXboUQ4YMgZWVleBtT0hIwJIlS3iLelWqVOEJDL158wZDhgxBQkICnJ2d\nsWbNGoUHoU6cOMEbpwcMGKB120qUKIHjx49jzpw52Lp1K098ISIiAr/++it27dqFUaNGyZTV1Lld\nSOd6aXtC2ZRG8jCwug794nxi5/i2bdvCz88P1tbWXJ5GjRoB+NbmoKAg2NnZKbR58+ZNnm1NRSYI\nglCfBg0awMjISG5EU0nKly+POnXqyP0TH/ocMWIEjh07BgCwsLDghNzi4+Oxb98+AOD150ZGRnBw\ncMCqVatkIhhIYmlpiStXrmDy5Mnw9/fn+kORSCQz7xCC+Ph4JCUl8T4LIYUXxHM+MSVKlBDMNkEQ\nBPGNzMxMXrooiNwkJydj8+bN2Lx5M758+cIbiwwMDDBixAisWrVK5ZqglZUVSpUqha9fv3I2li1b\nhkePHsHf3x9mZmZ6vQ+CIIjigHjecvPmTbRv317j8mJfBSBvHuTn5ydX9E4VwcHBaN++fb6LLqgT\n+EJdx0B18gKQK1rq5uaGu3fv8uaSixYt0ktEJjGSggsAYG5ujt27d2PkyJF6q5MgCOLLly8oV66c\nXmyzLIu+ffvqbKdjx464fv26AC3SDGkR06IwtyMIgiAIgiAIomijTHA0vwgJCeGlxetj4kBH+e1n\nIO0Hp8lnJJlfXrsrV67MqycmJkapvatXr+LOnTu8NcOFCxeq3Z784MuXL7w0CS8QRNFFJBLhzZs3\n3N5BfHy81rbEfdbHjx9hYWEhN4+/vz8mTpwIIK//3LdvH2xtbTWu69q1azhw4AAvuJ+Dg4PWbRcC\ndYR1CIIoXpDwQhHlwoULmDNnDu+B28nJCceOHUPv3r1hYGCAsmXLoly5ckhJSeEi9okxMDCgzXUp\n7t+/j969eyMhIYG7Jha02LNnDzp27KiWnZYtW+LBgwdwc3PD+vXruYNmYqeMa9euoUWLFli8eDFc\nXFxoU40gCJ1YtmwZ54QsPszZuXNnlcILycnJiIqKkrkuPSGIiopCWFiYTL4tW7bAxsYGubm5WLFi\nBRiGQZ06dfD48WMwDIOnT5/KlBELL4hp2rSpyvu7ceMGgG8TNWV9cWhoKC9vy5YtERsbi7Nnz2q8\nUCZGXCYhIQFnz57VqGxqaqrG9REEQRQnGjVqBFdXVyxevJjrh//8808MGTIEBw4cAPCtzx46dKhS\nW5s2bcK5c+c4B7W0tDQ4Ojri0qVLgrd70aJFSEhI4M213NzcuI2Tr1+/YtCgQdy8YcOGDQgMDMSh\nQ4dQs2ZNnq2goCDExMTw5mKaCvnIY/Pmzahbty5mz56NnJwc7jkgPT0do0ePxv3797Fu3ToYGBhw\nZXTdVBN6U04fm3wpKSmcbYZh0KFDB4V5v3z5whObk4xKKL3Q27JlSxgbG0MkEoFlWZXOmYGBgbx0\nt27d1L4HgiA0w8TEBHXr1sXz589hbm6OunXron79+ry/Bg0aaOT4Ld0/de7cGVOnTsX27du5/rZv\n375wd3eXG81AHsbGxvD19UWTJk3g4uKC3Nxc2NjYaCUSpwppQToAWot/ykP6IDAJLxAE8b3z+vVr\ntcRFtUU8Lj19+pT3fC8k165d0ymKTlpaGv744w+4u7tzcykxDMNg0KBBWL16NZo1a6aWvV69euH6\n9evo378/55zGMAyOHj2KiIgInDx5khNOIgiCIJRTGJys85sKFSrIjS5+584d/PLLL9w4NXv2bHh6\nesq1YWFhgYSEBLAsi/r16yM8PFwmj42NDY4fP86lJdeWAOD27dtYtWoVb1xs0qQJli5dqjRirRCI\n18Z+/PFHBAQEqD0GEwRB6Iq2e/L6QlPhn+HDhyMgIEAvbRF/LllZWXqb20VGRsrsUxEEQRAEQRAE\nkf8wDIP4+Hgu0IsmSJ5jAYChQ4fC1NRUq3aoG7RGH/z7778ybQHy5kRnz57F8OHD87U92dnZvLS2\nn4s8/wjpPat3794ptSHtJ9KwYUMMHjxYq/boi7dv33KvzczMFAbUIgji+0ST9T9t1grj4+MxYcIE\nrjzDMLCzsyvwdS9xQDYxJiYmBdQSgiDyCxJeKIIEBQXBxsaGNwGoV68eNm7ciO3btwPIG1ySkpKQ\nlJQEgB/ZQCzSUKNGjfxvfCElODgY/fr146mzsSwLQ0ND7Ny5U2ORCiMjI7i6umLkyJFwcHDA7du3\neVExsrKy4OrqioMHD2Lnzp1KDwTJQx+TYKEj2kpTUBN3gijuiJ29WJaFiYkJNm7cqFa5ixcvYsSI\nEXLfk/z/6uTkJDfPli1bMH/+fAQGBnJ26tWrxx1gvHPnjkyZe/fuca8NDAzQvHlzle0UCy+IUeYE\nfvv2bV66bdu2nLhEYXLwIAiC+J5wdnZGQEAAQkJCuPFl4sSJiI+P5xzwTE1NMXr0aKV2atasCWdn\nZ6xcuZIrd+XKFezdu1eraHuKCA0Nxe7du3nOgS1atMD48eO5PCdOnMDz588BfJtf/ffff2jZsiX8\n/PwwZMgQLq+3tzfPvqWlJXr06CFIW6dNm4bq1atjzJgxSEtL4z5fhmHg7u6OR48e4cyZM9w1TZEc\nO4XYiJMeizW1p05+SdEjMzMzpQIJFy9eRN++fTm70s7xkpibm6NNmza4desWAOD58+d4+/YtatWq\nJZP348ePCA0N5X5DZmZm+PXXX1W2nSAI7Tly5AjKli0ryDqXZF8qydq1a3HixAm0a9cOS5YsQatW\nrZTaycnJQUxMDN6/fw8zMzO0bNkSADBv3jw0btwY7u7u2Lt3r87tlcf9+/dlrrVo0UIw+2lpaby0\nubm5YLYJgiCKMkVxzV5XUlJS8Mcff2Dz5s3cHE+MWHDB1dUVP/30k8a2mzdvjv/++w/9+vXjBF4Z\nhkFISAjatm2LkydPqhyPCYIgCEKSEydOAPi2ztW7d2+5+aKiovD582dOeE/RXlZycjIvXapUKd57\nY8aMQW5uLlenoaEhdu/eDSMjYVx0WJbFtm3bEBQUxJvLiu+vX79+2LdvH3744QdB6iMIglCFonU1\nbRFqPqTJ/oa47xcafc/tCvIwFUEQBFG4SE1NzZeI1MOHD8eRI0f0Xg9BEERRRiQScf5l2sKyLCIj\nI7UqW9DCeBcvXuRei+cs4vYcOXIk34UXsrKyeG1JTk5Wy9fBw8MDzs7OXFqe8ELFihVhbm6O9PR0\nAHlrg8nJyXLH5KtXr+L69eu8dbzFixdre1sao+5vQjL4ozz/OIIgvi/Eex1i9LkOlZGRAWtra0RF\nRXH1lClTBmvWrNFbnUBeIKLU1FSULVsWhoaGMu/funWLC1Yr7r8tLCz02iaCIAoeEl7QkQ8fPsgo\nywHgHeDPzc2VGy0cyItqoCnp6ekwMzPjHJ1LlCiBAwcOwMzMjItgJ++hWLxBZGNjA3d3d43rLa6c\nPn0ao0eP5jmOsywLIyMj+Pn5YcyYMVrbbtiwIW7evAkPDw8sX74cmZmZ3CDLMAyePXuGLl26YMaM\nGVi7dq1aUQL9/Pzg5+endZvk4ePjg6lTp3IPJvPmzVP78DZBEAWLvb09bty4AYZh4OzsjIYNG2pU\nXnLiIx47FI0h0qxYsQIvXrzg0i1atEBAQABYlsWTJ0+QmJjIiywreQixQYMGMDMzU9o2lmVx69Yt\nrkz58uWVRuW5du0al9fc3BxNmzblTbo0RVcHCHJuIAiCyBPa8fPzQ+vWrTnhuI8fPwL4tpFhbW2t\nViTyhQsXYs+ePVzfzrIsXFxcMGzYMJ5jsy48e/YMubm5PAfBrVu38vKMGzcOVatWxfjx4/Hx40fu\nPr58+QJra2vMnTsXGzZsQFxcHAICAngLbaNGjRI0ktGQIUPw77//YvDgwYiLi+PVVb9+fRgaGsLV\n1RXLli3TyO79+/fRpk0bzp6BgQGys7O13pBjWRbGxsa89gUHB2t0UErVuJqeno6cnBy1hBQA2YPD\nqvJ369aNE14AgH/++QdTp06VyXf69GnenK9Tp06kbEsQeqZp06aC2PH09OT6S+mDKWXKlMGzZ8+Q\nnZ2Nz58/Izg4GPHx8fj8+TM+fPiA9+/fIzo6GtHR0Xj//j1iY2O5TacePXrg0qVLnK1+/fqhX79+\ngrRZHtKiM9WrV0fZsmUFsy92FhBDwgsEQRDf0KcDm7RTmlBos3716dMn+Pj4wMvLCwkJCbzDSQYG\nBhgyZAiWL1+us/BP9erVcePGDQwdOpRzQmMYBu/fv0fnzp1x8OBBDBo0SKc6CIIgiO+HU6dO8YRg\nu3TpIjefpIg4AIX7UpICoAzD8OZGIpEIsbGxAL6N4QsXLkSbNm10vQ0AwJMnT+Do6Ijg4GCZfT4D\nAwMsWbJEJnIeQRCEPilbtqyMII0ueHl5YcmSJQDy+tgTJ05oJWrduHFjlZFGpdH3wSR9ze0IgiAI\nQhLyWSMIgih4dBWQE8JOQfHy5Us8fPiQm/uYmJjg559/xq1bt8CyLM6ePSvjX65PWJbl+ZRJr+Up\nQ9o/QpF/mZWVFRdIEQBevXolV5h8xYoVvHSLFi00Pqskvo8zZ85o5Yeozpw0MjKSq8fKykrjOgiC\n0D+JiYmIiYlRmU/sry0mPj5e4flWSapUqYLy5csDyBNDkMTU1FSDlqpPZmYmhgwZwglOi9fRPDw8\nULlyZb3UKeb+/ftcgDUzMzOUKlUKJUqUgLGxMRITE5GYmCgzJgu150MQROGFhBd0ZPny5di9e7fS\nPF+/fpW7Ic8wDHJycpSWlfdQ27NnT9y5cwe9e/dGREQE/Pz8uAMr9evXl+nMGYZBxYoV0b59ezg4\nOGDAgAGqbuu7YevWrZg7d65MNFdjY2Ps27dPYTR4TZk/fz4GDhyI8ePHIyQkhHcQh2VZeHl54cyZ\nM9i9e7fSaO4EQRDSjBgxAjNnzkTVqlWxdOlSrWxI9knq5gXyJk2S41v79u15+W7cuIHBgwcDAGJj\nYxEREcHV065dO5V1hYaGIikpiSujzJkiLCwMsbGxXN6OHTuCYRgMGDBA5VgrjwsXLqBfv37cvXbo\n0EFp1GyCIAhCMU2aNMGyZcuwbNkyuWPNpEmT1LJjZmYGDw8PjBgxgrMTGxuLFStWwMPDQ5C2jhkz\nBl5eXggJCQHDMLC3t0fHjh1l8vXs2RMPHz7EhAkTcO7cOd5YumnTJgQHB6NZs2bIzs7m3fOECRME\naack7dq1w82bN9GnTx9O5fzXX3/Fpk2buDyabrKIVb7FlChRQvBITwYGBoKKUEgLIqraIFN3Y0xM\nt27d4Obmxn0Gf//9t1zhhQMHDgD49sw0bNgwlW0nCEI40tPT8fXrV2RnZyv9y8zMREpKisq/xMRE\nfP78GfHx8UhMTFRrbiEdafTx48f6vm2O3NxcTphP3A9JztOEgIQXCIIg+JiYmKBu3bqC2kxNTcWH\nDx+4MUXcp5cpU0bwqAkMw6glCJ2eno4LFy7Az88P586dg0gk4s0RjIyMMGrUKCxatEhtYdjs7Gyk\npKQgNTVV6XjcsWNH3L17l3PoYBgGaWlpsLa2xpYtWzB9+nTtPwCCIAjiuyA4OBhPnz7lxq5evXop\nFAcXCy+Ix19FwgvJyck8MXBJypUrBzs7O/z1119gGAYdOnTAqlWrdL6PrKwsrF69Ghs3buTGYjGS\nwq8kukAQREEg5BqRtJixqampVvY13dOoUqWKoPO7nJwczkcC+NZXm5ubo0qVKoLVA+Tdq7GxsaA2\nCYIgihL+/v6Ij49XmS8xMZGX/vz5Mzw9PdWqo3Tp0vj999+1al9BIOnjV5htEgRBFEdYlkXlypXV\nOhArjaenJxYsWAAg7zn/wYMHSgPWySMnJ4cLkFMQ7N+/n3vNMAy6d++O0aNHc4H4MjIy4Ovri3nz\n5uVLe6QPDGsyv1TXv6x27dp4+vQpl5YnvHDs2DEZfw5dAqXq6/uNj49HSkoKCS8QRCHnwIEDmDFj\nhtr5xWcmd+3ahV27dqnMv2XLFsycOROAbD+qKviqNiQmJmLgwIG4ffs2r58cPXq02j7mkmg6dxEH\nQWcYBpmZmcjMzOS9L7m+BwA1a9ZE//79NW4XQRBFCxJeEAhN1KjVzausk//xxx9x48YNnD59Gr/9\n9ht3vX79+sjOzoZIJOKcwc3MzGixS4rc3FzMnj0b3t7eMg4JJiYmOHz4MIYMGSJonQ0bNsTt27ex\nfv16rFy5EiKRiHdA6/Xr1+jWrRumTZuG9evXk+M6QRBqYW5uDltbW9ja2modTZlhGLi5uSl9+L97\n9y4cHR2V2mnbti2vT71+/TonvHDt2jUA3yYxiiIJSSKOCisu06dPH4V5T506xcurTcQLgiAIQn+4\nuLggICAA9+/f540VP/74I7p37662HRsbG/zyyy/477//uHmVt7c3HB0d0aBBA0HaunnzZnTq1AkW\nFhZKNzcqVqyIM2fOwMvLC87OzsjOzgaQN64GBQXJqK526NBB54iviqhXrx5u3bqFPn36IC4uDkeP\nHoWhoaHW9r5+/cpLqxIlKAxIO8moanNaWhovXapUKaX5f/31V5iamiIrKwssyyIwMBAxMTH4P3v3\nHdZE9vUB/DuEjkhXsKLi2pWfYFsbFqyAgA0EEbuiYu+6K6IoiKJgoVgACyAKyIriYl97R11WRQUb\nNiyIIj3vH7wZM4SShMR6Ps+Tx0yYuXMnyMzcueeeW6tWLXadhw8fsjPx8vl88Hg8DB48WMIjIYRU\nRWRkpFSdLRURvm5V9EyvrGdvDMMgMzMTmZmZ0NfXl2m9ynLy5El8+PCBUxdZJ/gUPn8yDPNDXCMI\nIUSe6tati3v37sm0zPnz52PNmjXssuD6o6enh5SUFCgqfp2uvQ8fPsDLywv//PMPrly5wmnzCAcW\nqKmpYfjw4ahZsya2bNmCnJwczuvTp0/sv8KvwsJCieojfB0WvHd3d0daWprYAfKEEEJ+TZs2bQLw\npQ9pzJgx5a579OhRznJ5MxZ9/PiRfV/Wc6Xp06cjODgYurq6iIiIqHIC0lOnTmHixIm4d++eSICf\ncBtQ2r5CQgghJZP3yFJcXBwnObOgHaOkpIRLly5BW1tbpvsjhJBfma+vr1iztgoI7qWfP3/ODm6t\nTJ06dX6oxAsAYGBggHnz5lW5nMePH8Pf35/i0Akh5BsQd3zS96KwsBBbt27lxMwNHjwYtra2UFNT\nQ25uLhvvN2PGjCrFt4mr9GQ+lcWICRM3vqxp06ZISEhgl4WTMAAlg5bnzJnD6euytLRE7969xa6L\nQFnPBKX5eXkEiWkFGjZsKGkVCSFfkSRjWSUpU1jpxAvy6IdQUlJirxuCf7t16yZWkojSpGm36Orq\nQldXV+SaUbpchmGgo6ODyMjIrxa3QQj5duivXAaEZ7ITVtFNa2WzhdrY2KBu3boAUGaWa1tbWzx6\n9AgAcObMGYSHh7M/U1BQoA71Crx69QqOjo44ceKESNIFdXV1REdHo3///nLZt4KCAhYtWoS+ffvC\nyckJqampIjPNb9y4EQkJCdi2bRssLCzkUg9CyM9lzZo10NTUlGpbwTmoXr16aN26dbnrCQYzVnTt\nql69Opo3b46UlBTw+Xz8/fff7M+OHTvGWVeSxAuCfW7ZsgWWlpaoU6eOyLqxsbGchmNFSRoIIYR8\nfTweD0FBQWjfvj0A7vVHUmvWrEHXrl3Z5YKCAkyfPh2JiYkyqWvnzp0xYMAAODo6QkdHp9L1p0+f\nDjMzMwwdOhSvXr0qN1OqIPurvBgaGuLMmTNITU2FoaFhlcp68+YNZ1lPT69K5X0NL1++ZN8zDIOa\nNWtWuL6kiRdUVVVhZWWF/fv3Ayj5PxwaGopFixax6wQGBnLadz179vwhvjtCfiY9evQAIF2nkrid\nLqXX09DQgIGBAWrWrAlDQ0MYGRmJ/KulpSVRXaS1d+9ekc9knZTu06dPnGVKvEAIIbJVXFyMnTt3\nisyICgBpaWnYvHmz3NsWApqamoiMjMSTJ08AlN/XlZubi9DQULHKLJ3QSECc67bwOsL33X5+fnj2\n7BnCw8Opb4wQQoiIZ8+eITo6mm0n6uvrw8rKqsx1MzMzcenSJXbdZs2acZJuChNOvFBWH12zZs3Q\nt29fTJkyBbVr15a6/snJyVi6dCkOHjzIXvsqC54mhBDyfdixYwdnWXD+zsrKwooVK+Dr6/stqkUI\nIT8tSZ81SVLmjzboVUBXVxezZs2qcjkXL16UeYIiQgghP6fIyEhkZGSw11AFBQUMGjQI1apVg5WV\nFaKjowGUJPUJDQ2V+cQaZSkdBydOPKBAdnY2Z7m8+IjmzZsD+HLvcOvWLc7Pvby88OjRI873IpyE\nXRKCMkxNTTF79mzOz7Zu3cpOlKiuro6goCDOz8+dO4fNmzdX+Fzx4sWLAL70xbVt21aqehJC5K+8\nsaylCbdnpOlXePXqFfteXhNMV6tWDUeOHEHPnj1x7do1dOzYEfHx8VBRUZGoHFNTU845uKCgAM+e\nPQNQcu4ta3yuwO+//47Lly9zJkJXVFSEoqIi1NTUUKtWLfTo0QNubm5VjtEmhPwYKPFCFS1cuBCj\nR48W+fzPP/9kB5lWq1YNiYmJEj18E8xeXp6rV68iIyMDfD7/qwVv/wxOnz4NR0dHPH/+XOShaN26\ndXHgwAGYmprKvR5mZma4fv06Zs6ciZCQEE5wBMMwSEtLQ69evTBx4kSsWbOGgtgJIRWSNumCPFha\nWrIZxP/991/8999/aNasGRsQxufz0bhxY9SvX7/SslxcXHDu3Dnk5+eDYRhcuXIFZmZm2Lt3Lydx\nw507d3DlyhX2vN6wYUO0atVKPgdICCFEasKzxQmuCSdPnkRoaChcXV3FLqdz586wsbFBfHw8W05S\nUhIOHDiAQYMGyaSuISEhFT5gK61Lly64evUqBg8ejEuXLon8/LfffsPgwYNlUreKaGpqyqSzIyMj\ng33PMAwMDAyqXKa8PX/+nLNcVqImYYLgeEEnkThtLldXV+zfv5/9fxcYGIgFCxZAQUEBHz58QHBw\nMCfj7uTJk6U/IEKIVIyNjaGpqYmPHz+K1VHE4/Ggrq6OatWqcV4aGhpISUlhk54yDIMVK1bAxMQE\n+vr6nJeSkpK8D0ssnz59QmRkJOe4GzdujCZNmsh8P8LomRUhhMjWoUOH8OLFC5EgCcF95sqVKzFm\nzBiJZuORFsMwcHV1xfLly6WeKUPaxEbCn6upqUFDQwMaGhqoVq0aVFVVceXKFU7yhb179+LVq1eI\ni4tD9erVJa4nIYSQn9eff/6JvLw8ACXXlYkTJ5Y7k96hQ4dQXFzMXl8qSvL94cMH9n15/XS7du2S\nOilnamoq/vjjD+zdu5eTiEk4rmDEiBG4d+8eLl++LNU+CCGkKnbv3o2RI0fKfT98Ph/9+vWrcjln\nzpyBgoKCyOehoaFwcXGpcvmlvX79GomJiWUm1ePz+di8eTNmzJhRaV8GIYQQ6VT0TKqqg44IIYQQ\nUraioiJ4enpyYqfs7e2hr68PABg3bhwnQeqKFSvg7Ows8aBaSQknXmAYptxEq2UpHR9R3nPAli1b\nsu/5fD6uXr3KLt+9exe+vr6c78XNzU2qOHd7e3s2Rs7U1BQjRozg/PzEiRNs4gUlJSWRnzdq1Igz\nWVFZSSgEiReAku+rXbt2EteTECJ/48ePr3DMqUBGRgZat27Ntn3c3d2xdOnSSrcTjkcQjs2tbEK0\nqtDU1ERSUhI8PDzg5eUFNTU1ictQUVFhk+EAJYkTiouLAZQck3DfTmnx8fGSV5oQ8lOjxAtV1LBh\nQzRs2FDkc0EDASgJ4u7UqZNM9ytoADAMI/fGxs+Az+fD29sbS5cuZYMlBJ8zDIMOHTogNjZWrjcB\npampqSEwMBADBw7EuHHjkJmZyQkUBEpmTD1+/Dhu3bpVaSD/X3/9hWvXrklVlytXrnCWz507Bw8P\nD6nK0tXVxbRp06TalhDy4xs0aBDWr1/PnsciIiLQv39/NmCcYRhYW1uLVZarqysaNmwIe3t7vHv3\nDgzD4PXr17C0tISPjw9mzJgBANi2bRuAL+f0rzGwlRBCiGRevHgBLy8vkdlN+Xw+Zs6ciV69eqFu\n3bpil+ft7Y2EhAT23t7KyorTgVFVkiRdEKhVqxZOnTqF1q1bIzU1FcCXa9PSpUt/qKCN9PR0zrIk\nv5tv5enTp5xlcRMvCIgzcK1fv34wNDTEy5cvAZTMmBgVFQVHR0ds2LABHz58YH/PdevWhY2NjSSH\nQAiRkdjYWOTl5UFZWRkqKirsS3hZVVUVGhoaSE1NBcMwaNGihUg5o0ePRlhYGPt3ffr0aSxYsECq\n8/m1a9cQGhoq19mAtm/fzp6HBNefIUOGyHw/ghkdJElcQwghRHzLli0D8OU826JFCzAMw87KkJmZ\nySZ0/hpGjx4NT09PAOIHovN4PGhqakJLSwvVq1eHlpYWtLS0oKioiLi4OPZapaenB29vb2hqaqJa\ntWrQ1NTkvDQ0NMqdMSMwMBBTp07lDI49efIkunbtijNnznxXiWoJIYTIV3FxMSIjIxEVFYUDBw5w\nfnbnzh22Xcfn86GhocH2LZUlJiYGwJfrcJ8+fcpcr6CggE0azjBMudcdaZIuPHnyBB4eHggLC0NR\nURG7D+GECy1btsSmTZvQpUsX9O/fX+J9EEKILEmbpO1bEn5+Ji+enp4oKChg96WpqYmuXbvi0KFD\nYBgGeXl5mDRpEg4ePCi3OhBCyK+Iz+fD0NCQM9GAsEePHqFBgwbs+blFixa4efNmueU1a9YM9+7d\nk1d1CSGEELn4Vm200SNLXwAAIABJREFU4OBgNgZDYN68eex7S0tLtG3blh3v8vjxY6xatYrtG5OX\n0vcFksQFCuIjBMqbMLdNmzZQVlZGQUEBgJJje/nyJfT19eHs7MwmhgVKYspWrVoldh2ErVy5Uqrt\nBExMTLB9+/YK17l06RJnokXqcyPk+6SsrAxdXd1K1/v8+TNnWU1NTazthAlPHCFNbLUktLW14efn\nJ7PySvexEEKIJCjxwjdUVFSE0NBQibfLzc1Fbm4ue+F6+fIlO+hUGs2aNcPvv/8u9fbfu/v372P0\n6NE4e/YspyEn6ERzcnLC1q1boays/E3qZ21tjeTkZDg5OeHkyZNsvQT/jh8/XqzZE+Pj46v0/0D4\nuzl37hzOnTsnVTkmJiaUeIGQX1jXrl2hp6eHt2/fAgCioqLw+vVrAF/Ou5LMRt6tWzdcuHABAwcO\nxP3798EwDIqKijBr1ixcvXoV69atw7Zt2zgNolGjRsn+wAghhFTJvHnz2NnHhYOEgZLZ4UaPHo2j\nR4+KXV6TJk0wduxYnDlzBuvXr0fv3r3lUm9JJSUl4d69e5zjbNmypViZZb8nKSkpAL5cu8tKNvi9\nefDgAYAvda5Xr16F65fuGBOnk4jH48HJyQlr165lf8ceHh7o1q0b1qxZw2nHTZ8+/YdKtkHIz6Rn\nz56VrnP9+nWsWLECcXFxaNWqFa5du1bmjHcCfD4ff//9N+bOnQtfX1+x61JYWAhPT0+sWrUKRUVF\n0NLSYgevytKnT5/KTHA0evRome9LmsQ1hBBCxBMaGopr165x2hMzZsxAjRo1YGNjw36+fft2DBw4\nELa2tnKvU/369dG7d2+kpqaiSZMmMDExQY0aNVCjRg3o6+uzSRUECRaqV69ebrKEZ8+eIS4ujl3W\n1NTEmDFjpKrXpEmToKSkhIkTJ7IzVDAMg4EDB1IAGCGE/CLy8vKwfft2+Pr6Ii0tTSQpHJ/Px7hx\n41BcXMyZSa68gMKnT58iISGBvd5qaWmhR48eZa5bekYkWV577O3tcfXq1TITLlSrVg3Lli3D9OnT\nK2zDEkLI1yJ4FiWPZ+Gymo28dJ+ULMqszL179xAUFMTpMxg9ejTc3Nxw5MgR9tp0+PBhbN68GW5u\nbnKrCyGEEEIIIeTrYxgGL168qPLzGz6fD1NTU6nrIO+Ec8JevXqFJUuWcJ5n9ejRA2ZmZpz1Fi5c\niKFDh7LrrVmzBo6OjmjSpInc6iaIKROoVauW2Nu+e/eOs1xe4gVlZWW0adMGly9fZj87ceIE/vvv\nP/ZZn+D3sWXLlipPcLFv3z706dMH1atXF2v9N2/eYNq0aTh9+jRu3boFHR2dMte7ePEiMjMz2WeT\n7du3r1I9CSFfz44dO9CuXTuJJ7B79uwZ1NXVyz0vfP78mTMpmqGhYZXqSQghPxJKvPAN5ebmYvz4\n8VJtK9wouXnzptTlACUBaj9r4gV/f38sWrQIOTk5bMNR8L0pKChg5cqVmD9//resIoCSm49jx47B\n09MTnp6eKCwsBAD07t0bs2fPlqis0scprqpkcKIBRYQQAQUFBVhZWbEzCKWmpuLRo0fsz+vXr48u\nXbpIVKaJiQkuXrwIGxsbnD17ln34tHv3bhw6dAjv379n1+3UqROaNWsms+MhhBBSdefPn8fu3bs5\nHQiNGjViE+rw+XycOHECAQEBEiXwWrt2LdTU1L6bIOOsrCxMmjRJZNDr+vXrf6j7ZT6fjxs3bnDa\nnM2bN//GtarcnTt3OMutWrWqcP3SA4fFDZB3c3ODv78/22ZLTU1F9+7d2cQiQEn7jgIlCfk+HT16\nFBs2bEBCQgL72a1bt+Dr68uZaaE0wTnRz88PpqamcHZ2rnRfN2/exKhRo5CcnMxuv2rVKpibm0uU\njE4cnp6eePnyJedaa2lpiUaNGsl0PwAlXiCEEHnJyspiA9IE9PT04OTkBBUVFXTq1Annz59nz/Xj\nx49Hu3btULt2bbnX7ciRI3LfhzTGjh0LZWVljB49Gnw+H3PmzIGXl9e3rhYhhJCvwM/PD5GRkWw7\nCABycnLw7t07NjjQ398f586dY39uYGCABQsWlFtmUFAQioqK2KBiJyencidukGfihe3bt6Ndu3Yo\nKChg23dKSkoYN24cli5dipo1a8psX6VlZmZCS0tLrEkhCCHEzs4Oqampcil769atWL16NYCS53Lb\nt29H165dJS6ne/fu7MymZmZmiIyMFFlHHufVmTNnoqCggJOYYtq0aWjUqBFGjhyJ0NBQtm03b948\n9OjRg2IcCCGEEEII+clIGytWejzHjxJz5ubmhnfv3nHaQUuWLBFZz97eHk2bNsWdO3fAMAxyc3Ph\n7OyMCxcugMfjyaVuDx8+BPBlMp8GDRqIvW1WVhYnhq68xAtAyUSDly9fZr+D9evXcxKuMwwDR0dH\n9O/fvwpHA/z7778YNmwYNDQ04OTkhDlz5sDExKTc9W/cuAFLS0u8efMGDMNgwoQJiI6OLnNdQQJ1\nQX3LS0xLCPm+vHjxAm5ubsjPz8eAAQOwcOFCscaIhoSEYO7cuejduzf27dtX5jopKSkoLi5mz21f\nYxK3oqIirFixQiZlCSZxAID8/Hx4eHjIpNy+ffuiY8eOMimLEPL9osQL3wHhm3Fp1pd2oL3wtj+j\noKAgzJgxgw2OAL40AmrVqoWwsDCxZkH8mpYuXYqePXtixIgRyMvLQ1hYmFTlfM0MhaX3Swgho0eP\nZs9fDMOgoKCAfe/i4iJVmdra2jh69ChGjhyJffv2see59+/fcx5KSZqshhBCiHzl5OTA1dWV81nT\npk1x9uxZmJqa4vHjx+x5fOHChejXrx8aN24sVtlVzfwsa66urnj27BnnujR48OAfrgMiOTmZvb4K\n/O9///uGNaocn8/nDGxmGAZt2rSpcJusrCzOsrgB8g0aNMDYsWMRGBjIfkdpaWmcfS9evBgqKirS\nHQwhRObevXuHHTt2ICgoiA0GF/6b1dTUxOfPn8Uqi8/nY8KECTAwMEDfvn3LXCcvLw+rV6/GqlWr\n2ABr4eclSUlJMk28cOvWLfj5+Yk8B1q6dKnM9iGsdGABJV4ghBDZcHJyQkZGBucatWTJEva+0t/f\nH506dUJRUREA4O3btxgwYABOnz5dYZCXvMXGxuK3335DixYtZFpuQUEBvLy84O7uXu7sGgAwcuRI\nKCkp4cKFC/D29pZpHQghhHx/BO2eDRs2sDEAwu2tBw8ewNzcHFevXsXChQs511VfX19oa2uXWW5+\nfj62bdvGWX/MmDHl1qN04gVxZ5YTR6tWrbBs2TIsWrQIPB4PI0aMgIeHB4yNjWW2j/Js27YN3t7e\nsLGxwdChQ2FpaVlu8glCCFFXV5dboLWenh5n2cjISKp9KSp+CYtUVVX9KoHhvr6+OHz4MOeaMnLk\nSDZB6ooVKxAXF8c+Y8vJycHAgQNx/vx5uSbXIYQQQgghhHw9graANP03eXl5bPwCwzCoVq2aVAkJ\nhCe0k7fAwEDExMRw2kHDhw+HhYWFyLoMw8DHxwc2NjYASr6ra9euYdasWdiwYYNc6nfv3j3OsiSJ\n7zIzMznL5T1fBIA+ffpg7dq1AEqO69KlS5w4jjp16sDf31/sfZdH8D3l5OQgJCQE1tbWFSZeaNas\nGXR1dfH27Vvw+XzExMQgNDRUJKYTAA4cOMD+HhUUFDBw4ECp65mfn0/PFgn5StauXYu8vDwwDIOE\nhATo6upWmnjByckJERERYBgGsbGxCAkJKXNC8Nu3bwP4cm1r2bKlXI5BWGFhocwSJAiTVeIFQbwh\nJV4g5Of3fUwN+gsTzuom7kt4W8GNreAiJunrZzZ+/HhYWlqywRaC72jYsGG4devWd5d0QaBz585I\nTk5GfHw8DA0NJd5ecJz79u1DUVGR3F8ZGRmUcIEQwtGtW7cyg60VFBQwevRoqctVVlZGVFQUZs6c\nWeY1rFatWrCzs5O6fEIIIbLn7u7ODnIV3Kdu3LgR2traCAoK4qz7+fNnuLi4/JD3luvWrWM7HgR0\ndHSwcePGb1gr6cTExHCW9fT08Ntvv32j2ognOTmZMwN7gwYNKh0ILG3iBaBkMLOamhrnM8H/W1NT\nU0yaNEnssggh8pGTk4OoqCgMHjwYtWvXxpw5c0Rm4NPX18fy5cvx6NEj/Pnnn5WWKXhGk5eXBxsb\nmzJnAPjrr7/QokULeHh4sLOTCq5/3bt3x+XLl2V6bfj06ROGDRuGwsJCAF+utVZWVmJlLpdGdnY2\nZ1mWA4wIIeRX5eHhgUOHDnHaE6amppg2bRq7bGZmhoULF7Lnej6fj9u3b8Pa2hp5eXnfotoICQnB\n0KFD0alTJ8TGxsqsXD6fzw4ybdWqFZKSkipc38HBAevXr5fZ/gkhhHxfzp07hytXrnA+E064oKCg\nAFtbW5w/fx7m5uZ4/fo17OzsONdHCwsLODs7l7uPgIAAvHjxgl1u3bp1hYlI3717x1mWdRKkuXPn\nYubMmbh58ybCwsK+StIFoOR52fv37xEeHg5ra2vUrl37q+yXEEJ+FqdPn8aiRYs4bTttbW34+Piw\ny7Vq1cL69es5fWGPHj1C//79Of0chBBCfizfayw2n89HXl6eTF6EEELEIxijY2hoiLdv30r8Wr58\nOae8M2fOSFzG69ev2brI26VLlzBr1izOvqpXrw4/P79yt7GysoK9vT2nz2vjxo3Ytm2bzOtXXFyM\nmzdvcurXvHlzsbd/+/Yt+15VVbXCiXi6devGxquVnjhWSUkJkZGR0NXVLXf7rKwszszsZXn37h12\n797Nfm916tTBgAEDKtxGRUUFW7duZevF5/Mxffp0PH78mLNeSkoK7ty5w67Xvn17GBgYVFh2eYqL\ni1GnTh3Mnz8faWlpUpVBCBHPu3fvEBwczP5983g8LFmypNLthg4dyr7n8/mYOXMm7t69K7Le6dOn\nOctfI/GCwPfaziKE/Doo8cI3pKGhgYKCAoled+/ehYKCAudGHACbmUjS8goKCrB58+Zv+TXIjYKC\nAiIjI1G3bl0AJQEPYWFhiIiIqDDb3PdAW1sb7du3r1IZP+KANULIz2PKlCkiiW/s7OxQv379Kpft\n6+uLJUuWiJznMjIy4Obmxs78Rwgh5NuKiYnB9u3bOdmshw0bhh49egAA+vbtCycnJ04nyqVLl364\nWUoTEhIwf/58ThuNYRgEBASgRo0a37h2kikqKkJoaCjnd9avX79vXa1KnTp1in3PMIxYg40Fs0kJ\nSJJ4wcjICFOnThW5F+HxeAgKCoKCAj1qIeRbyMjIQHh4OIYOHQoDAwM4OjoiNjaWDQYTdGw3adIE\n/v7+ePToERYvXlzpABnB33pcXBxUVVUBlGTWdnR0REhICADg/v37GDhwIAYNGoS0tDTOedTExAT7\n9+/HiRMnKhy4I6ni4mI4OzuLdHqpqqrKdfBp6cQ1lHiBEEKqJjQ0FMuXL+e0J3g8HgIDA0UCCZYu\nXYq2bdty2lBnz56FnZ0dPn369FXr7evri4kTJ4LP5+PTp08YMmSIWImMxDF58mTs378fDMMgIyMD\n/fr1w/Tp0ynAmxBCfiF5eXkIDQ2Fubk5unTpguTkZM6EFACgpKSEkSNH4tatW4iJiUH79u2Rk5OD\nQYMG4enTpwBKrqv6+vrYuXNnufvKysrCqlWrOO24+fPnV1i/0u0iWSde4PF4WLt2rUSz78mCcFC+\nYEZFQggh4vn3338xbNgwNlZBcE3x8vISGaji4uICa2trTtsuOTkZAwcOFLnGEEII+X7xeDwkJibi\n8OHDOHz4MMLCwr51lUTcvXsXampqVX5ZWFhwEuARQggp27p165CamorU1FScP39eJmVKc+7l8Xh4\n8eIFnj9/jufPn2PBggUyqUtpaWlpsLGxYftvBG2cVatWoWbNmhVu6+/vz8ZqCa4xbm5uOHDggEzr\nmJKSgpycHHZZX18f+vr6Ym374sULTjy6np5eheurqKjAzs6O8zsTfCcrV65Ep06dKtw+MDAQ9erV\nw/z583Hjxo1y1/n8+TNb7tixY8UamNy1a1e2X49hGHz8+BGTJ0/mrCMYUyaov7W1daXllicnJweZ\nmZlYs2YNGjdujClTpkhdFiGkYr6+vuwkOgzDYOjQoWjcuHGl29na2sLZ2Zk9L3z+/BkjRowQGYdz\n/Phx9jyjpKQkUfKaqpBmknNxJz+XVXmEkJ8fjQb4Rj58+IAlS5ZAQUFBotfatWtFOmkE78ePH4+s\nrCyJy/yZ6ejoYM+ePbCwsMDNmzcrnMmCEEKI7FhYWIh8Zm9vL5Oyi4uLcfz4cXZZOCAhMDAQffr0\nEZltiBBCyNf1+PFjTJw4USSb9dq1aznrrV+/nu3MEJzLPTw88O+//37V+krr2rVrcHR0ZLNNC65J\no0aNgqOj4zeuneQCAwPZwHSBwYMHf6PaiO/kyZMAvnT89OnTp9JthDOSA5INHC4oKCjz/6iioiLN\nSEXIV5aRkQF3d3e0aNECderUgaurK/bv34/c3FzOejweDwMHDkRiYiL+++8/TJkyBWpqapx13rx5\ng/z8/HL31b59e2zfvp1d5vP5mDhxIuzt7dGqVSscPnyYE3Smr6+P9evXIyUlBba2tjI86hITJ07E\ngQMHRBL/rFq1Cg0aNJD5/gDg8+fPIt8RJV4ghBDpBQUFYezYseyy4Fzu4+ODdu3aiayvqKiIffv2\noUaNGpz+ocTERHTr1o0zU7c8LV68GPPmzRNJEP727dsqB18XFhaisLCQM/gVKJmJvEOHDuxMO4QQ\nQn4Mkl4Xnj59ikWLFqFu3boYM2YMrl+/LtLmUVdXx9SpU3H//n2EhoayyQlyc3NhZWWFCxcusNcR\nBQUF7Nq1C7Vq1Sp3n15eXpznRM2aNYODg0OF9RT0QQmO72dpF5W+lxBMcEEIIZW5ePGixLFqFb3m\nzp3Lls3n89GvXz+pynn06NFXGSCanJyMHj16sAlshJORT5w4scxtQkND0bhxY067559//kHnzp3x\n5MkTudeZEEKIbPTp04d9de3a9VtXR4SsBhbR4CJCCBGPgYEBGjZsiIYNG6JevXrftC41atRgX+rq\n6jIv/+nTp7C0tBRpB9nb22PSpEmVbl+rVi34+vpyJqEtKCiAo6MjEhISZFbPS5cuse8ZhkHHjh3F\n3vb69eucZUNDw0q3EU44IfhOBg4ciDlz5lS6bVpaGjIyMrBmzRq0bdsWoaGhnJ8XFRVhy5YtnAHQ\n48ePF+NISvj4+MDIyIitW2JiIiIiIgAAHz9+xM6dOzkDnYcNGyZ22aUJ4ucEz2lLxyMSQmTj7du3\n2LhxI6dP5I8//hB7+4CAANSuXRtAyXnhxo0b8PDwYH+empqKR48eASj5e27fvj1UVFRkexBlUFFR\nQVFRkUxewhOfa2hoyKzcWbNmyf17IIR8ez/3qPvv1Llz59CmTRusXr0anz9/Fnu7M2fOICQkhL0o\nKisrY/bs2QDAzvxjb2+PgoICeVX9h9S5c2ccO3aMAgMIIeQrmj59OvtecN1atWqVSBY8aaxYsQJn\nz57lBGAL/mUYBidOnECHDh1w7969Ku+LEEKI5LKzszFw4EA2YFlwjg4MDBQJctbT08P69es5gW/5\n+fkYNWqUTK4Z8nTz5k307dtXZGbZli1bshmgfySCmd+FgyaMjIxgZWX1DWtVuczMTCQmJnI6fiwt\nLSvd7s2bN5xlcQPki4qKMGzYMBw6dEgk8D8vLw+2tra4du2ahEdBCJGWvr4+4uPjcefOHZGgL4Zh\n0KBBA/z555+4f/8+4uPjyzw/FBYWYt26dTAxMeEEdZfFwcGB7cgW/O3HxcUhPz+f3b+6ujoWLVqE\nBw8eYNq0aVBUVJTdAaMkEd3YsWOxbds2kfOQlZUV3N3dZbo/YS9fvhT5TFtbW277I4SQn5mvry9n\nJhnBudzFxQUzZ84sdztjY2McOHAAqqqqnGC069evo0OHDiJBYLJUUFAAZ2dndlZwQb0VFBTg4+OD\ngICAKgdhKyoqYuvWrdi6dSubJEnw3dy8eRPm5ubYunVrlY+FEELI1/Hs2TPOckWTQowePRoNGjTA\n6tWr8ebNG7bdJbgOaGpqYuHChUhPT8eGDRs4ff8fP36EtbU1Tp48yWmveXp6Vpig88qVK/Dz8+Ns\ns2zZskqP6/3795zln6Vd9PjxY05CwW89QIAQ8uP53gZlfo36nD9/Hr169WL7HATn0DZt2nCSuJam\no6ODgwcPQkdHh9O2S0lJQceOHXH58mW5150QQsj3r/RkApL43q7LhBBCfh5PnjxBjx49kJaWxvm8\nefPmIskCKjJ+/HiMHj2a0ybKzc2FnZ0dwsLCZFLXI0eOAPjSVuvSpYvY2wofC8Mw+O233ypcf8uW\nLfD19RW5BpuZmYm1v/T0dM7+jI2NOT/fs2cPm8CAYRg4OjqyiRTEUa1aNaxcuZITdz9r1ixkZ2cj\nPDwc2dnZbNmWlpZo2LCh2GWXVnryxIoS4xJCpLdy5UrO3+6IESPQtGlTsbfX0tLC5s2bOeeF1atX\n4+rVqwCAXbt2AfhyDu3Ro4eMj4AQQr5vso38JRUqLCyEh4cHVq9ejaKiIjZQrEOHDpVum5GRgZEj\nR3KCC8aNGwcfHx+cPXsW58+fB8MwOH36NEaMGIE9e/ZASUnpKxwVIYSQX4EkM0GEhIQgKSmJE5wF\nALdv38bKlSslyqRX2unTp7FixQpOENyoUaOQmJiIFy9esMkX7t+/j44dOyIqKkqswZeEEEJko6io\nCEOGDMG///4rcq4ePnx4mduMGDECu3btYgfP8/l8XL9+HZ6enmIFOn8LycnJ6NOnDyfQgc/no0aN\nGuwgqB/J58+fYW9vzz6EFfze5s+fDx6P941rV7GtW7ciLy+PvQdo164datSoUeE2hYWF+PDhA9vR\nxePxxMruXlRUBCcnpzJnmBf8++HDB/Tv3x+nTp2S6CE2IUQ6ysrKWLJkCSZMmMCeB3R0dGBnZ4eR\nI0eiW7duFW4fHx+PuXPnIjU1FQCwceNG9OzZE4MGDSp3mwkTJkBBQQGTJk1CcXEx53xgamqKhIQE\niTq3JfH582c4OTkhLi5OZJbxli1bsh1e8nLz5k2Rzyo75xJCCOHKzs7GmDFjsH//fpF7yh49eiAo\nKKjSMjp06IBdu3bB0dERBQUF7DXwyZMn6NixI/78808sWLCgwsGtksrKyoKtrS1OnTrFqbeqqirC\nw8MxZMgQme0LKBl8a2ZmhsGDB+Phw4fsd/T582dMmDABx44dQ0hICKpVqybT/RJCCJEtQWCzQEWJ\nL5WVldkYCuFga01NTUybNg2zZs2Cjo6OyHYZGRkYMGAAbt68yXlG4+bmhoULF5a7v7y8PLi4uHCS\nv5qbm4t1TSs9+OlnSbxQOli+dHA3IYRURDg5siwIxxlUpUx5Djr19/fH3LlzUVhYyPm8fv36OHDg\nAJtMrjyNGzdGTEwMrKys8OnTJ7Zt9/z5c3Tu3Bl//PEHFi1aJNO2HSGEEMkIJ137mudjPp+PKVOm\n4PDhw5w20osXLyQqw9DQEEuWLKlyfdLT0+Hr68uWSwghhMvR0RFRUVEyL1cQgyAr69evr/JEDsnJ\nyRgwYADnmsTn86GtrY24uDhoaGhIVN6WLVuQkpKCixcvsm2ioqIijB49Grdv34a3t7fU1+CioiKR\nWHZB4oXs7GykpqbC2NgYurq6Ittu2LAB+/bt4zxvrCj+JDw8HFOnTi0znszPzw9Tp06Fnp5ehfVN\nS0vj1FU47iw3NxdLlizh1Eea2dZHjRqFgIAAXL9+HRoaGvDw8ICKigqbMEJQtnDyeGmUnhCpdu3a\nVSqPECIqPT0dmzZtYv92lZSUpBqjY21tDSsrKxw8eBBAyblz7ty5OH78OHbv3s05L/Xq1Uumx0AI\nId87SrzwlaSkpMDZ2Rk3btzgdOpcvHix0sQLr169gqWlJR4/fsx+VqdOHaxYsQIAsHPnTrRr1w7v\n3r0DwzDYv38/+vfvj9jYWGhqasrngAghhPwUBI0hZ2dnODs7i7VuRcEJ//33H+bMmVNmYAWfz4eX\nlxcsLS3RqVMniet6584d2NnZcYLgOnfujG3btiEtLQ19+vTBw4cP2Ydv79+/x4ABA7B+/XpMmTJF\n4v0RQgiRnJubG9thIWBiYoKNGzdWuF1QUBBatGjBBpbx+XysWrUKgwYNwv/+9z95V1siJ06cgJ2d\nHZukACi5xqmrq+Ovv/764QKSc3NzMWjQIFy/fp3ze/vtt9+q3Ikib0VFRQgODubco7i6ula6nSD7\nt4A47eaPHz9iyJAh+PvvvzkPk+vUqYO2bdsiPj4eQMm9z+vXr9G5c2dER0ejZ8+ekh8YIUQiLi4u\nCAgIQOfOnWFnZ4eePXtWmjQmKSkJXl5enMGjAlFRURUmXgCAcePGQV9fHyNHjsSnT5/Yz2/cuAFX\nV1f4+fmhefPm0h9UGR4/foxBgwYhOTlZpM5169bFoUOH5P4cMDY2VuSzRo0ayXWfhBDyM7l+/TqG\nDRuGBw8eiARe9e/fH/v374eysrJYZdnb22P//v0YNmwYcnNz2edhhYWFWLJkCQ4ePIjt27fLJBnY\n/fv3YWNjgzt37nDuhXV1dREfH4/ff/+9yvsoS+vWrXHlyhWMGDECiYmJ7HfFMAyioqJw/fp1REdH\no1WrVnLZPyGEkPIVFBTgw4cP0NLSgqKiaMhJQUEBAgMDsWPHDs5zmwYNGpRb5oQJExASEsKuW716\ndbi7u2PmzJnlJja4ePEihg0bhidPnnD2M3z4cAQEBFR4DHPmzOFc25SUlLBt2zaxjr904HBZCSF+\nNE+fPsXHjx857U1KKkoIEVeHDh04fSZVtWHDBixevBhAyTP3uLg4qQK7mzdvzom1k5Xs7GyMHTuW\nHYQDfBmE2qhRIxw7dgx169YVq6zu3bvj0KFDsLa2xocPHwCAHWj0xx9/4NChQzJr2xFCCJHMtWvX\n8PLlS/Zcr6X5Yfi/AAAgAElEQVSl9VX2m5+fjxEjRiAmJobzLI7P52PGjBn4/fffUadOnUrLYRgG\n2tracHNzq3KdLl68WOYM3oQQQn4csjiHx8bGwtXVFR8/fmQ/4/P5qFatGv766y+pYgeUlZURExOD\njh07ss/4BPVdu3YtLl++jLCwMNSvX1/isk+dOoX379+zZWpra6N9+/YASp5fmpubAwBUVFRgYGAA\nLS0tKCkp4cGDB8jOzuZ8Z6qqqrCzsytzP0FBQZwY9dJx9tnZ2Vi0aFGFydfz8vLw8OFDdllHRweG\nhobssq+vL/v9MAyDAQMGSNU/xjAM1q1bh7lz52LPnj0wMTGBn58f0tPT2TrXqlULVlZWEpct7Pnz\n5wC+fBfi3LsQQiQzb9485OfnAyj52x4/frzUMVwBAQE4duwYcnNzMWTIEGzZsgUJCQnsuBwAMDQ0\nRNeuXWVWf0II+RFQSmQ5effuHYCSC1h2djbMzMzYpAuCG8gmTZpUOvPetWvX0K5dO9y5cwcA2E7/\nnTt3sgEGDRs2RGRkJHg8Hlv28ePH8b///Q8XLlyQ74ESQgj5KQgexpT3AirPWJ2VlYVBgwZxHqq1\nbt0a/fv3Z69P+fn5sLe3FxnwWJmXL19iwIABbCZxPp8PHR0d7NmzBwzDoGHDhjh79ixat27N/lwQ\nkDBnzhzcu3dPov0RQgiR3NKlSxESEsK5bqirqyMqKgrq6uoVblu3bl2sXLkSfD6f88D98+fPVa7X\n69evOctVmYkiPDwcAwYMEEm6oKKigv3796Ndu3ZSl/0tZGZmolevXjh69Cjn96asrIzw8PAyA+e/\nJwEBAUhPT2eXVVRU4ODgUOl2N27c4CzXrFmzwvUzMjLQpUsXNumCQO3atXHixAlER0ejT58+nNkY\n3717h379+ok1YzEhpGqUlZWRnJyMzZs3w9LSstykC3w+H3v37oW5uTn69u3LJl0QXHfatGmDI0eO\nYM+ePWLt19bWFmfOnEG9evU4nf9JSUlo06YNJk6ciPv378vkGOPj42FmZsbO4Co4Hj6fj3r16uHk\nyZNynyHgv//+Q0REBOc82KhRI6iqqsp1v4QQ8jPIzs7GrFmz0KFDB05wguAaZG9vj7i4OKioqEhU\nrpWVFSfxjvD96IULF9CqVStMmjQJL1++lLruhw8fRvv27XH37l1OoHejRo1w/vx5uSVdENDS0kJC\nQgIWL14MBQUFzjHeu3cPHTt2xPbt2+VaB0IIIaJev34NAwMDKCsrQ1VVFfr6+qhTpw4aNWoEY2Nj\nqKurY/r06SguLuZsZ2FhUW6ZZmZmMDU1hZaWFpYuXYr09HR4eHiUm3Rh7dq16NatG54+fcpp240Z\nMwa7d++usP7BwcGcmaAYhsGCBQvQsmVLsY4/MzOTs/w9JV4QTl4uieTkZJHPmjVrVtXqEEJ+Ierq\n6jJ7lU5Ip6KiIlU5peMdZCEiIgJNmzYVSbrAMAyaNm2K06dPo169ehKV2bVrVxw9ehR6enqcdpeg\nbde6dWu4ubnh1atXMjsOQgghXFOmTIG7uzsWLlyIP//8E9OmTUOfPn04bQZpE24LBkOJIzs7G/36\n9eMkXRC+nr148QLdu3fH3bt3y9xeVVUVoaGh2LFjB3bs2IFVq1ZJVefSGjVqxCl36tSpMimXEEJ+\nJpXFYEvykke5VVFYWIi5c+di8ODBIkkXNDQ0cOjQIXTu3Fnq8o2MjHDq1Ck0aNCAE6fOMAxOnz6N\nVq1aYcuWLZXGsJcmnGSVYRhYW1uz8SS6urpo0KABG9f+7NkzpKSkIDk5mU1OKhzHOHv2bBgYGIjs\nw8vLC5MnT+YkSlJSUuLEzfP5fGzbtg1nzpwpt6537txhn+sxDMN5Tvny5Uv4+Phw2ovLly+X6LsQ\n1r17d1y8eBEmJib48OEDvLy8OPcds2fPrlJ8JSA6KZK4yQkJIeJJSkriPJvS0NDAH3/8IXV59evX\nh7e3N8LDwxEVFQVdXV2sXLkSwJfnXg4ODpSIjRDyy6HEC3IiPFtoUVER8vPz2RtSBQUFTJ06Fdeu\nXYOpqWmZ2xcWFsLb2xtdunRhbzz5fD54PB7CwsJEEjZYWlpi586dUFRUZC9sDx8+RNeuXeHu7i4y\n68KPThYDsAghhIivsgdwxcXFcHBwYAcW8fl8qKqqIjIyEiEhIWxgHMMwePXqFWxsbNgZGyrz4sUL\n9OrVC48ePWLLVlRUxO7duzlZMGvWrIlTp07h999/Z6+5PB4Pu3btwm+//VaVwyeEEFKJBQsWYOXK\nlZwgMwUFBezatavcNk9pU6dORceOHaGgoIDJkyfj1q1bVR7E8/DhQ+Tk5HA+q169usTlFBYWwt3d\nHa6urpzACEGSgv3796Nv375VquvXdu7cOZibm+P8+fMiwYGbNm367pNIvHr1CsuWLeN0/Dg4OIg1\n08iGDRvY9wzDVJjp98KFC+jYsSNu3brF6VQzMjLC8ePH0ahRIygpKSEmJgYdOnTgdLoVFRVh8uTJ\ncHJy+una5IT8SN69e4eAgAA0adIEDg4OnGd2QEm2/q1bt+L69evo3bu3RGW3adMGN27cwPDhwzmd\n3MXFxQgJCUHTpk1hZ2eHf/75R6q6f/78GVOnToWtrS3evn3Lfi7Yj4mJCRuAIE+3bt2ClZUVCgoK\n2P0zDFPlWQ4IIeRXsHv3bjRt2hTr169nA6YE94w8Hg8eHh6Ijo6WOumZhYUFLl68iKZNm4oM0Cku\nLkZwcDBMTEywePFivHjxQqKyV61aBWtra2RlZbH1ZhgGHTp0wLlz59C4cWOJypMkyLy05cuXIyYm\nhm3PCeqSm5uLcePGYdy4ccjLy5O6fEIIIZKpVasWOzi0oKAA7969w/Pnz5Geno4nT56guLhYJKl3\ny5YtYWlpWWG527dvR3p6OpYtW1bhM57Xr1/Dz88PhYWF7D4YhsGsWbM4iWHL8vfff2Pq1KmcdVq2\nbIklS5aIffzCM8ABlSf1lCdBMjxBfdLS0qQq5/DhwyKf0ezqhJBvpXS/jrQ8PT0REBCAgIAAzJo1\nq0pl3blzB71794aTkxNevHgh0q9ibW2NCxcuwMjISKryzc3NcfnyZbRu3VqkbVdUVITAwECYmJhg\n2bJlIgnHCSGEVN2///6LjRs3wtvbG56enti0aRM7+Z7A8OHDpSpbEO8mUDrBkMCrV6/QvXt3nDx5\nktMHPn78eNja2rLLaWlpMDc3x/r160Wet/F4PLi4uLCvQYMGSVXn0vT19TnlVjbhICGE/GoiIiJQ\nVFQkk5ePjw9bLsMwuHHjhszKdnd3l/jYbt++jXbt2mHt2rWc52HCSRe6dOlS5e/Q2NgYJ0+eZGO4\nhNtEnz59wpQpU2BmZobTp0+LVV5WVhZiY2M57St7e3vOOm3atGH77Mp6CfY/bNgwLFu2jLNtYWEh\n3NzcsGTJEk77UBDbvm/fPtSvX5+9fhcXF8PFxYWdfLC027dvc5b/97//se8XL17MJrxgGAa2trac\nn0tDUGcPDw9OTFudOnXg5uZWpbIBiExSUr9+/SqXSQj54urVq2y7gmEYzJ8/HzVq1ChzXUGsV2Wm\nTp0KJycnAEBCQgIuXLjAOe87OztXsdaEEPLj+b6nr/xB7du3D69fv+bMdie4aTYyMkJ4eDh69epV\n7vYJCQlYuHAhbt++zbnZV1FRwdatW8udwdPBwQE8Hg+jRo1Cbm4ugJJA740bN2Lnzp2YMmUKJk+e\nLPcZ8L4GwQylgu9VSUnpG9fo1yVp9kBCyPeHYRhMnjxZosGt5ubmnOUxY8bgyJEjnGve2rVr0aRJ\nEwCAv78/XFxc2GvjjRs30Lt3bxw5cqTCmYAeP36MXr164cGDB5yyN27ciH79+omsr6WlhaSkJAwe\nPBiJiYnYvHkzBg8eLPZxCZSerai8WXMJIYQAs2fPhp+fn0iQmbe3N2xtbcUuh2EY7Nixg52lQRYW\nLVok8lmtWrUkKiM1NRXOzs64fPmySMC4pqYmYmJiKmzffW/y8/Ph4eGBNWvWoKioqMzf29ixY79x\nLSs3c+ZMfPjwga0/j8djf99Xr17F58+fUaNGDejp6aF69epQUFBAamoqVqxYITLLfXkdgL6+vli0\naBFngBxQMqNHYmIiJ2GDuro6jhw5Amtra5w5c4Yz+0hERASSkpKwYcMGODo6yvNrIYT8v+LiYvz9\n99/Yvn074uPj2YSowJfzXe3atTF79mxMnDgRampqUu9LW1sbe/bsgZ2dHdzd3fHq1SvObAYHDhzA\ngQMH0KhRIwwfPhzDhw9Hq1atKi33+PHjGD9+PNLS0kQCGBiGQadOnXDgwAHo6elJVN9hw4bh7t27\nqFGjBgwMDKCrqwsdHR1oaWlBQ0MDampqbPvn5cuX+Oeff3Do0CGRmWoZpmQmWUIIIaL4fD6io6Ph\n5eWFmzdvcpKZCs7j+vr62LNnj0zaEk2bNsWlS5cwZswY7Nu3jxMMxjAMcnJysGrVKvj6+mLw4MFw\nd3dHx44dyy3v7du3cHV1xcGDB0Xq7ezsjODgYKioqEhcz//++4+zLGkZNjY2OH/+PAYNGoQHDx5w\n7rm3b9+O5ORkNpCNEEKI/LVq1QonT56sdD2GYWBoaIjIyMhK1xU3gauBgQEOHz6Mrl274sOHD1BR\nUYG/vz/Gjx9f4XYXLlzAsGHDOM96dHR0EBMTI3a8wfv373H9+nV2WVtbG7q6umJtKw/CbUI+n4/L\nly/j3r17EiUkf/jwIfbs2cNpe5qYmEBDQ0OmdSWEEHHdvHmTsyztsztZBITfu3cPnp6eiIyMFOlT\nAcAm1CurP0pSxsbGOH/+PCZMmIDdu3eLtO0+ffqE5cuXw9vbGw4ODnB3d6/yYBtCCCElOnXqJDKQ\nU/g8bGdnJ1Hsg7A9e/aw5TEMw05eJCw9PR2WlpYi8XETJ07E5s2b8ebNGyQnJ+Phw4fs875Zs2bB\nx8cHLi4uGDhwINq3by/VMztCCCHft281TiM3NxcrV67EmjVrUFBQINIWqlOnDmJjY2FmZiazfdat\nWxenT5/GoEGDcPXqVfZ6CHxJQmFhYYFevXrhjz/+QNeuXcsty8/PD7m5uez2+vr66N+/P2ed9u3b\n4++//y5zQlhlZWV07NgRbm5uGDp0KOdnmZmZGDx4MP755x/O96KkpITw8HAMGTIEABAQEAAbGxt2\nu0ePHsHW1haJiYlsMlWBc+fOseUwDMNOmPT48WOEhYWx9wfKyspYtWpV5V+mGM6cOYMNGzZw7j2W\nLVtWbpKospSOIxG4du0a+55hGOq7I0TGFixYAAcHB8ybNw+XL1/GnDlzyl3333//5SxX1heSk5PD\nJq8WnBu6d+9Oz6AIIb8kSrwgY8+fP8eUKVPKDIju3bs3du3aBQMDA5HtCgsLERsbC29vb1y7do3t\nNBFsW6NGDezfv7/SQbFDhw6FsbEx7O3tkZGRwW7/4cMHeHl5wcfHB/369YO9vT1sbGy+aRBAeQoL\nC3Hu3DkYGBjAwMAAWlpa7PeZn5+PmJgYBAcHc5JSVDRo91cj+F4EjbavtU9CyI/t999/x4gRI6Ta\ndsqUKQgPD+dct0aOHIlJkyax6zg7O+Off/5hZxpiGAZXrlyBhYUFjhw5AkNDQ5FyBbOqPn36lFP2\nvHnzMGHChHLro6qqivj4eCQkJHAeWkni+PHjnOWyOr0IIYQAwcHBZSZdGD9+PGbPni1xeU2aNGGT\n9pTnwoULiIuLg56eHnR0dKCtrQ1NTU1oaGhAWVkZ+fn5ePDgAbZu3YqzZ89y7lXr1q1b5jWnPEFB\nQZgzZw5ycnJEOpCMjIxw6NAhtGnTRuLj/FYSExMxc+ZM3L17V+R4FBUVsWnTpkoD1L8HQUFBiIiI\n4NwfODo6wsTEBEDJcS5durTc7YX/TygoKIjMTvLmzRu4uroiISFB5P+2ubk5Dh48WGa7vnr16khK\nSoKTkxNiYmI4A8HevHkDJycnBAcH448//kCPHj1k8VUQQoQUFRXh7NmziI+PR2RkJDIyMgBw/+YZ\nhkGDBg0wf/58uLq6yjSR59ChQ9G/f394enrC398f+fn5nKC8hw8fwsvLC15eXjAxMUHPnj1hYWGB\n7t27c2bCe/XqFebNm8e2scp6xuji4oLAwECpAugMDQ2xb98+ibYpqw6jRo1Cy5YtJd4/IYT8zAoK\nChAREYHVq1fjzp07IgkXgC+B2gEBAVLPhFoWDQ0NREVFITw8HLNnz8bbt29FBukUFhYiMjISkZGR\naNGiBYYMGYIhQ4agRYsWbDmnT5+Gs7Mz+zxOUHdFRUX4+PhgxowZZe7/+fPnyM/Ph5GRUZkBWc+e\nPePcozMMI9Xs4M2aNcOlS5cwfPhwHD16lHPPffXqVZiZmeHAgQPo3LmzxGUTQgiRjKmpKS5evIiC\nggI2kYEwHo+HRo0awcbGBrNnzy53tiVptWrVCvv378fEiRMRGRkpkjC8tOPHj8PW1hafPn0CUHJ9\n4/F4iIiI4CTXrIyXlxcKCwvZ648sg8ul0bZtW2zfvp1dzs/PR//+/eHt7Y3OnTtXeL+RkZGBEydO\nYOnSpXj//j3nWVufPn2+RvUJIb+YjIwMZGVlwcjIqNz+94iICJFZSSXp15GVW7duwcvLC9HR0Sgu\nLubE8QElbZomTZogODhYJrO7CqiqqiI8PBz9+vXDzJkzkZmZKdK2y8/PR1hYGMLCwtC2bVsMHToU\nQ4cORcOGDWVWD0II+RFVZWCqqampSAyshoYGmjZtipEjR2LKlCllbrdz5052Qj59fX3o6uqiWrVq\nYBgGDx48wOrVq3HlyhXONaRZs2acMoqKitC9e3eR+DhHR0ds3rwZQEnCtaNHj8LS0hIPHz5k13n5\n8iV8fHzg4+MDBQUF1KtXD4aGhqhWrRqUlZWhqKgIJSUlKCoqlvsS/FxBQQEFBQXIz89Hfn4+8vLy\n2PfCr/I+z8/Px+XLlyWeCIMQQsj3Jzo6GvPnz0d6enqZsQIdO3ZETEyMVP08lTEyMsLZs2fh7u6O\n4OBgkfYQABw7dgzHjh1D27ZtMXXqVDg4OHASGbx//14kocCECRNE4kMWLFiABQsWAAA+ffqE3Nxc\n5OfnQ0VFBTo6OmWOj7lw4QIcHBzw+PFjTj+auro6oqOjOckdrKysMHnyZGzZsoVd9/Tp0+jVqxci\nIyNRt25dACUxIoKkqILjFSRQr1evHi5cuIBFixbh6NGjmDZtWoUJV9+/f8++r2h8T05ODlxdXTn3\nT82aNYOrq2u52wAl8YXCnjx5IrLO48eP2fFwfD4fxsbGNMkuIXJgbGyMvXv34v3791BUVMSZM2dQ\ns2ZN6OnpQVNTEwzDIDk5mT3PCc6HZcW9Cps7dy4ePXrEOYfMmzdPrscyevRohIWFyaVs4QQ+Hz9+\nhIKCglz2Y2Jignv37smlbELIt0OJF2SsRo0aaN68OWcGTR6Ph2XLlmHx4sUi61+/fh27d+/Gzp07\n8fr16zI7auzt7bFlyxbo6+uLVYd27drhxo0bcHNzE5ndqKioCAcPHsTBgwehoKCAFi1aoGvXrli3\nbp1E2ckqIs0gfOFtFBUVYWdnh9evX7OfKSkpQUVFBTk5OWyHlrAfabDT10LJEAgh4hDOCCqN2bNn\nsw+FBNeb9u3bIzg4WGTdjRs34vbt2zh//jx7vbt16xb+97//Yffu3ejZsye7blRUFMaNG4ecnBxO\nPadNmyZWtk4ej1dm0oX379/jwIEDqFGjBtvhpaOjg+rVq0NRUREZGRnYsWMHJ0MowzBo3ry5tF8R\nIYT81IYMGYJly5bh5cuX7DlzxIgR2LJli1z36+PjI9Z6pQfNV9Y5IJCSkoIJEybg3Llz5XYg7du3\nT6YDpeTpxo0bWLhwIY4cOVLm4C89PT1ER0fDwsLiG9ZSPBcvXsT06dM5vxcNDQ14enqyy506dQJQ\nfptIuI08duxYGBsbsz/bs2cPZsyYgczMTJH/PzY2NtizZ0+Fs2spKysjOjoas2fPxvr160U6AE+d\nOoVevXrh999/x+rVq2UakEnIr+j9+/dITEzEX3/9hcOHD7OdyGU9XzM3N8f06dPh4OAgt06UatWq\nwdvbG5MnT4a3tzfCwsKQl5fHqQcAPHjwAPfv30dwcDCMjY1x48YNVK9eHbGxsRgzZgyysrJErj9A\nSeC1v78/xo0bJ3UdW7duzamLOIQ72wXf5aZNm6SuAyGE/Gz+++8/hISEYNeuXex9ZFkJF+rUqYNN\nmzbByspKbnVxcXHBgAEDMGPGDERERIjUQVCvlJQUeHh4wMPDAxYWFkhKSkJaWhosLS3ZwaTC7YW9\ne/dWmDwsPj4ekydPBlCSkExPTw9aWlpQU1NDdnY2UlNTkZeXx3neVlmi8fJoa2sjMTERs2bNgr+/\nP+f5ZvXq1WmwESHkl6avr486deoAKDnvy3O203Xr1mHdunXscnFxMQoKCthZzqSdnVwSvXr1wr17\n9ypt48XHx8PBwQF5eXkAvjzr8fb2ZhMMxMbG4vnz56hfvz7q1asHHR0dqKurQ0NDA4qKikhPT0dA\nQAD8/f0517OBAwfK/TgrYmdnh1mzZnFmHkxPT8ewYcPELqN00j8FBQVOcnVCCJGVU6dOwcnJCUDJ\ndcLAwADq6upQU1ODsrIyHj9+jOfPn3POSfr6+mjcuPFXqd/nz58RGRmJkJAQXLhwAUDZzxmVlJQw\nf/58LFmyRG6DR0aMGIF+/fph+vTpnJnShesElMwieu3aNSxcuBBOTk7YuXOnXOpDCCHfO3V1dVSr\nVg0A2H8lMXz4cAwdOpRNKqeoqChWP8pff/1VabLr0uVYWlpylnk8HiwsLNgkDgzDoFevXiIDj4yN\njXHlyhVMmDCB3afwtYHP5+PRo0dIT0+vtN6yUPq42rVrR0kXCCG/jMzMTGRlZcml7Ddv3nCWnzx5\nAg0NDbnsq0GDBpznaklJSVi0aBGuXr1abl+Xq6srAgMD5TqQXklJCVu2bEHXrl0xadIkfPr0idMX\nJNweGjNmDKZPnw5bW1v8+eefaNiwIRYvXsyJueDxeJU+69LQ0Kjwey4uLsaKFSvg6enJjmUS7keL\nj49n49WE+fn5ISUlhR1bxjAMzp8/j2bNmmHo0KH47bffEBERwamvkZERp6/LzMwMR44cQWRkZIXP\nIl+/fo0TJ06wddPU1Cx3XXd3dzx8+JAz3k0wsWJFBEkUBdutXbsWnTt3Zut79+5djB07FgUFBex6\ngiQShBD5EPxd9u/fn008XVpZiV3KsmPHDpFxQe3atUO/fv1kXOuyyWMMZOkEfV9jH4SQnwclXpAx\nHo+HqKgomJmZ4dmzZ6hevTqioqLQt29fznorVqzAtm3b8OjRIwBld9Q0aNAAPj4+sLe3l7geenp6\niIqKQnx8PObNm4fU1FSRIG8+n4/bt2/DwsJCZkkXLC0tOUEUrVq1qnSbvn37cm7sW7VqBVNTUxw9\nepT9rLCwEIWFhSL1B0oym9NsoYQQIhkej8fJ8Mnj8STavqioCOPGjWMTFAjUr18fsbGxZV5XlJSU\nEBsbi65duyI1NZW99r18+RJ9+vRBXFwcrKys4OXlhSVLlogMdpw9e7bYA23Lo6ysjDFjxpTZwCl9\nHRY2aNCgKu2XEEJ+Vrq6uggJCYG1tTUYhoGzszNCQ0PlmgSsTZs2UFBQEOthlfA6xsbGmDlzplj7\nCAkJ4SRdEL4+TJkyBevWrRPJ4Pw9mzt3Lo4dO1bm8fTo0QPh4eE/TBBEUFAQ8vPzOQHuK1asQL16\n9dh1BDMNlvd/RPA99O7dG35+fgBKZuCdNGkSEhISRNrnPB4Py5cvx8KFC8Wu59q1a9GtWzeMHTsW\n796948zEy+fzcfHiRXYgAiFEepGRkXBzcwMg+mwNKOkcHzFiBCZNmoT//e9/X61exsbG2LJlCzw8\nPLBx40bs2LEDGRkZALjnJiUlJezduxfVq1cHAJibm0NJSanM83Xr1q2xc+dOtGzZskp1EwSqS9rp\nI6iTi4sLNm3axGlPEkLIr6qwsBADBw5EUlISAO7gF+FzuIaGBmbMmIH58+fLLThOmL6+Pnbt2oXJ\nkydj0aJFOHPmjEidBAwMDLBp0ybweDyYmJjA39+fTaDAMAzatGmDuLg4zv12WQR9QQzDIDs7G9nZ\n2ew+hQPhBHVQVlbGhAkTpD5GhmHg5+eHFi1aYOrUqcjPz4e+vj6OHDnywyTII4QQedixY8c327eC\ngoJcEz1UtN+KeHp6wsPDg70GCa5NS5cuxaxZs9j10tPTMXv27Er3VzoZ6KhRo6SsuWwYGRlh7ty5\nWLlyJefzqiTbW7hwIVq0aCGzOhJCiEDTpk0BlJxrcnNzOTNjCj9DF14WPPuTt+joaIz/P/buO67K\n8v/j+PsGRHCLW1ETR0utnH3NNFeuzPHNyL1wZGqOpCxBMck9snLirtQcaZmiuTOzXKllwxRnSmY5\nERA4vz/8cX85Mg+cwwF5PR+P85Az7uv6nCPc17mu+7o+V9++unHjhhljfBzxDMPQCy+8oIkTJyba\nrdwRvLy8tHz5cvXr109vvfWW9u/fL4vFkigBg8VikY+PT4bnUgBAdvb9999nuAwXFxebE3fXqFFD\na9asSfX7d/y5u169ekkmI33nnXf0ySefyGKxqHr16lq3bl2Sc/kKFiyoVatWae/evZo6dao2bdpk\nJotIWE9muL+N7Nq1a6bVDQDONmrUKC1cuNBh5SdsV9q0aeOwOsLCwlSuXDl98cUXCg4O1oEDBxIl\n6IzvmxUpUkRz5szRf//7X4fEk5TOnTurYcOGGjp0qNatW5eo7YmP89atW7p9+7bKly+vXbt2ae7c\nuVbzyvz8/FSmTJkMxTJ58mSNHTs2UZ+1atWq2rBhgx5KsOlPQu7u7tqwYYOaNWumgwcPmsfduXPH\nKslSwniT+4xfeeUVSdLGjRsVFBSkEiVKKH/+/PLw8FBkZKR27Nihf/75x/xskktiOGvWLC1atMiq\nzqFDhxCgWk0AACAASURBVCaZOOJ+8Rt9xMd87NgxVapUKdHrEl4T7Ny5c6rlAsi4KlWq6Mcff0z0\neMKxpHr16qlWrVrJlrFgwQKr+y4uLvrwww/tG2gyHDXn3JFz2TOzDgDOkX1WimQjxYsX1+rVq9Wt\nWzetX78+yYvSNWrU0JgxY5KchFeyZEn5+/vrtddey/BinhdffFEvvPCCFi1apKlTp+rkyZNW9ZUs\nWVLjx4/PUB0J1ahRQzVq1LDpmFq1aiVqvB9//HFzsmJKcuXKpXnz5mWrRU+ZIb7TFX/R0pFu376t\nGTNm8GUByGbatWuniIiIdB17584dvfzyy+bCROleu1KiRAlt27ZNJUuWTPbY4sWLa/v27apfv77O\nnTtnHl+2bFkziU7CgZmEE+DGjh2brngTypMnj3x8fHT69Okkn0+qXX7ppZdUp06dDNcNAA+q1q1b\nq3v37jIMI1Mmd3t6eqpChQo6depUqq+NP68/8sgjWr9+vQoWLJimOqZMmaIDBw5o3759ZjklSpTQ\nokWLMi17qz2tW7dO9erV088//yzpf4u/goODNXjw4EyLwx4TPWbOnKl9+/bp999/l3RvMHjIkCFW\nrylYsKBq1KihsLAw3b17V7GxseZFpYIFC6pq1arq1q2bubOWJO3du1ebN29OdIGsaNGiWrFihRo3\nbmxzrG3btlXNmjXVpUsX7d271+r7xejRo9WgQYP0fgwA/p+fn5+mTJmisLAw8zHDMFSrVi316NFD\n3bp1SzGLv6MVL15c48aNU1BQkLZu3arly5dr06ZN5u4X/v7+VmNiZcuW1cqVK9W8eXOrXZXeeOMN\njRs3zi7jX5UqVVKRIkUUGRmpqKgoqwl5SXFzc9Ojjz6qRo0aqW/fvnrssccyHAMAPCjc3Nw0YMAA\nqyTSCb/z5c6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prV27VuXLl3dylADSK7lBrNR2fMhI2ba4du2ann/+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\nU56enipZsmScVn8CgOTk6tWrpuJjC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6+597Bo0SL8+uuvMk9mGz16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\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yrn+0tbWxefPm0gqbZ+TIkWjZsiWePXsGIP+c3bVrV6Wvk7Kzs3l98HfffQcdHR2l6hRC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\nfmT/a2pqivbt26Njx4746aefYGJionANfX199O7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotTopics(10, 20)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 对宋词进行主题分析初探" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "宋词数据下载 http://cos.name/wp-content/uploads/2011/03/SongPoem.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T19:50:57.915939", + "start_time": "2017-09-21T19:50:57.503632" + }, + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PageAuthorTitleTitle2Sentence
00001.1和岘导引导引气和玉烛，叡化着鸿明。缇管一阳生。郊禋盛礼燔柴毕，旋轸凤凰城。森罗仪卫振华缨。载路溢欢声。皇...
10001.2和岘六州六州严夜警，铜莲漏迟迟。清禁肃，森陛戟，羽卫俨皇闱。角声励，钲鼓攸宜。金管成雅奏，逐吹逶迤。荐苍...
20001.3和岘十二时忆少年承宝运，驯致隆平。鸿庆被寰瀛。时清俗阜，治定功成。遐迩咏由庚。严郊祀，文物声明。会天正、星拱...
\n", + "
" + ], + "text/plain": [ + " Page Author Title Title2 \\\n", + "0 0001.1 和岘 导引 导引 \n", + "1 0001.2 和岘 六州 六州 \n", + "2 0001.3 和岘 十二时 忆少年 \n", + "\n", + " Sentence \n", + "0 气和玉烛，叡化着鸿明。缇管一阳生。郊禋盛礼燔柴毕，旋轸凤凰城。森罗仪卫振华缨。载路溢欢声。皇... \n", + "1 严夜警，铜莲漏迟迟。清禁肃，森陛戟，羽卫俨皇闱。角声励，钲鼓攸宜。金管成雅奏，逐吹逶迤。荐苍... \n", + "2 承宝运，驯致隆平。鸿庆被寰瀛。时清俗阜，治定功成。遐迩咏由庚。严郊祀，文物声明。会天正、星拱... " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdf = pd.read_csv('./data/SongPoem.csv', encoding = 'gb18030')\n", + "\n", + "pdf[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:24:50.900495", + "start_time": "2017-09-21T21:24:50.896679" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20692" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T19:54:52.414697", + "start_time": "2017-09-21T19:54:52.411095" + }, + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "poems = pdf.Sentence" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the corpus there are 147177 unique tokens\n" + ] + } + ], + "source": [ + "import gensim\n", + "\n", + "processed_docs = [cleancntxt(doc, stopwords) for doc in poems]\n", + "word_count_dict = gensim.corpora.Dictionary(processed_docs)\n", + "print (\"In the corpus there are\", len(word_count_dict), \"unique tokens\")\n", + "# word_count_dict.filter_extremes(no_below=5, no_above=0.2) # word must appear >5 times, and no more than 10% documents\n", + "# print \"After filtering, in the corpus there are only\", len(word_count_dict), \"unique tokens\"\n", + "bag_of_words_corpus = [word_count_dict.doc2bow(pdoc) for pdoc in processed_docs]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "tfidf = models.TfidfModel(bag_of_words_corpus )\n", + "corpus_tfidf = tfidf[bag_of_words_corpus ]\n", + "lda_model = gensim.models.LdaModel(corpus_tfidf, num_topics=20, id2word=word_count_dict, passes=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:37:37.346628", + "start_time": "2017-09-21T21:37:21.206610" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# 使用并行LDA加快处理速度。 \n", + "lda_model2 = gensim.models.ldamulticore.LdaMulticore(corpus=None, num_topics=20, id2word=word_count_dict,\\\n", + " workers=None, chunksize=2000, passes=1, \\\n", + " batch=False, alpha='symmetric', eta=None, \\\n", + " decay=0.5, offset=1.0, eval_every=10, \\\n", + " iterations=50, gamma_threshold=0.001, random_state=None)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:37:42.591267", + "start_time": "2017-09-21T21:37:42.572235" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(14,\n", + " u'0.000*\"\\u5bab\\u58f6\" + 0.000*\"\\u5a25\\u7709\" + 0.000*\"\\u5c11\\u7384\" + 0.000*\"\\u7ea2\\u8865\\u7fe0\" + 0.000*\"\\u5bd2\\u7981\" + 0.000*\"\\u6069\\u6ce2\\u6e3a\" + 0.000*\"\\u6210\\u7b11\" + 0.000*\"\\u4e00\\u65b9\" + 0.000*\"\\u632f\\u4f69\" + 0.000*\"\\u5343\\u6761\"'),\n", + " (3,\n", + " u'0.000*\"\\u7b11\\u6885\" + 0.000*\"\\u98de\\u7fe5\" + 0.000*\"\\u751a\\u5904\\u5e02\" + 0.000*\"\\u7076\\u59d4\\u5ca9\" + 0.000*\"\\u58f0\\u4e91\\u5916\" + 0.000*\"\\u8bd7\\u9b13\\u7a7a\" + 0.000*\"\\u9999\\u5e15\" + 0.000*\"\\u4e00\\u5411\" + 0.000*\"\\u559c\\u8fd1\" + 0.000*\"\\u8349\\u5e26\"'),\n", + " (9,\n", + " u'0.000*\"\\u9189\\u5f52\\u82b1\" + 0.000*\"\\u96e8\\u9701\\u9ad8\\u70df\" + 0.000*\"\\u79c1\\u81ea\" + 0.000*\"\\u5c1a\\u4e8e\" + 0.000*\"\\u5ba2\\u4e91\" + 0.000*\"\\u4ea4\\u8a89\" + 0.000*\"\\u7f18\\u529b\" + 0.000*\"\\u9ad8\\u4eba\\u53f3\" + 0.000*\"\\u7814\\u971c\" + 0.000*\"\\u751f\\u60b2\"')]" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lda_model2.print_topics(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T21:37:46.916576", + "start_time": "2017-09-21T21:37:46.866015" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 \"杯面\" \"衡任\" \"鹊声\" \"东瓯\" \"毛遂\" \"狂胡\" \"金横带\" \"为民\" \"贪欢适\" \"女骋\"\n", + "2 \"事关\" \"长不昧\" \"扑鼻\" \"印曲花\" \"千亿\" \"悲似\" \"成絮\" \"绿须\" \"辗柔茵\" \"中眉\"\n", + "3 \"此等\" \"半疑\" \"菜传\" \"羞郎觑\" \"工艺\" \"翠翘花\" \"苦自\" \"闲发\" \"正梅粉\" \"愁梦欲\"\n", + "4 \"笑梅\" \"飞翥\" \"甚处市\" \"灶委岩\" \"声云外\" \"诗鬓空\" \"香帕\" \"一向\" \"喜近\" \"草带\"\n", + "5 \"合姓\" \"入户\" \"生青雾\" \"千掌\" \"佳兆\" \"平镜\" \"脉脉\" \"几间\" \"留春语\" \"先递\"\n", + "6 \"松路\" \"菊荒\" \"苎萝\" \"涌大\" \"波平岸\" \"任城\" \"题桐叶\" \"三三五五\" \"望仙官\" \"景疏\"\n", + "7 \"幽欢整\" \"虔祈\" \"步鸯\" \"开口笑\" \"怨深\" \"列郡\" \"风拂罗衣\" \"似途\" \"恨苦\" \"情忠武\"\n", + "8 \"粟粟\" \"报临\" \"摩孩罗\" \"半嗔\" \"虚野\" \"倚定\" \"欲语\" \"夷夏高仰\" \"看君行\" \"未伊瘦损\"\n", + "9 \"刺萦\" \"适忘鱼\" \"困流霞\" \"犀隐\" \"只弹\" \"幼稚\" \"花阴淡\" \"恐山深\" \"盘山\" \"今底\"\n", + "10 \"醉归花\" \"雨霁高烟\" \"私自\" \"尚于\" \"客云\" \"交誉\" \"缘力\" \"高人右\" \"研霜\" \"生悲\"\n", + "11 \"休为\" \"迷舞凤\" \"惬邻\" \"愁味\" \"解禁\" \"一物\" \"亭北\" \"催庭树\" \"梦翠翘\" \"披蕊\"\n", + "12 \"老此\" \"横塘处\" \"这闲福\" \"初不悟\" \"花满碧蹊归\" \"纵巧\" \"放荡\" \"歌者\" \"要称\" \"先泪\"\n", + "13 \"筹密边\" \"任碧罗\" \"犹闻\" \"秋霁碧\" \"瘦千崖\" \"翠如葱\" \"休争\" \"爱此\" \"辅盈成\" \"蒸民\"\n", + "14 \"送日眺\" \"事皆非\" \"顶头\" \"储秀降\" \"济水\" \"良日\" \"辜伊\" \"岫边\" \"若耶溪\" \"空歇\"\n", + "15 \"宫壶\" \"娥眉\" \"少玄\" \"红补翠\" \"寒禁\" \"恩波渺\" \"成笑\" \"一方\" \"振佩\" \"千条\"\n", + "16 \"开景运\" \"休治\" \"争映\" \"明时\" \"念羁\" \"天末家\" \"点墨\" \"春权\" \"丝弦\" \"盈畴\"\n", + "17 \"情念骤\" \"上林\" \"侵染\" \"香高烛\" \"心许\" \"裂石\" \"兽烟\" \"麦光\" \"符梦\" \"尘想\"\n", + "18 \"别郎\" \"庐中\" \"不待禁\" \"整冠落\" \"同摘\" \"穿线\" \"细草芳\" \"村姑\" \"季真非\" \"待取\"\n", + "19 \"云里认\" \"一枕松风\" \"雨惜\" \"西瑶\" \"调冰荐\" \"已生些\" \"谢郎池\" \"散场\" \"锅汤\" \"赋里\"\n", + "20 \"访隐\" \"劳君\" \"尹字\" \"火力\" \"入轻\" \"衷肠\" \"绝境\" \"地来\" \"清颍咽\" \"心与秋空\"\n" + ] + } + ], + "source": [ + "topictermlist = lda_model2.print_topics(-1)\n", + "top_words = [[j.split('*')[1] for j in i[1].split(' + ')] for i in topictermlist] \n", + "for k, i in enumerate(top_words): \n", + " print (k+1, \" \".join(i) )" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "perplexity_list = [fastInferTopicNumber(bag_of_words_corpus, num, word_count_dict) for num in [5, 15, 20, 25, 30, 35, 40 ]]" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([5, 15, 20, 25, 30, 35, 40], perplexity_list)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:09:50.079692", + "start_time": "2017-09-21T22:08:45.637001" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import pyLDAvis.gensim\n", + "\n", + "song_data = pyLDAvis.gensim.prepare(lda_model, bag_of_words_corpus, word_count_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "ExecuteTime": { + "end_time": "2017-09-21T22:49:54.162572", + "start_time": "2017-09-21T22:29:23.068167" + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Note: if you're in the IPython notebook, pyLDAvis.show() is not the best command\n", + " to use. Consider using pyLDAvis.display(), or pyLDAvis.enable_notebook().\n", + " See more information at http://pyLDAvis.github.io/quickstart.html .\n", + "\n", + "You must interrupt the kernel to end this command\n", + "\n", + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [21/Sep/2017 22:29:23] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [21/Sep/2017 22:29:23] \"GET /LDAvis.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [21/Sep/2017 22:29:23] \"GET /d3.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [21/Sep/2017 22:29:23] \"GET /LDAvis.js HTTP/1.1\" 200 -\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "pyLDAvis.enable_notebook()\n", + "pyLDAvis.show(song_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# 阅读材料" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Willi Richert, Luis Pedro Coelho, 2013, Building Machine Learning Systems with Python. Chapter 4. Packt Publishing.\n", + "\n", + "LDA Experiments on the English Wikipedia https://radimrehurek.com/gensim/wiki.html#latent-dirichlet-allocation\n", + "\n", + "东风夜放花千树：对宋词进行主题分析初探 https://chengjunwang.com/zh/post/cn/2013-09-27-topic-modeling-of-song-peom/\n", + "\n", + "Chandra Y, Jiang LC, Wang C-J (2016) Mining Social Entrepreneurship Strategies Using Topic Modeling. PLoS ONE 11(3): e0151342. doi:10.1371/journal.pone.0151342\n", + "\n", + "https://rare-technologies.com/tutorial-on-mallet-in-python/" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 0, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": false, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "47px", + "left": "1035px", + "top": "44.8438px", + "width": "159px" + }, + "toc_section_display": false, + "toc_window_display": false + }, + "toc_position": { + "height": "364px", + "left": "1226.03px", + "right": "20px", + "top": "120px", + "width": "193px" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebook/.ipynb_checkpoints/10-07-bert-topic-checkpoint.ipynb b/notebook/.ipynb_checkpoints/10-07-bert-topic-checkpoint.ipynb deleted file mode 100644 index 5080b5b..0000000 --- a/notebook/.ipynb_checkpoints/10-07-bert-topic-checkpoint.ipynb +++ /dev/null @@ -1,39 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "bebe2120", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/.ipynb_checkpoints/BERTopic-checkpoint.ipynb b/notebook/.ipynb_checkpoints/BERTopic-checkpoint.ipynb new file mode 100644 index 0000000..6fb35a4 --- /dev/null +++ b/notebook/.ipynb_checkpoints/BERTopic-checkpoint.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "40d39dec", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# BERTopic\n", + "\n", + "BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.\n", + "\n", + "https://maartengr.github.io/BERTopic/" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "55c78235", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:59:18.174535Z", + "start_time": "2024-08-03T07:59:17.777485Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channels:\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + " - https://mirrors.ustc.edu.cn/anaconda/pkgs/main/\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/\n", + " - https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/\n", + " - https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/\n", + " - defaults\n", + "\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "conda config --show channels" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "96c95d53", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T08:01:32.972110Z", + "start_time": "2024-08-03T07:59:54.222619Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting package metadata (current_repodata.json): done\n", + "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n", + "Collecting package metadata (repodata.json): | ^C\n", + "- " + ] + } + ], + "source": [ + "# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + "!conda install hdbscan" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b72c2063", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:51:19.265753Z", + "start_time": "2024-08-03T07:50:38.295860Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting hdbscan\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9d/47/a6493a4e17cc45220a0b5de012641b81f57272961570a4ab99fcdf727c38/hdbscan-0.8.37.tar.gz (5.2 MB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: cython<3,>=0.27 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (0.29.23)\n", + "Requirement already satisfied: joblib>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.0.1)\n", + "Requirement already satisfied: scipy>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.6.2)\n", + "Requirement already satisfied: numpy<2,>=1.20 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.20.0)\n", + "Requirement already satisfied: scikit-learn>=0.20 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.20->hdbscan) (2.1.0)\n", + "Building wheels for collected packages: hdbscan\n", + " Building wheel for hdbscan (pyproject.toml) ... \u001b[?25lerror\n", + " \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", + " \n", + " \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for hdbscan \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n", + " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", + " \u001b[31m╰─>\u001b[0m \u001b[31m[48 lines of output]\u001b[0m\n", + " \u001b[31m \u001b[0m running bdist_wheel\n", + " \u001b[31m \u001b[0m running build\n", + " \u001b[31m \u001b[0m running build_py\n", + " \u001b[31m \u001b[0m creating build\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/validity.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/prediction.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/plots.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/hdbscan_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/robust_single_linkage_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_prediction_utils.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_rsl.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_hdbscan.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m running build_ext\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_tree.pyx to hdbscan/_hdbscan_tree.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_linkage.pyx to hdbscan/_hdbscan_linkage.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_boruvka.pyx to hdbscan/_hdbscan_boruvka.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_reachability.pyx to hdbscan/_hdbscan_reachability.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_prediction_utils.pyx to hdbscan/_prediction_utils.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/dist_metrics.pyx to hdbscan/dist_metrics.c\n", + " \u001b[31m \u001b[0m building 'hdbscan._hdbscan_tree' extension\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include/python3.8 -I/private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/numpy/core/include -c hdbscan/_hdbscan_tree.c -o build/temp.macosx-10.9-x86_64-cpython-38/hdbscan/_hdbscan_tree.o\n", + " \u001b[31m \u001b[0m xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'test_suite'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'tests_require'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_tree.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_linkage.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_boruvka.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_reachability.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_prediction_utils.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/dist_metrics.pxd\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m error: command '/usr/bin/gcc' failed with exit code 1\n", + " \u001b[31m \u001b[0m \u001b[31m[end of output]\u001b[0m\n", + " \n", + " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", + "\u001b[31m ERROR: Failed building wheel for hdbscan\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[?25hFailed to build hdbscan\n", + "\u001b[31mERROR: Could not build wheels for hdbscan, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", + "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U hdbscan" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "468ac5ed", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:34:09.105736Z", + "start_time": "2024-08-03T07:33:28.292626Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting bertopic\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/34/38/a3b97cfc8346683d1498ffe7dc2c58265d1dea980d89c769b6b74e01e35c/bertopic-0.16.3-py3-none-any.whl (143 kB)\n", + "Collecting hdbscan>=0.8.29\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9d/47/a6493a4e17cc45220a0b5de012641b81f57272961570a4ab99fcdf727c38/hdbscan-0.8.37.tar.gz (5.2 MB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hCollecting plotly>=4.7.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b8/f0/bcf716a8e070370d6598c92fcd328bd9ef8a9bda2c5562da5a835c66700b/plotly-5.23.0-py3-none-any.whl (17.3 MB)\n", + "Requirement already satisfied: pandas>=1.1.5 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.4.3)\n", + "Requirement already satisfied: sentence-transformers>=0.4.1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (3.0.1)\n", + "Collecting umap-learn>=0.5.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d1/1b/46802a050b1c55d10c4f59fc6afd2b45ac9b4f62b2e12092d3f599286f14/umap_learn-0.5.6-py3-none-any.whl (85 kB)\n", + "Requirement already satisfied: tqdm>=4.41.1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (4.59.0)\n", + "Requirement already satisfied: numpy>=1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.20.0)\n", + "Requirement already satisfied: scikit-learn>=0.22.2.post1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.0)\n", + "Requirement already satisfied: scipy>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (1.6.2)\n", + "Requirement already satisfied: joblib>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (1.0.1)\n", + "Requirement already satisfied: cython<3,>=0.27 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (0.29.23)\n", + "Requirement already satisfied: pytz>=2020.1 in 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transformers<5.0.0,>=4.34.0 in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (4.43.3)\n", + "Requirement already satisfied: huggingface-hub>=0.15.1 in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (0.24.5)\n", + "Collecting pynndescent>=0.5\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d2/53/d23a97e0a2c690d40b165d1062e2c4ccc796be458a1ce59f6ba030434663/pynndescent-0.5.13-py3-none-any.whl (56 kB)\n", + "Requirement already satisfied: numba>=0.51.2 in /opt/anaconda3/lib/python3.8/site-packages (from umap-learn>=0.5.0->bertopic) (0.53.1)\n", + "Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (3.0.12)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) 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python-dateutil>=2.8.1->pandas>=1.1.5->bertopic) (1.15.0)\n", + "Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (2.5)\n", + "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (2.11.3)\n", + "Requirement already satisfied: sympy in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.8)\n", + "Requirement already satisfied: regex!=2019.12.17 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (2021.4.4)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (0.4.3)\n", + "Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (0.19.1)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.1.1)\n", + "Requirement already satisfied: decorator>=4.3.0 in /opt/anaconda3/lib/python3.8/site-packages (from networkx->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (5.0.9)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (1.26.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2024.6.2)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (4.0.0)\n", + "Requirement already satisfied: idna<3,>=2.5 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2.10)\n", + "Requirement already satisfied: mpmath>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from sympy->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.2.1)\n", + "Building wheels for collected packages: hdbscan\n", + " Building wheel for hdbscan (pyproject.toml) ... \u001b[?25lerror\n", + " \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", + " \n", + " \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for hdbscan \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n", + " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", + " \u001b[31m╰─>\u001b[0m \u001b[31m[48 lines of output]\u001b[0m\n", + " \u001b[31m \u001b[0m running bdist_wheel\n", + " \u001b[31m \u001b[0m running build\n", + " \u001b[31m \u001b[0m running build_py\n", + " \u001b[31m \u001b[0m creating build\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/validity.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/prediction.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/plots.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/hdbscan_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/robust_single_linkage_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_prediction_utils.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_rsl.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_hdbscan.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m running build_ext\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_tree.pyx to hdbscan/_hdbscan_tree.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_linkage.pyx to hdbscan/_hdbscan_linkage.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_boruvka.pyx to hdbscan/_hdbscan_boruvka.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_reachability.pyx to hdbscan/_hdbscan_reachability.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_prediction_utils.pyx to hdbscan/_prediction_utils.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/dist_metrics.pyx to hdbscan/dist_metrics.c\n", + " \u001b[31m \u001b[0m building 'hdbscan._hdbscan_tree' extension\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include/python3.8 -I/private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/numpy/core/include -c hdbscan/_hdbscan_tree.c -o build/temp.macosx-10.9-x86_64-cpython-38/hdbscan/_hdbscan_tree.o\n", + " \u001b[31m \u001b[0m xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'test_suite'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'tests_require'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_tree.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_linkage.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_boruvka.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_reachability.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_prediction_utils.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/dist_metrics.pxd\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m error: command '/usr/bin/gcc' failed with exit code 1\n", + " \u001b[31m \u001b[0m \u001b[31m[end of output]\u001b[0m\n", + " \n", + " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", + "\u001b[31m ERROR: Failed building wheel for hdbscan\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[?25hFailed to build hdbscan\n", + "\u001b[31mERROR: Could not build wheels for hdbscan, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", + "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# 使用清华大学镜像快速安装Python包 \n", + "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U bertopic\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c51277b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "幻灯片", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebook/08-04-RNN.ipynb b/notebook/08-04-RNN.ipynb index 9a8e133..0b41765 100644 --- a/notebook/08-04-RNN.ipynb +++ b/notebook/08-04-RNN.ipynb @@ -140,8 +140,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:42:33.912449Z", - "start_time": "2021-08-08T03:42:32.179866Z" + "end_time": "2024-08-04T01:30:51.492041Z", + "start_time": "2024-08-04T01:30:48.434235Z" }, "slideshow": { "slide_type": "subslide" @@ -165,8 +165,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:43:04.565161Z", - "start_time": "2021-08-08T03:43:04.557772Z" + "end_time": "2024-08-04T01:31:21.554155Z", + "start_time": "2024-08-04T01:31:21.535124Z" }, "slideshow": { "slide_type": "subslide" @@ -186,8 +186,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:44:19.681351Z", - "start_time": "2021-08-08T03:44:19.657797Z" + "end_time": "2024-08-04T01:32:32.451194Z", + "start_time": "2024-08-04T01:32:32.396615Z" }, "slideshow": { "slide_type": "subslide" @@ -218,8 +218,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:45:10.351689Z", - "start_time": "2021-08-08T03:45:10.342799Z" + "end_time": "2024-08-04T01:33:29.642052Z", + "start_time": "2024-08-04T01:33:29.631285Z" }, "slideshow": { "slide_type": "subslide" @@ -257,8 +257,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:45:14.820226Z", - "start_time": "2021-08-08T03:45:14.802268Z" + "end_time": "2024-08-04T01:33:34.010278Z", + "start_time": "2024-08-04T01:33:33.968173Z" }, "slideshow": { "slide_type": "subslide" @@ -301,8 +301,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:45:22.947640Z", - "start_time": "2021-08-08T03:45:22.929000Z" + "end_time": "2024-08-04T01:33:37.516565Z", + "start_time": "2024-08-04T01:33:37.510947Z" }, "slideshow": { "slide_type": "subslide" @@ -312,9 +312,9 @@ { "data": { "text/plain": [ - "tensor([[[ 0.4814, 0.2471],\n", - " [ 0.5347, -0.7445],\n", - " [-0.1018, -0.1504]]], grad_fn=)" + "tensor([[[-0.5909, 0.1073],\n", + " [ 0.2635, -0.3354],\n", + " [ 0.5518, -0.5265]]], grad_fn=)" ] }, "execution_count": 6, @@ -331,8 +331,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:45:28.583283Z", - "start_time": "2021-08-08T03:45:28.576295Z" + "end_time": "2024-08-04T01:33:45.322873Z", + "start_time": "2024-08-04T01:33:45.317899Z" }, "slideshow": { "slide_type": "subslide" @@ -342,23 +342,23 @@ { "data": { "text/plain": [ - "tensor([[[ 0.9635, -0.7152],\n", - " [ 0.6888, 0.4281],\n", - " [-0.0014, -0.0229],\n", - " [ 0.1338, -0.5729],\n", - " [ 0.4814, 0.2471]],\n", + "tensor([[[-0.5776, 0.0029],\n", + " [ 0.0410, 0.5644],\n", + " [ 0.6657, -0.5857],\n", + " [-0.0645, -0.1888],\n", + " [-0.5909, 0.1073]],\n", "\n", - " [[ 0.6749, 0.3051],\n", - " [ 0.0923, 0.7774],\n", - " [-0.3299, -0.1518],\n", - " [ 0.1451, -0.7179],\n", - " [ 0.5347, -0.7445]],\n", + " [[ 0.4553, 0.2731],\n", + " [-0.4560, -0.0468],\n", + " [ 0.4745, -0.4278],\n", + " [ 0.0765, -0.2564],\n", + " [ 0.2635, -0.3354]],\n", "\n", - " [[-0.6955, 0.2545],\n", - " [-0.1804, -0.7042],\n", - " [ 0.5372, -0.1592],\n", - " [ 0.3898, 0.4898],\n", - " [-0.1018, -0.1504]]], grad_fn=)" + " [[ 0.5659, -0.4668],\n", + " [ 0.0325, -0.2379],\n", + " [-0.2366, 0.6431],\n", + " [ 0.3623, 0.3852],\n", + " [ 0.5518, -0.5265]]], grad_fn=)" ] }, "execution_count": 7, @@ -408,8 +408,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:47:41.988571Z", - "start_time": "2021-08-08T03:47:41.976267Z" + "end_time": "2024-08-04T01:36:38.102963Z", + "start_time": "2024-08-04T01:36:38.093656Z" }, "slideshow": { "slide_type": "subslide" @@ -443,8 +443,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:48:19.550071Z", - "start_time": "2021-08-08T03:48:19.540423Z" + "end_time": "2024-08-04T01:37:28.526281Z", + "start_time": "2024-08-04T01:37:28.520517Z" }, "slideshow": { "slide_type": "subslide" @@ -481,8 +481,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:49:25.862970Z", - "start_time": "2021-08-08T03:49:25.856073Z" + "end_time": "2024-08-04T01:38:39.856397Z", + "start_time": "2024-08-04T01:38:39.852379Z" }, "slideshow": { "slide_type": "subslide" @@ -514,8 +514,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:49:30.778971Z", - "start_time": "2021-08-08T03:49:30.770671Z" + "end_time": "2024-08-04T01:38:57.897406Z", + "start_time": "2024-08-04T01:38:55.797416Z" }, "slideshow": { "slide_type": "subslide" @@ -548,9 +548,12 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T03:49:40.247015Z", - "start_time": "2021-08-08T03:49:39.666190Z" + "end_time": "2024-08-04T01:39:55.217320Z", + "start_time": "2024-08-04T01:39:54.696394Z" }, + "code_folding": [ + 0 + ], "slideshow": { "slide_type": "subslide" } @@ -681,10 +684,8 @@ " loss += criterion(output, label.view(-1))\n", "\n", " print(\", epoch: %d, loss: %1.3f\" % (epoch + 1, loss.item()))\n", - "\n", " loss.backward()\n", " optimizer.step()\n", - "\n", "print(\"Learning finished!\")" ] }, @@ -703,11 +704,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-06-01T08:25:01.865952Z", - "start_time": "2020-06-01T08:25:01.860982Z" + "end_time": "2024-08-04T01:41:25.990538Z", + "start_time": "2024-08-04T01:41:25.986523Z" }, "slideshow": { "slide_type": "subslide" @@ -732,11 +733,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-06-01T08:25:02.313130Z", - "start_time": "2020-06-01T08:25:02.309602Z" + "end_time": "2024-08-04T01:41:39.250448Z", + "start_time": "2024-08-04T01:41:39.246803Z" }, "slideshow": { "slide_type": "subslide" @@ -758,11 +759,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-08-14T02:24:12.800185Z", - "start_time": "2020-08-14T02:24:12.794329Z" + "end_time": "2024-08-04T01:42:16.142189Z", + "start_time": "2024-08-04T01:42:16.137379Z" }, "slideshow": { "slide_type": "subslide" @@ -795,11 +796,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-06-01T08:25:03.394452Z", - "start_time": "2020-06-01T08:25:03.389212Z" + "end_time": "2024-08-04T01:42:31.259841Z", + "start_time": "2024-08-04T01:42:31.254156Z" }, "slideshow": { "slide_type": "subslide" @@ -1080,11 +1081,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T15:53:19.862473Z", - "start_time": "2021-08-08T15:53:19.856431Z" + "end_time": "2024-08-04T01:46:20.029212Z", + "start_time": "2024-08-04T01:46:20.020373Z" }, "slideshow": { "slide_type": "subslide" @@ -1094,13 +1095,13 @@ { "data": { "text/plain": [ - "tensor([[[ 0.5150, -0.6074, 2.5425, -0.0226, 0.2476]],\n", + "tensor([[[-0.0815, -0.4436, 1.8614, -0.5239, -1.3621]],\n", "\n", - " [[ 1.2734, -0.1155, 1.2753, 2.0634, -1.2319]]],\n", - " grad_fn=)" + " [[ 1.2612, -1.4771, -0.2260, -0.8251, 0.1337]]],\n", + " grad_fn=)" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1122,11 +1123,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T14:45:43.634856Z", - "start_time": "2021-08-08T14:45:43.625524Z" + "end_time": "2024-08-04T01:47:08.722161Z", + "start_time": "2024-08-04T01:47:08.711391Z" }, "slideshow": { "slide_type": "subslide" @@ -1154,11 +1155,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T14:45:47.211100Z", - "start_time": "2021-08-08T14:45:47.207674Z" + "end_time": "2024-08-04T01:47:16.211835Z", + "start_time": "2024-08-04T01:47:16.209292Z" }, "slideshow": { "slide_type": "subslide" @@ -1178,11 +1179,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T14:45:47.838996Z", - "start_time": "2021-08-08T14:45:47.833208Z" + "end_time": "2024-08-04T01:48:16.860329Z", + "start_time": "2024-08-04T01:48:16.852391Z" }, "slideshow": { "slide_type": "subslide" @@ -1216,11 +1217,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2021-08-08T14:45:48.702559Z", - "start_time": "2021-08-08T14:45:48.695446Z" + "end_time": "2024-08-04T01:48:33.555373Z", + "start_time": "2024-08-04T01:48:33.549594Z" }, "slideshow": { "slide_type": "subslide" @@ -1258,6 +1259,7 @@ "end_time": "2021-08-08T14:45:50.112980Z", "start_time": "2021-08-08T14:45:49.997065Z" }, + "code_folding": [], "slideshow": { "slide_type": "subslide" } diff --git a/notebook/09-word2vec.ipynb b/notebook/09-word2vec.ipynb index 0a2a3ec..f58616d 100644 --- a/notebook/09-word2vec.ipynb +++ b/notebook/09-word2vec.ipynb @@ -208,11 +208,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2021-08-09T02:04:52.213426Z", - "start_time": "2021-08-09T02:04:52.208807Z" + "end_time": "2024-08-04T03:19:10.706800Z", + "start_time": "2024-08-04T03:19:10.587677Z" }, "slideshow": { "slide_type": "subslide" @@ -260,11 +260,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:25.270510Z", - "start_time": "2024-08-01T05:57:25.255369Z" + "end_time": "2024-08-04T03:20:38.593334Z", + "start_time": "2024-08-04T03:20:38.549440Z" }, "slideshow": { "slide_type": "subslide" @@ -277,7 +277,7 @@ "-0.4999999999999999" ] }, - "execution_count": 12, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -297,11 +297,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2021-08-09T02:07:13.165583Z", - "start_time": "2021-08-09T02:07:13.155548Z" + "end_time": "2024-08-04T03:20:47.960736Z", + "start_time": "2024-08-04T03:20:47.894750Z" }, "slideshow": { "slide_type": "subslide" @@ -314,7 +314,7 @@ "array([[-0.5]])" ] }, - "execution_count": 9, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -338,11 +338,11 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 24, "metadata": { "ExecuteTime": { - "end_time": "2021-08-09T02:07:45.266255Z", - "start_time": "2021-08-09T02:07:45.260211Z" + "end_time": "2024-08-04T03:21:42.789378Z", + "start_time": "2024-08-04T03:21:42.771898Z" }, "slideshow": { "slide_type": "fragment" @@ -355,7 +355,7 @@ "-0.5" ] }, - "execution_count": 31, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -369,11 +369,11 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2021-08-09T02:08:02.664267Z", - "start_time": "2021-08-09T02:08:02.658116Z" + "end_time": "2024-08-04T03:21:52.649531Z", + "start_time": "2024-08-04T03:21:52.643475Z" }, "slideshow": { "slide_type": "subslide" @@ -386,7 +386,7 @@ "0.6582337075311759" ] }, - "execution_count": 32, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -397,11 +397,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": { "ExecuteTime": { - "end_time": "2021-08-09T02:08:09.150782Z", - "start_time": "2021-08-09T02:08:09.140213Z" + "end_time": "2024-08-04T03:21:58.847464Z", + "start_time": "2024-08-04T03:21:58.842663Z" }, "slideshow": { "slide_type": "fragment" @@ -414,7 +414,7 @@ "-0.3683509554826695" ] }, - "execution_count": 33, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -484,11 +484,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:56:36.801457Z", - "start_time": "2024-08-01T05:56:13.779269Z" + "end_time": "2024-08-04T03:24:10.076477Z", + "start_time": "2024-08-04T03:23:35.013799Z" }, "slideshow": { "slide_type": "subslide" @@ -504,11 +504,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:56:43.236363Z", - "start_time": "2024-08-01T05:56:43.225919Z" + "end_time": "2024-08-04T03:24:54.321178Z", + "start_time": "2024-08-04T03:24:54.314183Z" }, "slideshow": { "slide_type": "subslide" @@ -518,25 +518,27 @@ { "data": { "text/plain": [ - "array([ 0.24316406, -0.07714844, -0.10302734], dtype=float32)" + "array([ 0.24316406, -0.07714844, -0.10302734, -0.10742188, 0.11816406,\n", + " -0.10742188, -0.11425781, 0.02563477, 0.11181641, 0.04858398],\n", + " dtype=float32)" ] }, - "execution_count": 8, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model['woman'][:3]" + "model['woman'][:10]" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:08.514575Z", - "start_time": "2024-08-01T05:56:44.712674Z" + "end_time": "2024-08-04T03:26:23.195463Z", + "start_time": "2024-08-04T03:26:22.920970Z" }, "slideshow": { "slide_type": "subslide" @@ -546,34 +548,34 @@ { "data": { "text/plain": [ - "[('Socrates', 0.5317621231079102),\n", - " ('Epicurus', 0.5069470405578613),\n", - " ('Aristotle', 0.5054760575294495),\n", - " ('Plato_dialogues', 0.5022973418235779),\n", - " ('Epictetus', 0.5015553832054138),\n", - " ('Parmenides', 0.49688035249710083),\n", - " ('Democritus', 0.49514809250831604),\n", - " ('Protagoras', 0.4905695915222168),\n", - " ('Greek_philosopher_Plato', 0.48871928453445435),\n", - " ('Critias', 0.4856548011302948)]" + "[('Plato_Apology', 0.5661361217498779),\n", + " ('philosopher_Socrates', 0.561074435710907),\n", + " ('Aristotle', 0.5599992275238037),\n", + " ('Parmenides', 0.5416265726089478),\n", + " ('Sophists', 0.5381807088851929),\n", + " ('Sócrates', 0.533310055732727),\n", + " ('Plato', 0.5317621231079102),\n", + " ('philosopher_Aristotle', 0.5256754755973816),\n", + " ('Plato_Socrates', 0.5240647792816162),\n", + " ('philosopher_Plato', 0.5164784789085388)]" ] }, - "execution_count": 9, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.most_similar('Plato')" + "model.most_similar('Socrates')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:14.606275Z", - "start_time": "2024-08-01T05:57:14.591809Z" + "end_time": "2024-08-04T03:26:53.787888Z", + "start_time": "2024-08-04T03:26:53.772787Z" }, "slideshow": { "slide_type": "subslide" @@ -586,7 +588,7 @@ "0.76640123" ] }, - "execution_count": 10, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -597,11 +599,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 33, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:30.360195Z", - "start_time": "2024-08-01T05:57:30.350626Z" + "end_time": "2024-08-04T03:27:07.391195Z", + "start_time": "2024-08-04T03:27:07.381976Z" }, "slideshow": { "slide_type": "fragment" @@ -614,7 +616,7 @@ "0.76640123" ] }, - "execution_count": 13, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -625,11 +627,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:33.982193Z", - "start_time": "2024-08-01T05:57:33.636875Z" + "end_time": "2024-08-04T03:28:59.716020Z", + "start_time": "2024-08-04T03:28:59.462173Z" }, "slideshow": { "slide_type": "subslide" @@ -639,23 +641,23 @@ { "data": { "text/plain": [ - "[('UK', 0.630479633808136),\n", - " ('Britain', 0.6012536287307739),\n", - " ('EURASIAN_NATURAL_RESOURCES_CORP.', 0.5524139404296875),\n", - " ('Europe', 0.5444059371948242),\n", - " ('United_Kingdom', 0.5375454425811768)]" + "[('went', 0.7435076236724854),\n", + " ('came', 0.5402584671974182),\n", + " ('ran', 0.5255370140075684),\n", + " ('gone', 0.5027263164520264),\n", + " ('stayed', 0.4797917604446411)]" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#model.most_similar(positive=['woman', 'king'], negative=['man'], topn=5)\n", - "model.most_similar(positive=['London', 'China'], negative=['Beijing'], topn=5)\n", + "#model.most_similar(positive=['London', 'China'], negative=['Beijing'], topn=5)\n", "#model.most_similar(positive=['big', 'worst'], negative=['bad'], topn=5)\n", - "#model.most_similar(positive=['go', 'did'], negative=['do'], topn=5)" + "model.most_similar(positive=['go', 'did'], negative=['do'], topn=5)" ] }, { @@ -683,11 +685,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:45.191566Z", - "start_time": "2024-08-01T05:57:44.397192Z" + "end_time": "2024-08-04T03:30:15.506542Z", + "start_time": "2024-08-04T03:30:15.501677Z" }, "slideshow": { "slide_type": "subslide" @@ -718,11 +720,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:50.010484Z", - "start_time": "2024-08-01T05:57:49.993121Z" + "end_time": "2024-08-04T03:30:20.615247Z", + "start_time": "2024-08-04T03:30:20.606779Z" }, "slideshow": { "slide_type": "subslide" @@ -737,7 +739,7 @@ " dtype=float32)" ] }, - "execution_count": 16, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -748,11 +750,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 41, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:42:04.210764Z", - "start_time": "2024-08-01T05:42:03.532817Z" + "end_time": "2024-08-04T03:30:33.411130Z", + "start_time": "2024-08-04T03:30:33.253872Z" }, "slideshow": { "slide_type": "subslide" @@ -798,11 +800,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 42, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:47:03.003399Z", - "start_time": "2024-08-01T05:47:02.998479Z" + "end_time": "2024-08-04T03:30:37.853213Z", + "start_time": "2024-08-04T03:30:37.843930Z" }, "slideshow": { "slide_type": "subslide" @@ -827,11 +829,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 43, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:56.562776Z", - "start_time": "2024-08-01T05:57:56.105204Z" + "end_time": "2024-08-04T03:30:40.985134Z", + "start_time": "2024-08-04T03:30:40.829064Z" }, "slideshow": { "slide_type": "subslide" @@ -846,11 +848,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 44, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:57:59.629646Z", - "start_time": "2024-08-01T05:57:59.621930Z" + "end_time": "2024-08-04T03:30:53.223559Z", + "start_time": "2024-08-04T03:30:53.217667Z" }, "slideshow": { "slide_type": "subslide" @@ -863,7 +865,7 @@ "(-0.014282565941581948, -0.10834102708111581, -0.060338924032504515)" ] }, - "execution_count": 18, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -874,11 +876,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 45, "metadata": { "ExecuteTime": { - "end_time": "2024-08-01T05:58:03.320671Z", - "start_time": "2024-08-01T05:58:03.293911Z" + "end_time": "2024-08-04T03:31:00.001106Z", + "start_time": "2024-08-04T03:30:59.983439Z" }, "slideshow": { "slide_type": "subslide" @@ -902,7 +904,9 @@ "end_time": "2024-08-01T05:58:10.594033Z", "start_time": "2024-08-01T05:58:09.702748Z" }, - "code_folding": [], + "code_folding": [ + 0 + ], "slideshow": { "slide_type": "subslide" } diff --git a/notebook/10-01-text-mining.ipynb b/notebook/10-01-text-mining.ipynb index 733e097..81db0f9 100644 --- a/notebook/10-01-text-mining.ipynb +++ b/notebook/10-01-text-mining.ipynb @@ -38,11 +38,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T06:30:37.559938Z", - "start_time": "2022-07-21T06:30:37.549675Z" + "end_time": "2024-08-05T01:34:08.797195Z", + "start_time": "2024-08-05T01:34:08.758948Z" }, "code_folding": [], "slideshow": { @@ -311,11 +311,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-06-12T06:41:54.271442Z", - "start_time": "2020-06-12T06:41:53.272468Z" + "end_time": "2024-08-05T01:56:25.911606Z", + "start_time": "2024-08-05T01:56:24.902161Z" }, "slideshow": { "slide_type": "subslide" @@ -443,11 +443,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-06-12T06:42:56.076008Z", - "start_time": "2020-06-12T06:42:55.544759Z" + "end_time": "2024-08-05T01:56:54.201227Z", + "start_time": "2024-08-05T01:56:54.193189Z" }, "slideshow": { "slide_type": "fragment" @@ -526,14 +526,14 @@ "2 1 2 1 1 1 2 1" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", - "pd.DataFrame(bag.toarray(), columns = count.get_feature_names())" + "pd.DataFrame(bag.toarray(), columns = count.get_feature_names_out())" ] }, { @@ -670,11 +670,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-06-12T06:47:37.164242Z", - "start_time": "2020-06-12T06:47:37.149763Z" + "end_time": "2024-08-05T01:59:11.987874Z", + "start_time": "2024-08-05T01:59:11.980925Z" }, "slideshow": { "slide_type": "subslide" @@ -945,11 +945,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:08:56.288874Z", - "start_time": "2022-07-21T07:08:56.271378Z" + "end_time": "2024-08-05T02:00:28.169031Z", + "start_time": "2024-08-05T02:00:28.154912Z" }, "slideshow": { "slide_type": "fragment" @@ -963,11 +963,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:08:57.384920Z", - "start_time": "2022-07-21T07:08:57.381179Z" + "end_time": "2024-08-05T02:00:43.108401Z", + "start_time": "2024-08-05T02:00:43.104433Z" }, "slideshow": { "slide_type": "fragment" @@ -980,7 +980,7 @@ "48" ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -2841,7 +2841,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true, @@ -3310,7 +3309,7 @@ "top": "0px", "width": "159.359px" }, - "toc_section_display": false, + "toc_section_display": "none", "toc_window_display": true }, "toc_position": { diff --git a/notebook/10-03-cnsenti-cntext.ipynb b/notebook/10-03-cntext.ipynb similarity index 80% rename from notebook/10-03-cnsenti-cntext.ipynb rename to notebook/10-03-cntext.ipynb index 5cd1c57..ca94350 100644 --- a/notebook/10-03-cnsenti-cntext.ipynb +++ b/notebook/10-03-cntext.ipynb @@ -8,21 +8,21 @@ } }, "source": [ - "# cnsenti\n", + "# cntext\n", "\n", - "中文情感分析库(Chinese Sentiment)可对文本进行情绪分析、正负情感分析。\n", + "cntext 是一个文本分析包，提供基于词嵌入模型的语义距离和语义投影。 此外，cntext还提供了传统的方法，如字数统计、可读性、文档相似度、情感分析等。\n", "\n", - "- github地址 https://github.com/thunderhit/cnsenti\n", - "- pypi地址 https://pypi.org/project/cnsenti/" + "- github地址 https://github.com/hiDaDeng/cntext/\n", + "- pypi地址 https://pypi.org/project/cntext/" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:20:34.962273Z", - "start_time": "2022-07-21T07:20:31.728487Z" + "end_time": "2024-08-04T04:40:52.208419Z", + "start_time": "2024-08-04T04:40:46.180045Z" }, "slideshow": { "slide_type": "subslide" @@ -34,70 +34,101 @@ "output_type": "stream", "text": [ "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting cnsenti\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/64/91/06bff77081acf17c99bbc59aff7b06a834664b6bbe5c25bc250ce1f53911/cnsenti-0.0.7-py3-none-any.whl (395 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m395.8/395.8 KB\u001b[0m \u001b[31m892.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cnsenti) (0.42.1)\n", - "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.8/site-packages (from cnsenti) (1.20.1)\n", - "Installing collected packages: cnsenti\n", - "Successfully installed cnsenti-0.0.7\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", + "Requirement already satisfied: cntext in /opt/anaconda3/lib/python3.8/site-packages (1.7.9)\n", + "Requirement already satisfied: nltk in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.6.1)\n", + "Requirement already satisfied: numpy==1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.20.0)\n", + "Requirement already satisfied: mittens in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.2)\n", + "Requirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.42.1)\n", + "Requirement already satisfied: pyecharts in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.9.1)\n", + "Collecting gensim==4.0.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/81/09/6929fd1e882943d1764f2aaf1e66ed32fc1cef987dab6ddbec0291e3ae4a/gensim-4.0.0-cp38-cp38-macosx_10_9_x86_64.whl (23.9 MB)\n", + "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.3.4)\n", + "Requirement already satisfied: scikit-learn==1.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.0)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (5.2.1)\n", + "Requirement already satisfied: scipy>=0.18.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (1.6.2)\n", + "Requirement already satisfied: joblib>=0.11 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (1.0.1)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (2.1.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (0.10.0)\n", + "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (8.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.8.1)\n", + "Requirement already satisfied: tqdm in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (4.59.0)\n", + "Requirement already satisfied: regex in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (2021.4.4)\n", + "Requirement already satisfied: click in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (7.1.2)\n", + "Requirement already satisfied: prettytable in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.3.0)\n", + "Requirement already satisfied: simplejson in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.17.6)\n", + "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (2.11.3)\n", + "Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->cntext) (1.15.0)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->pyecharts->cntext) (1.1.1)\n", + "Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.8/site-packages (from prettytable->pyecharts->cntext) (0.2.5)\n", + "Installing collected packages: gensim\n", + " Attempting uninstall: gensim\n", + " Found existing installation: gensim 3.8.3\n", + " Uninstalling gensim-3.8.3:\n", + " Successfully uninstalled gensim-3.8.3\n", + "Successfully installed gensim-4.0.0\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ - " pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cnsenti" + " pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cntext" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:21:32.910915Z", - "start_time": "2022-07-21T07:21:31.361943Z" + "end_time": "2024-08-04T04:42:04.753866Z", + "start_time": "2024-08-04T04:41:56.317408Z" }, "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building prefix dict from the default dictionary ...\n", - "Dumping model to file cache /var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/jieba.cache\n", - "Loading model cost 0.824 seconds.\n", - "Prefix dict has been built successfully.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "{'words': 22, 'sentences': 2, 'pos': 4, 'neg': 0}\n" + "Help on package cntext:\n", + "\n", + "NAME\n", + " cntext\n", + "\n", + "PACKAGE CONTENTS\n", + " dictionary\n", + " mind\n", + " similarity\n", + " stats\n", + "\n", + "VERSION\n", + " 1.7.9\n", + "\n", + "FILE\n", + " /opt/anaconda3/lib/python3.8/site-packages/cntext/__init__.py\n", + "\n", + "\n" ] } ], "source": [ - "from cnsenti import Sentiment\n", + "import cntext as ct\n", "\n", - "senti = Sentiment()\n", - "test_text= '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result = senti.sentiment_count(test_text)\n", - "print(result)" + "help(ct)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:21:46.164954Z", - "start_time": "2022-07-21T07:21:46.151660Z" + "end_time": "2024-08-04T04:42:32.087353Z", + "start_time": "2024-08-04T04:42:31.270546Z" }, "slideshow": { "slide_type": "subslide" @@ -105,20 +136,39 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "{'words': 22, 'sentences': 2, '好': 0, '乐': 4, '哀': 0, '怒': 0, '惧': 0, '恶': 0, '惊': 0}\n" + "Building prefix dict from the default dictionary ...\n", + "Dumping model to file cache /var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/jieba.cache\n", + "Loading model cost 0.802 seconds.\n", + "Prefix dict has been built successfully.\n" ] + }, + { + "data": { + "text/plain": [ + "Counter({'看待': 1,\n", + " '网文': 1,\n", + " '作者': 1,\n", + " '黑客': 1,\n", + " '大佬': 1,\n", + " '盗号': 1,\n", + " '改文因': 1,\n", + " '万分': 1,\n", + " '惭愧': 1,\n", + " '停': 1})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from cnsenti import Emotion\n", + "text = '如何看待一网文作者被黑客大佬盗号改文，因万分惭愧而停更。'\n", "\n", - "emotion = Emotion()\n", - "test_text = '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result = emotion.emotion_count(test_text)\n", - "print(result)" + "ct.term_freq(text, lang='chinese')" ] }, { @@ -129,20 +179,25 @@ } }, "source": [ - "**sentiment_calculate(text)** belongs to the Sentiment class, which can calculate the emotional information of the chinese text more accurately. Compared with sentiment_count only counts the number of positive and negative sentiment words in the text, sentiment_calculate also considers\n", + "## readability\n", + "文本可读性，指标越大，文章复杂度越高，可读性越差。\n", "\n", - "- Is there a modifier of strength adverbs before emotional words\n", - "- Is there an emotional semantic reversal effect of negative words before emotional words?\n", - "for examples:" + "> readability(text, lang='chinese')\n", + "\n", + "徐巍,姚振晔,陈冬华.中文年报可读性：衡量与检验[J].会计研究,2021(03):28-44.\n", + "\n", + "- readability1 ---每个分句中的平均字数\n", + "- readability2 ---每个句子中副词和连词所占的比例\n", + "- readability3 ---参考Fog Index， readability3=(readability1+readability2)×0.5" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T07:23:53.508215Z", - "start_time": "2022-07-21T07:23:53.488286Z" + "end_time": "2024-08-04T04:43:01.222065Z", + "start_time": "2024-08-04T04:43:01.215733Z" }, "slideshow": { "slide_type": "subslide" @@ -150,50 +205,77 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "sentiment_count {'words': 22, 'sentences': 2, 'pos': 4, 'neg': 0}\n", - "sentiment_calculate {'sentences': 2, 'words': 22, 'pos': 27.0, 'neg': 0.0}\n" - ] + "data": { + "text/plain": [ + "{'readability1': 28.0,\n", + " 'readability2': 0.15789473684210525,\n", + " 'readability3': 14.078947368421053}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from cnsenti import Sentiment\n", + "text1 = '如何看待一网文作者被黑客大佬盗号改文，因万分惭愧而停更。'\n", "\n", - "senti = Sentiment()\n", - "test_text = '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'\n", - "result1 = senti.sentiment_count(test_text)\n", - "result2 = senti.sentiment_calculate(test_text)\n", - "print('sentiment_count',result1)\n", - "print('sentiment_calculate',result2)" + "ct.readability(text1, lang='chinese')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": { + "ExecuteTime": { + "end_time": "2024-08-04T04:46:15.124091Z", + "start_time": "2024-08-04T04:46:15.119890Z" + }, "slideshow": { - "slide_type": "slide" + "slide_type": "subslide" } }, + "outputs": [ + { + "data": { + "text/plain": [ + "['DUTIR.pkl',\n", + " 'HOWNET.pkl',\n", + " 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',\n", + " 'SentiWS.pkl',\n", + " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", + " 'ANEW.pkl',\n", + " 'LSD2015.pkl',\n", + " 'NRC.pkl',\n", + " 'geninqposneg.pkl',\n", + " 'HuLiu.pkl',\n", + " 'Loughran_McDonald_Financial_Sentiment.pkl',\n", + " 'AFINN.pkl',\n", + " 'ADV_CONJ.pkl',\n", + " 'STOPWORDS.pkl',\n", + " 'Concreteness.pkl',\n", + " 'ChineseEmoBank.pkl']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## cntext\n", - "\n", - "https://github.com/hidadeng/cntext\n", + "import cntext as ct\n", "\n", - "cntext is a text analysis package that provides semantic distance and semantic projection based on word embedding models. Besides,cntext also provides the traditional methods, such as word count , readability, document similarity, sentiment analysis, etc." + "# 获取cntext内置词典列表(pkl格式)\n", + "ct.dict_pkl_list()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:43:09.507980Z", - "start_time": "2022-07-21T11:43:06.411977Z" - }, - "slideshow": { - "slide_type": "subslide" + "end_time": "2024-08-04T04:48:41.259954Z", + "start_time": "2024-08-04T04:48:41.249663Z" } }, "outputs": [ @@ -201,93 +283,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Requirement already satisfied: cntext in /opt/anaconda3/lib/python3.8/site-packages (1.7.9)\n", - "Requirement already satisfied: numpy==1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.20.0)\n", - "Requirement already satisfied: jieba in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.42.1)\n", - "Requirement already satisfied: nltk in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.6.1)\n", - "Requirement already satisfied: gensim==4.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (4.0.0)\n", - "Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (3.3.4)\n", - "Requirement already satisfied: scikit-learn==1.0 in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.0)\n", - "Requirement already satisfied: mittens in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (0.2)\n", - "Requirement already satisfied: pyecharts in /opt/anaconda3/lib/python3.8/site-packages (from cntext) (1.9.1)\n", - "Requirement already satisfied: smart-open>=1.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (5.2.1)\n", - "Requirement already satisfied: scipy>=0.18.1 in /opt/anaconda3/lib/python3.8/site-packages (from gensim==4.0.0->cntext) (1.6.2)\n", - "Requirement already satisfied: joblib>=0.11 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (1.0.1)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn==1.0->cntext) (2.1.0)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (0.10.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (1.3.1)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.8.1)\n", - "Requirement already satisfied: pillow>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (8.2.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/anaconda3/lib/python3.8/site-packages (from matplotlib->cntext) (2.4.7)\n", - "Requirement already satisfied: regex in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (2021.4.4)\n", - "Requirement already satisfied: tqdm in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (4.59.0)\n", - "Requirement already satisfied: click in /opt/anaconda3/lib/python3.8/site-packages (from nltk->cntext) (7.1.2)\n", - "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (2.11.3)\n", - "Requirement already satisfied: prettytable in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.3.0)\n", - "Requirement already satisfied: simplejson in /opt/anaconda3/lib/python3.8/site-packages (from pyecharts->cntext) (3.17.6)\n", - "Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->cntext) (1.15.0)\n", - "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->pyecharts->cntext) (1.1.1)\n", - "Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.8/site-packages (from prettytable->pyecharts->cntext) (0.2.5)\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" + "1.7.9\n", + "dict_keys(['DUTIR', 'Referer', 'Desc'])\n" ] } ], "source": [ - "!pip3 install --upgrade cntext -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "import cntext as ct\n", + "\n", + "print(ct.__version__)\n", + "# 导入pkl词典文件,\n", + "dutir = ct.load_pkl_dict('DUTIR.pkl')\n", + "print(dutir.keys())" ] }, { - "cell_type": "code", - "execution_count": 21, + "cell_type": "markdown", "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:51:23.838864Z", - "start_time": "2022-07-21T11:50:32.711340Z" - }, "slideshow": { - "slide_type": "subslide" + "slide_type": "slide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.8/site-packages (1.2.4)\n", - "Collecting pandas\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/85/8afe540bd0299c4d58f0a5b88acc49a8021804abe05a00d2cbc2fccde873/pandas-1.4.3-cp38-cp38-macosx_10_9_x86_64.whl (11.4 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.4/11.4 MB\u001b[0m \u001b[31m276.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:02\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (2021.1)\n", - "Requirement already satisfied: numpy>=1.18.5 in /opt/anaconda3/lib/python3.8/site-packages (from pandas) (1.20.0)\n", - "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n", - "Installing collected packages: pandas\n", - " Attempting uninstall: pandas\n", - " Found existing installation: pandas 1.2.4\n", - " Uninstalling pandas-1.2.4:\n", - " Successfully uninstalled pandas-1.2.4\n", - "Successfully installed pandas-1.4.3\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - } - ], "source": [ - "!pip3 install --upgrade pandas -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "## sentiment\n", + "\n", + "> sentiment(text, diction, lang='chinese') \n", + "\n", + "使用diy词典进行情感分析，计算各个情绪词出现次数; 未考虑强度副词、否定词对情感的复杂影响，\n", + "\n", + "- text: 待分析中文文本\n", + "- diction: 情感词字典；\n", + "- lang: 语言类型，\"chinese\"或\"english\"，默认\"chinese\"" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:52:32.466752Z", - "start_time": "2022-07-21T11:52:25.722860Z" + "end_time": "2024-08-04T04:49:32.782452Z", + "start_time": "2024-08-04T04:49:32.287078Z" }, "slideshow": { "slide_type": "subslide" @@ -295,39 +330,42 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting python-Levenshtein\n", - " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2a/dc/97f2b63ef0fa1fd78dcb7195aca577804f6b2b51e712516cc0e902a9a201/python-Levenshtein-0.12.2.tar.gz (50 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: setuptools in /opt/anaconda3/lib/python3.8/site-packages (from python-Levenshtein) (52.0.0.post20210125)\n", - "Building wheels for collected packages: python-Levenshtein\n", - " Building wheel for python-Levenshtein (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp38-cp38-macosx_10_9_x86_64.whl size=80491 sha256=6a0876479d2777b80eec9fb1b73b1f5feb1079854b8b4c41d60dba3798030bbd\n", - " Stored in directory: /Users/chengjun/Library/Caches/pip/wheels/28/a5/92/bf15714fe87b46cdfefbba580ca70f86ee6392d27a1b501d4b\n", - "Successfully built python-Levenshtein\n", - "Installing collected packages: python-Levenshtein\n", - "Successfully installed python-Levenshtein-0.12.2\n", - "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", - "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] + "data": { + "text/plain": [ + "{'乐_num': 2,\n", + " '好_num': 0,\n", + " '怒_num': 0,\n", + " '哀_num': 0,\n", + " '惧_num': 0,\n", + " '恶_num': 0,\n", + " '惊_num': 0,\n", + " 'stopword_num': 8,\n", + " 'word_num': 14,\n", + " 'sentence_num': 1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "!pip3 install --upgrade python-Levenshtein -i https://pypi.tuna.tsinghua.edu.cn/simple\n" + "import cntext as ct\n", + "\n", + "text = '我今天得奖了，很高兴，我要将快乐分享大家。'\n", + "\n", + "ct.sentiment(text=text,\n", + " diction=ct.load_pkl_dict('DUTIR.pkl')['DUTIR'],\n", + " lang='chinese')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:52:50.879009Z", - "start_time": "2022-07-21T11:52:50.874088Z" + "end_time": "2024-08-04T04:50:25.934568Z", + "start_time": "2024-08-04T04:50:25.909495Z" }, "slideshow": { "slide_type": "subslide" @@ -335,43 +373,46 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on package cntext:\n", - "\n", - "NAME\n", - " cntext\n", - "\n", - "PACKAGE CONTENTS\n", - " dictionary\n", - " mind\n", - " similarity\n", - " stats\n", - "\n", - "VERSION\n", - " 1.7.9\n", - "\n", - "FILE\n", - " /opt/anaconda3/lib/python3.8/site-packages/cntext/__init__.py\n", - "\n", - "\n" - ] + "data": { + "text/plain": [ + "{'Referer': 'Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911',\n", + " 'Desc': '语言具体性词典， 具体性计算应用案例可参考Packard, Grant, and Jonah Berger. \"How concrete language shapes customer satisfaction.\" *Journal of Consumer Research* 47, no. 5 (2021): 787-806.',\n", + " 'Concreteness': word valence\n", + " 0 roadsweeper 4.85\n", + " 1 traindriver 4.54\n", + " 2 tush 4.45\n", + " 3 hairdress 3.93\n", + " 4 pharmaceutics 3.77\n", + " ... ... ...\n", + " 39949 unenvied 1.21\n", + " 39950 agnostically 1.20\n", + " 39951 conceptualistic 1.18\n", + " 39952 conventionalism 1.18\n", + " 39953 essentialness 1.04\n", + " \n", + " [39954 rows x 2 columns]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "import cntext as ct\n", "\n", - "help(ct)" + "# load the concreteness.pkl dictionary file\n", + "concreteness_df = ct.load_pkl_dict('concreteness.pkl')\n", + "concreteness_df" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:56:12.150628Z", - "start_time": "2022-07-21T11:56:12.146199Z" + "end_time": "2024-08-04T04:53:34.420645Z", + "start_time": "2024-08-04T04:53:34.404483Z" }, "slideshow": { "slide_type": "subslide" @@ -381,40 +422,30 @@ { "data": { "text/plain": [ - "['DUTIR.pkl',\n", - " 'HOWNET.pkl',\n", - " 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'SentiWS.pkl',\n", - " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", - " 'ANEW.pkl',\n", - " 'LSD2015.pkl',\n", - " 'NRC.pkl',\n", - " 'geninqposneg.pkl',\n", - " 'HuLiu.pkl',\n", - " 'Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'AFINN.pkl',\n", - " 'ADV_CONJ.pkl',\n", - " 'STOPWORDS.pkl',\n", - " 'Concreteness.pkl',\n", - " 'ChineseEmoBank.pkl']" + "{'valence': 9.28, 'word_num': 5}" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ct.dict_pkl_list()" + "reply = \"I'll go look for that\"\n", + "\n", + "score=ct.sentiment_by_valence(text=reply, \n", + " diction=concreteness_df, \n", + " lang='english')\n", + "score" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:55:38.007632Z", - "start_time": "2022-07-21T11:55:37.997085Z" + "end_time": "2024-08-04T04:52:33.083998Z", + "start_time": "2024-08-04T04:52:33.076815Z" }, "slideshow": { "slide_type": "subslide" @@ -439,7 +470,7 @@ " 'sentence_num': 1}" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -454,54 +485,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T11:57:03.874083Z", - "start_time": "2022-07-21T11:57:03.861742Z" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Referer': 'Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911',\n", - " 'Desc': '语言具体性词典， 具体性计算应用案例可参考Packard, Grant, and Jonah Berger. \"How concrete language shapes customer satisfaction.\" *Journal of Consumer Research* 47, no. 5 (2021): 787-806.',\n", - " 'Concreteness': word valence\n", - " 0 roadsweeper 4.85\n", - " 1 traindriver 4.54\n", - " 2 tush 4.45\n", - " 3 hairdress 3.93\n", - " 4 pharmaceutics 3.77\n", - " ... ... ...\n", - " 39949 unenvied 1.21\n", - " 39950 agnostically 1.20\n", - " 39951 conceptualistic 1.18\n", - " 39952 conventionalism 1.18\n", - " 39953 essentialness 1.04\n", - " \n", - " [39954 rows x 2 columns]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ct.load_pkl_dict('concreteness.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:57:29.203436Z", - "start_time": "2022-07-21T11:57:29.190140Z" + "end_time": "2024-08-04T04:52:50.963134Z", + "start_time": "2024-08-04T04:52:50.948664Z" }, "slideshow": { "slide_type": "subslide" @@ -572,7 +560,7 @@ "4 pharmaceutics 3.77" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -585,44 +573,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-21T11:57:57.215210Z", - "start_time": "2022-07-21T11:57:57.197092Z" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'valence': 9.28, 'word_num': 5}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reply = \"I'll go look for that\"\n", - "\n", - "score=ct.sentiment_by_valence(text=reply, \n", - " diction=concreteness_df, \n", - " lang='english')\n", - "score" - ] - }, - { - "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T12:02:53.846511Z", - "start_time": "2022-07-21T12:02:53.746320Z" + "end_time": "2024-08-04T04:53:50.123421Z", + "start_time": "2024-08-04T04:53:50.021025Z" }, "slideshow": { "slide_type": "subslide" @@ -665,11 +620,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2022-07-21T12:11:05.599905Z", - "start_time": "2022-07-21T12:10:03.155453Z" + "end_time": "2024-08-04T04:56:34.798503Z", + "start_time": "2024-08-04T04:55:33.788633Z" }, "slideshow": { "slide_type": "subslide" @@ -684,7 +639,7 @@ "Step 2/4:...Collect co-occurrency information ...\n", "Step 3/4:...Calculate mutual information ...\n", "Step 4/4:...Save candidate words ...\n", - "Finish! used 62.41 s\n" + "Finish! used 60.98 s\n" ] } ], diff --git a/notebook/10-04-topic-models-update.ipynb b/notebook/10-04-topic-models-update.ipynb index e60fa99..e9718a0 100644 --- a/notebook/10-04-topic-models-update.ipynb +++ b/notebook/10-04-topic-models-update.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -33,7 +32,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -98,7 +96,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -158,7 +155,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -418,7 +414,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -430,7 +425,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -448,7 +442,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -2498,7 +2491,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -3707,7 +3699,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/notebook/10-06-llm-ollama.ipynb b/notebook/10-06-llm-ollama.ipynb index 9906667..2b3ae99 100644 --- a/notebook/10-06-llm-ollama.ipynb +++ b/notebook/10-06-llm-ollama.ipynb @@ -56,8 +56,112 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "8878a75c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-05T02:49:41.479340Z", + "start_time": "2024-08-05T02:49:34.757067Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting ollama\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2f/25/c3442864bd77621809a208a483b0857f8d6444b7a67906b58b9dcddd1574/ollama-0.3.1-py3-none-any.whl (10 kB)\n", + "Collecting httpx<0.28.0,>=0.27.0\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/41/7b/ddacf6dcebb42466abd03f368782142baa82e08fc0c1f8eaa05b4bae87d5/httpx-0.27.0-py3-none-any.whl (75 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 KB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hCollecting httpcore==1.*\n", + " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/78/d4/e5d7e4f2174f8a4d63c8897d79eb8fe2503f7ecc03282fee1fa2719c2704/httpcore-1.0.5-py3-none-any.whl (77 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 KB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: idna in /opt/anaconda3/lib/python3.8/site-packages (from httpx<0.28.0,>=0.27.0->ollama) (2.10)\n", + "Requirement already satisfied: anyio in /opt/anaconda3/lib/python3.8/site-packages (from httpx<0.28.0,>=0.27.0->ollama) (3.5.0)\n", + "Requirement already satisfied: sniffio in /opt/anaconda3/lib/python3.8/site-packages (from httpx<0.28.0,>=0.27.0->ollama) (1.3.1)\n", + "Requirement already satisfied: certifi in /opt/anaconda3/lib/python3.8/site-packages (from httpx<0.28.0,>=0.27.0->ollama) (2024.6.2)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in /opt/anaconda3/lib/python3.8/site-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->ollama) (0.13.0)\n", + "Installing collected packages: httpcore, httpx, ollama\n", + "Successfully installed httpcore-1.0.5 httpx-0.27.0 ollama-0.3.1\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", + "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# 使用清华大学镜像快速安装Python包\n", + "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U ollama" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7db57ee4", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-05T02:51:22.841548Z", + "start_time": "2024-08-05T02:51:22.215507Z" + } + }, + "outputs": [], + "source": [ + "import ollama\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93793942", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-05T02:55:21.930894Z", + "start_time": "2024-08-05T02:51:44.171515Z" + } + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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follow_redirects)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mauth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_request_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m response = self._send_handling_auth(\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m 940\u001b[0m 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"\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpx/_transports/default.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 231\u001b[0m )\n\u001b[1;32m 232\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mmap_httpcore_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m 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Note that in this case we still have to manage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;31m# Send the request on the assigned connection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m response = connection.handle_request(\n\u001b[0m\u001b[1;32m 197\u001b[0m \u001b[0mpool_request\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m )\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/connection.py\u001b[0m in 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"\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"response_closed\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_response_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;31m# Sending the request...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[0mtrailing_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m ) = self._receive_response_headers(**kwargs)\n\u001b[0m\u001b[1;32m 114\u001b[0m trace.return_value = (\n\u001b[1;32m 115\u001b[0m \u001b[0mhttp_version\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_response_headers\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_receive_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 187\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNEED_DATA\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m data = self._network_stream.read(\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREAD_NUM_BYTES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m )\n", + "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/httpcore/_backends/sync.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mmap_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "ollama.chat(model='phi3:mini', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "28b059ef", "metadata": { "ExecuteTime": { "end_time": "2024-08-01T07:17:16.464779Z", diff --git a/notebook/10-07-bert-topic.ipynb b/notebook/10-07-bert-topic.ipynb deleted file mode 100644 index 5080b5b..0000000 --- a/notebook/10-07-bert-topic.ipynb +++ /dev/null @@ -1,39 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "bebe2120", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "toc": { - "toc_cell": false, - "toc_number_sections": true, - "toc_threshold": 6, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebook/BERTopic.ipynb b/notebook/BERTopic.ipynb new file mode 100644 index 0000000..6fb35a4 --- /dev/null +++ b/notebook/BERTopic.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "40d39dec", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# BERTopic\n", + "\n", + "BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.\n", + "\n", + "https://maartengr.github.io/BERTopic/" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "55c78235", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:59:18.174535Z", + "start_time": "2024-08-03T07:59:17.777485Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "channels:\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + " - https://mirrors.ustc.edu.cn/anaconda/pkgs/main/\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/\n", + " - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/\n", + " - https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/\n", + " - https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/\n", + " - defaults\n", + "\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "conda config --show channels" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "96c95d53", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T08:01:32.972110Z", + "start_time": "2024-08-03T07:59:54.222619Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting package metadata (current_repodata.json): done\n", + "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n", + "Collecting package metadata (repodata.json): | ^C\n", + "- " + ] + } + ], + "source": [ + "# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + "!conda install hdbscan" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b72c2063", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:51:19.265753Z", + "start_time": "2024-08-03T07:50:38.295860Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting hdbscan\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9d/47/a6493a4e17cc45220a0b5de012641b81f57272961570a4ab99fcdf727c38/hdbscan-0.8.37.tar.gz (5.2 MB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: cython<3,>=0.27 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (0.29.23)\n", + "Requirement already satisfied: joblib>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.0.1)\n", + "Requirement already satisfied: scipy>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.6.2)\n", + "Requirement already satisfied: numpy<2,>=1.20 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.20.0)\n", + "Requirement already satisfied: scikit-learn>=0.20 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan) (1.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.20->hdbscan) (2.1.0)\n", + "Building wheels for collected packages: hdbscan\n", + " Building wheel for hdbscan (pyproject.toml) ... \u001b[?25lerror\n", + " \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", + " \n", + " \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for hdbscan \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n", + " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", + " \u001b[31m╰─>\u001b[0m \u001b[31m[48 lines of output]\u001b[0m\n", + " \u001b[31m \u001b[0m running bdist_wheel\n", + " \u001b[31m \u001b[0m running build\n", + " \u001b[31m \u001b[0m running build_py\n", + " \u001b[31m \u001b[0m creating build\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/validity.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/prediction.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/plots.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/hdbscan_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/robust_single_linkage_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_prediction_utils.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_rsl.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_hdbscan.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m running build_ext\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_tree.pyx to hdbscan/_hdbscan_tree.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_linkage.pyx to hdbscan/_hdbscan_linkage.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_boruvka.pyx to hdbscan/_hdbscan_boruvka.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_reachability.pyx to hdbscan/_hdbscan_reachability.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_prediction_utils.pyx to hdbscan/_prediction_utils.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/dist_metrics.pyx to hdbscan/dist_metrics.c\n", + " \u001b[31m \u001b[0m building 'hdbscan._hdbscan_tree' extension\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include/python3.8 -I/private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/numpy/core/include -c hdbscan/_hdbscan_tree.c -o build/temp.macosx-10.9-x86_64-cpython-38/hdbscan/_hdbscan_tree.o\n", + " \u001b[31m \u001b[0m xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'test_suite'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'tests_require'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_tree.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_linkage.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_boruvka.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_hdbscan_reachability.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/_prediction_utils.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-egmmbja_/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-9bt_cxv3/hdbscan_30f23a8a186c4fc89f6be4db193785c0/hdbscan/dist_metrics.pxd\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m error: command '/usr/bin/gcc' failed with exit code 1\n", + " \u001b[31m \u001b[0m \u001b[31m[end of output]\u001b[0m\n", + " \n", + " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", + "\u001b[31m ERROR: Failed building wheel for hdbscan\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[?25hFailed to build hdbscan\n", + "\u001b[31mERROR: Could not build wheels for hdbscan, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", + "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/\n", + "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U hdbscan" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "468ac5ed", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-03T07:34:09.105736Z", + "start_time": "2024-08-03T07:33:28.292626Z" + }, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting bertopic\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/34/38/a3b97cfc8346683d1498ffe7dc2c58265d1dea980d89c769b6b74e01e35c/bertopic-0.16.3-py3-none-any.whl (143 kB)\n", + "Collecting hdbscan>=0.8.29\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9d/47/a6493a4e17cc45220a0b5de012641b81f57272961570a4ab99fcdf727c38/hdbscan-0.8.37.tar.gz (5.2 MB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hCollecting plotly>=4.7.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b8/f0/bcf716a8e070370d6598c92fcd328bd9ef8a9bda2c5562da5a835c66700b/plotly-5.23.0-py3-none-any.whl (17.3 MB)\n", + "Requirement already satisfied: pandas>=1.1.5 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.4.3)\n", + "Requirement already satisfied: sentence-transformers>=0.4.1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (3.0.1)\n", + "Collecting umap-learn>=0.5.0\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d1/1b/46802a050b1c55d10c4f59fc6afd2b45ac9b4f62b2e12092d3f599286f14/umap_learn-0.5.6-py3-none-any.whl (85 kB)\n", + "Requirement already satisfied: tqdm>=4.41.1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (4.59.0)\n", + "Requirement already satisfied: numpy>=1.20.0 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.20.0)\n", + "Requirement already satisfied: scikit-learn>=0.22.2.post1 in /opt/anaconda3/lib/python3.8/site-packages (from bertopic) (1.0)\n", + "Requirement already satisfied: scipy>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (1.6.2)\n", + "Requirement already satisfied: joblib>=1.0 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (1.0.1)\n", + "Requirement already satisfied: cython<3,>=0.27 in /opt/anaconda3/lib/python3.8/site-packages (from hdbscan>=0.8.29->bertopic) (0.29.23)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas>=1.1.5->bertopic) (2021.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.8/site-packages (from pandas>=1.1.5->bertopic) (2.8.1)\n", + "Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.8/site-packages (from plotly>=4.7.0->bertopic) (24.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /opt/anaconda3/lib/python3.8/site-packages (from plotly>=4.7.0->bertopic) (8.5.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.22.2.post1->bertopic) (2.1.0)\n", + "Requirement already satisfied: torch>=1.11.0 in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (2.2.2)\n", + "Requirement already satisfied: Pillow in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (8.2.0)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (4.43.3)\n", + "Requirement already satisfied: huggingface-hub>=0.15.1 in /opt/anaconda3/lib/python3.8/site-packages (from sentence-transformers>=0.4.1->bertopic) (0.24.5)\n", + "Collecting pynndescent>=0.5\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/d2/53/d23a97e0a2c690d40b165d1062e2c4ccc796be458a1ce59f6ba030434663/pynndescent-0.5.13-py3-none-any.whl (56 kB)\n", + "Requirement already satisfied: numba>=0.51.2 in /opt/anaconda3/lib/python3.8/site-packages (from umap-learn>=0.5.0->bertopic) (0.53.1)\n", + "Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (3.0.12)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2024.6.1)\n", + "Requirement already satisfied: requests in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2.25.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (5.4.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/anaconda3/lib/python3.8/site-packages (from huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (4.12.2)\n", + "Requirement already satisfied: setuptools in /opt/anaconda3/lib/python3.8/site-packages (from numba>=0.51.2->umap-learn>=0.5.0->bertopic) (52.0.0.post20210125)\n", + "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /opt/anaconda3/lib/python3.8/site-packages (from numba>=0.51.2->umap-learn>=0.5.0->bertopic) (0.36.0)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas>=1.1.5->bertopic) (1.15.0)\n", + "Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (2.5)\n", + "Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (2.11.3)\n", + "Requirement already satisfied: sympy in /opt/anaconda3/lib/python3.8/site-packages (from torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.8)\n", + "Requirement already satisfied: regex!=2019.12.17 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (2021.4.4)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (0.4.3)\n", + "Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers>=0.4.1->bertopic) (0.19.1)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/anaconda3/lib/python3.8/site-packages (from jinja2->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.1.1)\n", + "Requirement already satisfied: decorator>=4.3.0 in /opt/anaconda3/lib/python3.8/site-packages (from networkx->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (5.0.9)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (1.26.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2024.6.2)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (4.0.0)\n", + "Requirement already satisfied: idna<3,>=2.5 in /opt/anaconda3/lib/python3.8/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers>=0.4.1->bertopic) (2.10)\n", + "Requirement already satisfied: mpmath>=0.19 in /opt/anaconda3/lib/python3.8/site-packages (from sympy->torch>=1.11.0->sentence-transformers>=0.4.1->bertopic) (1.2.1)\n", + "Building wheels for collected packages: hdbscan\n", + " Building wheel for hdbscan (pyproject.toml) ... \u001b[?25lerror\n", + " \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", + " \n", + " \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for hdbscan \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n", + " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", + " \u001b[31m╰─>\u001b[0m \u001b[31m[48 lines of output]\u001b[0m\n", + " \u001b[31m \u001b[0m running bdist_wheel\n", + " \u001b[31m \u001b[0m running build\n", + " \u001b[31m \u001b[0m running build_py\n", + " \u001b[31m \u001b[0m creating build\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/validity.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/prediction.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/plots.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/hdbscan_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m copying hdbscan/robust_single_linkage_.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m creating build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_flat.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_prediction_utils.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/__init__.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_rsl.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m copying hdbscan/tests/test_hdbscan.py -> build/lib.macosx-10.9-x86_64-cpython-38/hdbscan/tests\n", + " \u001b[31m \u001b[0m running build_ext\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_tree.pyx to hdbscan/_hdbscan_tree.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_linkage.pyx to hdbscan/_hdbscan_linkage.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_boruvka.pyx to hdbscan/_hdbscan_boruvka.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_hdbscan_reachability.pyx to hdbscan/_hdbscan_reachability.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/_prediction_utils.pyx to hdbscan/_prediction_utils.c\n", + " \u001b[31m \u001b[0m cythoning hdbscan/dist_metrics.pyx to hdbscan/dist_metrics.c\n", + " \u001b[31m \u001b[0m building 'hdbscan._hdbscan_tree' extension\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38\n", + " \u001b[31m \u001b[0m creating build/temp.macosx-10.9-x86_64-cpython-38/hdbscan\n", + " \u001b[31m \u001b[0m gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include -arch x86_64 -I/opt/anaconda3/include/python3.8 -I/private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/numpy/core/include -c hdbscan/_hdbscan_tree.c -o build/temp.macosx-10.9-x86_64-cpython-38/hdbscan/_hdbscan_tree.o\n", + " \u001b[31m \u001b[0m xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'test_suite'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/setuptools/_distutils/dist.py:268: UserWarning: Unknown distribution option: 'tests_require'\n", + " \u001b[31m \u001b[0m warnings.warn(msg)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_tree.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_linkage.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_boruvka.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_hdbscan_reachability.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/_prediction_utils.pyx\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-build-env-cc69x99s/overlay/lib/python3.8/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /private/var/folders/l6/ntr5b4610hx38gy0_2xp7ngh0000gn/T/pip-install-h6jbk28q/hdbscan_28538faf0b4640dc9762d2c27f88113c/hdbscan/dist_metrics.pxd\n", + " \u001b[31m \u001b[0m tree = Parsing.p_module(s, pxd, full_module_name)\n", + " \u001b[31m \u001b[0m error: command '/usr/bin/gcc' failed with exit code 1\n", + " \u001b[31m \u001b[0m \u001b[31m[end of output]\u001b[0m\n", + " \n", + " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", + "\u001b[31m ERROR: Failed building wheel for hdbscan\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[?25hFailed to build hdbscan\n", + "\u001b[31mERROR: Could not build wheels for hdbscan, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n", + "\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.2 is available.\n", + "You should consider upgrading via the '/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# 使用清华大学镜像快速安装Python包 \n", + "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -U bertopic\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c51277b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "幻灯片", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "toc": { + "toc_cell": false, + "toc_number_sections": true, + "toc_threshold": 6, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebook/output/sopmi_candi_words/neg.txt b/notebook/output/sopmi_candi_words/neg.txt index 8e78491..22db50c 100644 --- a/notebook/output/sopmi_candi_words/neg.txt +++ b/notebook/output/sopmi_candi_words/neg.txt @@ -1,777 +1,782 @@ -心灵,33.17993872989303,neg,2,n -期间,31.77900620939178,neg,2,f -西溪,30.87839808390589,neg,2,ns -人事,29.594976229171877,neg,2,n -复杂,29.47870186147108,neg,2,a -直到,27.86014637934966,neg,2,v -宰客,27.27304813428452,neg,2,nr -保险,26.433136238404746,neg,2,n -迎来,25.83859896903048,neg,2,v -至少,25.105021416064616,neg,2,d -融资,25.09148586460598,neg,2,vn -或,24.48343281812743,neg,1,c -列,22.20695894382675,neg,1,v -存在,22.041049266517774,neg,2,v -和夫,21.95735945136433,neg,2,nz -价格调整,21.95735945136433,neg,4,l -落么,21.819855927614395,neg,2,v -振华,21.767036974958184,neg,2,nz -落落,21.613405050146966,neg,2,v -墨尔本,21.497927832727033,neg,3,ns -不了,21.409572942232003,neg,2,v -胸膛,21.37239695064317,neg,2,n -分枝,21.305282754784635,neg,2,n -体现,20.95696667547938,neg,2,v -练习场,20.497927832727033,neg,3,n -逛一逛,20.497927832727033,neg,3,v -提拔,20.17599973783967,neg,2,v -倒地,20.17599973783967,neg,2,n -人生,20.11139892572354,neg,2,n -低潮期,20.082890333448187,neg,3,n -闭店,20.082890333448187,neg,2,v -拿捏,20.082890333448187,neg,2,v -重用,19.983354659897277,neg,2,v -固执,19.912965332005875,neg,2,a -神佛,19.912965332005875,neg,2,n -经科,19.86049791211174,neg,2,n -Google,19.819855927614398,neg,6,eng -催熟,19.819855927614395,neg,2,v -悉尼,19.819855927614395,neg,2,ns -球场,19.72032025406348,neg,2,n -事项,19.702585203258472,neg,2,n -通过率,19.64993092617208,neg,3,n -总任务,19.63543135647697,neg,3,n -酷热,19.497927832727033,neg,2,a -离谱,19.497927832727033,neg,2,n -费率,19.497927832727033,neg,2,n -松坂,19.497927832727033,neg,2,ns -起起伏伏,19.497927832727033,neg,4,v -一厢情愿,19.497927832727033,neg,4,i -导航台,19.49792783272703,neg,3,n -嬉笑,19.328002831284717,neg,2,v -中潜层,19.30528275478464,neg,3,n -接待工作,19.234893426893237,neg,4,l -人浮于事,19.17599973783967,neg,4,l -缴存,19.175999737839668,neg,2,v -独栋,19.150004529306724,neg,2,nr -随手,19.150004529306724,neg,2,d -新一波,19.08289033344819,neg,3,nr -玻璃房,19.01250100555679,neg,3,n -讲于,19.01250100555679,neg,2,v -期货市场,18.983354659897277,neg,4,n -潮水般,18.912965332005875,neg,3,l -逊色,18.912965332005875,neg,2,n -必定,18.86049791211174,neg,2,d -美剧,18.86049791211174,neg,2,nt -王朔,18.819855927614395,neg,2,nr -不法行为,18.819855927614395,neg,4,l -装满,18.787434449922014,neg,2,v -失业,18.72032025406348,neg,2,n -恶劣行径,18.667852834169345,neg,4,l -证券市场,18.635431356476968,neg,4,n -亚布力,18.56736463012182,neg,3,nr -西山,18.497927832727033,neg,2,ns -隐痛,18.497927832727033,neg,2,a -初次见面,18.497927832727033,neg,4,d -Storytellers,18.497927832727033,neg,12,eng -睿智,18.497927832727033,neg,2,a -柔滑,18.497927832727033,neg,2,a -休眠期,18.497927832727033,neg,3,t -球团,18.497927832727033,neg,2,n -凶猛,18.497927832727033,neg,2,a -爽脆,18.497927832727033,neg,2,a -闲下来,18.497927832727033,neg,3,l -预感,18.497927832727033,neg,2,n -砸碎,18.497927832727033,neg,2,v -各庄,18.49792783272703,neg,2,r -促成,18.49792783272703,neg,2,v -瑞银,18.42753850483563,neg,2,j -千玺,18.313503261589606,neg,2,nr -小看,18.30528275478464,neg,2,v -不顾,18.234893426893237,neg,2,v -猎人,18.135357753342326,neg,2,n -涌向,18.135357753342323,neg,2,v -翻译,18.1194162094733,neg,2,v -飞行,18.01250100555679,neg,2,v -河风,18.003163140977456,neg,2,n -暂缓,17.912965332005875,neg,2,d -起伏,17.912965332005875,neg,2,v -历史纪录,17.912965332005875,neg,4,n -神女,17.912965332005875,neg,2,n -抵挡不住,17.912965332005875,neg,4,n -从南到北,17.912965332005875,neg,4,l -幻灭,17.912965332005875,neg,2,v -无处不在,17.912965332005875,neg,4,l -间谍,17.912965332005875,neg,2,n -游人,17.912965332005875,neg,2,n -防腐剂,17.912965332005875,neg,3,nz -居酒,17.86565961722752,neg,2,n -臭小子,17.86565961722752,neg,3,n -不规矩,17.819855927614395,neg,3,l -尽快,17.79497812796198,neg,2,d -渴求,17.787434449922014,neg,2,v -臃肿,17.760962238560825,neg,2,a -学佛,17.69057291066943,neg,2,n -法师,17.690572910669427,neg,2,n -回调,17.635431356476968,neg,2,v -玩电脑,17.631194363590495,neg,3,n -高低,17.565042028585566,neg,2,a -摇摇,17.54373152234016,neg,2,v -些小,17.54373152234016,neg,2,a -归来,17.497927832727033,neg,2,v -行径,17.497927832727033,neg,2,n -休整,17.497927832727033,neg,2,vn -不止一次,17.497927832727033,neg,4,l -比起,17.372396950643175,neg,2,v -关窗,17.37239695064317,neg,2,n -翻到,17.351086444397765,neg,2,v -习气,17.275535411390585,neg,2,n -其中,17.25819665584921,neg,2,r -制在,17.20842121553205,neg,2,v -操纵者,17.17599973783967,neg,3,n -挖山,17.128694023061314,neg,2,n -法语,17.10561040994827,neg,2,nz -付钱,17.003163140977456,neg,2,v -跨界,16.983354659897273,neg,2,n -没人管,16.958769021619,neg,3,l -报导,16.912965332005875,neg,2,n -简洁,16.912965332005875,neg,2,a -吵杂,16.912965332005875,neg,2,v -小吃店,16.912965332005875,neg,3,n -暗处,16.912965332005875,neg,2,d -市场疲软,16.912965332005875,neg,4,i -往回,16.912965332005875,neg,2,t -和谐社会,16.912965332005875,neg,4,l -完美无缺,16.912965332005875,neg,4,i -传感,16.912965332005875,neg,2,n -本心,16.912965332005875,neg,2,n -奔波,16.912965332005875,neg,2,v -开路,16.86565961722752,neg,2,n -管辖,16.854071642952306,neg,2,v -分,16.839860878505018,neg,1,v -本能,16.819855927614395,neg,2,n -解析,16.819855927614395,neg,2,vn -低成本,16.787434449922017,neg,3,l -稀缺,16.760962238560825,neg,2,a -卡座,16.753766737156624,neg,2,nz -不失,16.743040330563563,neg,2,v -事件,16.73214174423648,neg,2,n -毋庸置疑,16.690572910669427,neg,4,l -关大博,16.690572910669427,neg,3,nr -离别,16.690572910669427,neg,2,v -剂,16.667852834169345,neg,1,zg -迫切,16.64993092617208,neg,2,a -关门,16.64993092617208,neg,2,n -内在,16.63285741281314,neg,2,b -公积金,16.623458714810887,neg,3,n -拉杆箱,16.60176364371157,neg,3,n -蒙古包,16.60176364371157,neg,3,nz -Munich,16.60176364371157,neg,6,eng -劵,16.591037237118513,neg,1,zg -加盟,16.591037237118513,neg,2,ns -表决,16.565042028585566,neg,2,v -大龄,16.54373152234016,neg,2,n -天山网,16.519301483519598,neg,3,nz -如出一辙,16.497927832727033,neg,4,i -横盘,16.497927832727033,neg,2,n -释出,16.497927832727033,neg,2,v -的断,16.497927832727033,neg,2,d -萎缩,16.497927832727033,neg,2,v -钻,16.465506355034655,neg,1,v -摘帽,16.439034143673467,neg,2,n -店里,16.360424308977095,neg,2,n -严正,16.360424308977095,neg,2,nr -追捕,16.360424308977095,neg,2,v -疲软,16.351086444397765,neg,2,a -长长,16.35108644439776,neg,2,n -再,16.34048994553786,neg,1,d -生煎,16.32800283128472,neg,2,v -摄像头,16.32800283128472,neg,3,n -梦乡,16.328002831284717,neg,2,n -相辅相成,16.328002831284717,neg,4,i -追逐,16.328002831284717,neg,2,v -充斥,16.30528275478464,neg,2,v -布局,16.265267075936755,neg,2,n -走高,16.234893426893237,neg,2,v -净空,16.205146083499184,neg,2,n -杭州,16.193073251198612,neg,2,ns -操纵,16.17599973783967,neg,2,v -着迷,16.17599973783967,neg,2,v -禁止性,16.17599973783967,neg,3,n -折扣,16.17599973783967,neg,2,v -触犯,16.17599973783967,neg,2,v -贫困村,16.17599973783967,neg,3,n -吃吃喝喝,16.128694023061314,neg,4,l -翻翻,16.128694023061314,neg,2,v -字数,16.128694023061314,neg,2,n -而已,16.1194162094733,neg,2,y -庭院,15.990133192528335,neg,2,n -加剧,15.95735945136433,neg,2,v -欲望,15.912965332005877,neg,2,v -SelfSell,15.912965332005875,neg,8,eng -ThomasJ,15.912965332005875,neg,7,eng -蓝洞,15.912965332005875,neg,2,nr -霞姐,15.912965332005875,neg,2,n -经济学界,15.912965332005875,neg,4,n -另一家,15.912965332005875,neg,3,l -Sargent,15.912965332005875,neg,7,eng -舍离,15.912965332005875,neg,2,v -休息时间,15.912965332005875,neg,4,i -聚集,15.895891818646938,neg,2,v -表明,15.889118590051508,neg,2,v -酒屋,15.86565961722752,neg,2,n -放手,15.84585113614734,neg,2,v -注意力,15.79748811458594,neg,3,n -验证,15.77546180825594,neg,2,v -下应,15.753766737156623,neg,2,v -屋檐,15.753766737156623,neg,2,n -工作服,15.753766737156623,neg,3,n -办公,15.732393086364057,neg,2,n -传言,15.71365652378247,neg,2,n -麻辣,15.690572910669427,neg,2,z -米林,15.690572910669427,neg,2,nr -一不小心,15.690572910669427,neg,4,l -不孝,15.690572910669427,neg,2,a -灵性,15.690572910669427,neg,2,n -产值,15.690572910669427,neg,2,n -林芝,15.690572910669427,neg,2,nrt -别墅,15.690572910669426,neg,2,n -花,15.676075925600053,neg,1,v -包厢,15.654231063605707,neg,2,n -补种,15.623458714810893,neg,2,n -工作日内,15.60965291528586,neg,4,l -载货,15.601763643711573,neg,2,n -真人,15.591037237118515,neg,2,n -年销售额,15.591037237118513,neg,4,n -洗发水,15.543731522340158,neg,3,nz -结婚,15.497927832727033,neg,2,v -中日,15.497927832727033,neg,2,t -京东方,15.497927832727033,neg,3,nz -结合处,15.497927832727033,neg,3,n -股指,15.497927832727033,neg,2,n -扎堆,15.439034143673467,neg,2,ns -并于,15.431838642269263,neg,2,c -打破,15.410464991476694,neg,2,v -住房,15.402003412728495,neg,2,n -贫困县,15.368644815782066,neg,3,n -对外,15.360424308977096,neg,2,s -及后,15.32800283128472,neg,2,c -双拼,15.32800283128472,neg,2,nr -简单,15.328002831284719,neg,2,a -捕捉,15.328002831284717,neg,2,v -举足轻重,15.328002831284717,neg,4,i -负责人,15.296632880478763,neg,3,n -报送,15.208421215532047,neg,2,v -地位,15.20247194920086,neg,2,n -肉鸡,15.175999737839671,neg,2,n -就别,15.175999737839671,neg,2,d -欺诈,15.175999737839671,neg,2,vn -证券法,15.175999737839671,neg,3,n -审定,15.175999737839671,neg,2,v -领薪,15.175999737839671,neg,2,n -切实落实,15.168804236435465,neg,4,l -持续增长,15.105610409948271,neg,4,n -驻扎,15.10561040994827,neg,2,v -萤火虫,15.038496214089735,neg,3,n -炉膛,15.016801142990419,neg,2,n -国有企业,14.974365876670019,neg,4,n -下跌,14.967413116028252,neg,2,v -注,14.93568540850596,neg,1,v -违章,14.912965332005877,neg,2,vn -任何事物,14.912965332005875,neg,4,n -产地,14.912965332005875,neg,2,n -满脑子,14.912965332005875,neg,3,l -自贡市,14.891270260906557,neg,3,ns -阻拦,14.85407164295231,neg,2,v -可想而知,14.85407164295231,neg,4,l -说,14.828339769930308,neg,1,v -中型,14.810350265522992,neg,2,b -王者,14.79748811458594,neg,2,n -蓝天白云,14.797488114585938,neg,4,nr -生物质,14.753766737156623,neg,3,n -大虾,14.743040330563563,neg,2,n -阶段性,14.743040330563563,neg,3,n -误解,14.71365652378247,neg,2,v -展示,14.6803045752156,neg,2,v -出租车,14.661426565009913,neg,3,n -分界,14.654231063605708,neg,2,n -暂时,14.63994683759946,neg,2,d -追踪,14.591037237118517,neg,2,v -小伙伴,14.591037237118515,neg,3,n -合谋,14.591037237118513,neg,2,n -粮食安全,14.591037237118513,neg,4,l -上市,14.591037237118512,neg,2,ns -卖,14.563815768383492,neg,1,v -黄酒,14.543731522340158,neg,2,n -眼药水,14.543731522340158,neg,3,n -again,14.543731522340158,neg,5,eng -Akita,14.543731522340158,neg,5,eng -开到,14.543731522340158,neg,2,v -发油,14.543731522340158,neg,2,n -市场化,14.534453708752146,neg,3,n -违背,14.497927832727033,neg,2,v -睁眼,14.497927832727033,neg,2,n -刑法,14.439034143673467,neg,2,n -白鸽,14.43183864226926,neg,2,nr -包括,14.417938928613914,neg,2,v -刚好,14.410464991476694,neg,2,d -每月,14.410464991476694,neg,2,r -龙,14.410464991476694,neg,1,n -小宝宝,14.406227998590223,neg,3,nr -热闹,14.389403375948861,neg,2,a -摘自,14.368644815782066,neg,2,v -厨房,14.358376480328237,neg,2,n -单方面,14.32800283128472,neg,3,n -阴霾,14.32800283128472,neg,2,n -有待,14.32800283128472,neg,2,v -熬过,14.328002831284717,neg,2,v -吊打,14.280697116506362,neg,2,v -设计方案,14.260888635426182,neg,4,n -犀牛,14.239193564326866,neg,2,n -随便,14.233391401728008,neg,2,d -刻不容缓,14.231141292032131,neg,4,i -汽车尾气,14.23114129203213,neg,4,n -股价,14.213474443258336,neg,2,n -念头,14.175999737839671,neg,2,n -关乎,14.175999737839671,neg,2,v -早起,14.17599973783967,neg,2,v -逃,14.164027096173594,neg,1,v -生物,14.161644444862599,neg,2,n -板块,14.147430585642898,neg,2,n -心理学,14.10561040994827,neg,3,n -毛,14.097048396444848,neg,1,nr -重型,14.083915338848952,neg,2,b -办理,14.050468855755808,neg,2,n -警方正,14.038496214089735,neg,3,n -林正,14.038496214089735,neg,2,nr -历史关头,14.038496214089735,neg,4,n -屋,14.013216805641378,neg,1,n -后天,13.995427492197852,neg,2,t -评价,13.98857098875805,neg,2,n -不着,13.958769021619,neg,2,d -瞅,13.958769021619,neg,1,v -即将,13.943338981049395,neg,2,d -如下,13.939932379606146,neg,2,t -瓶盖,13.912965332005875,neg,2,n -经济学,13.883217988611824,neg,3,n -北京市,13.871813225339574,neg,3,ns -屋内,13.85407164295231,neg,2,s -参数,13.85407164295231,neg,2,n -标的,13.854071642952308,neg,2,n -刷卡,13.846876141548103,neg,2,n -奈,13.843291804199065,neg,1,vg -进一步提高,13.833238139535142,neg,5,i -江湖,13.833238139535142,neg,2,ns -或者,13.818867962016604,neg,2,c -口感,13.79748811458594,neg,2,n -仍然,13.796522660834333,neg,2,d -营业,13.775461808255939,neg,2,n -下滑,13.763218212501192,neg,2,v -博客,13.690572910669427,neg,2,nr -响应,13.683377409265225,neg,2,v -商业银行,13.66503781856229,neg,4,nt -动,13.63994683759946,neg,1,v 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+逊色,18.91104277305744,neg,2,n +美剧,18.858575353163303,neg,2,nt +王朔,18.81793336866596,neg,2,nr +不法行为,18.81793336866596,neg,4,l +装满,18.785511890973577,neg,2,v +失业,18.718397695115044,neg,2,n +必定,18.68865035172099,neg,2,d +恶劣行径,18.66593027522091,neg,4,l +证券市场,18.63350879752853,neg,4,n +亚布力,18.563519512224946,neg,3,nr +预感,18.496005273778596,neg,2,n +隐痛,18.496005273778596,neg,2,a +睿智,18.496005273778596,neg,2,a +凶猛,18.496005273778596,neg,2,a +Storytellers,18.496005273778596,neg,12,eng +砸碎,18.496005273778596,neg,2,v +初次见面,18.496005273778596,neg,4,d +球团,18.496005273778596,neg,2,n +闲下来,18.496005273778596,neg,3,l +西山,18.496005273778596,neg,2,ns +休眠期,18.496005273778596,neg,3,t +柔滑,18.496005273778596,neg,2,a +爽脆,18.496005273778596,neg,2,a +各庄,18.496005273778593,neg,2,r +促成,18.496005273778593,neg,2,v +瑞银,18.425615945887195,neg,2,j +千玺,18.31158070264117,neg,2,nr +小看,18.303360195836202,neg,2,v +不顾,18.2329708679448,neg,2,v +猎人,18.13343519439389,neg,2,n +涌向,18.133435194393886,neg,2,v 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-修炼,12.221803427452796,neg,2,v -协议书,12.221803427452796,neg,3,n -愿意,12.19110663022988,neg,2,v -人家,12.190094567725456,neg,2,n -总裁,12.17599973783967,neg,2,n -通道,12.158077829842407,neg,2,v -低于,12.156831594769393,neg,2,v -弥补,12.080075317841136,neg,2,v -前,12.060522520419735,neg,1,f -地上,12.054984336878302,neg,2,s -正规,12.041231181810977,neg,2,a -青皮,12.038496214089735,neg,2,n -大兴,12.038496214089735,neg,2,b -居,12.0291583495104,neg,1,v -验收,12.026252618334988,neg,2,v -城乡,12.01925012514509,neg,2,n -不用,12.006074736397355,neg,2,v -二级,11.987053799596364,neg,2,b -出差,11.958769021619,neg,2,v -笑话,11.958769021619,neg,2,n -天猫,11.908276686212247,neg,2,n +马,12.594276383285859,neg,1,n +逾,12.589114678170079,neg,1,vg +坑,12.589114678170079,neg,1,n +现代人,12.589114678170077,neg,3,n +桥,12.589114678170075,neg,1,n +闭关,12.541808963391722,neg,2,v +抢购,12.536831287237337,neg,2,v +还有,12.522000482311542,neg,2,v +瓜,12.497414844033267,neg,1,n +爹,12.473637460750142,neg,1,n +信号,12.47055806781817,neg,2,n +先进经验,12.45161115442014,neg,4,n +涉嫌,12.446156724328034,neg,2,v +最为,12.440722838277406,neg,2,d +没事,12.426331745971787,neg,2,v +谈,12.416278081307862,neg,1,v +解除,12.392441377902161,neg,2,v +当作,12.366722256833631,neg,2,v +借鉴,12.352075480869226,neg,2,v +哈哈哈,12.34017510222207,neg,3,o +扩容,12.326080272336284,neg,2,v +长时间,12.316096183763662,neg,3,l +维权,12.316096183763662,neg,2,n +动物,12.311092277830658,neg,2,n +通知,12.30656665388075,neg,2,v +大量,12.306180714898579,neg,2,n +难度,12.267186583282715,neg,2,d +落实,12.256209428726049,neg,2,a +气味,12.248077760335011,neg,2,n +本市,12.245491512183396,neg,2,n +协议书,12.21988086850436,neg,3,n +修炼,12.21988086850436,neg,2,v +锤,12.21988086850436,neg,1,ng +相聚,12.21988086850436,neg,2,v +愿意,12.189184071281444,neg,2,v +人家,12.18817200877702,neg,2,n +总裁,12.174077178891233,neg,2,n +通道,12.15615527089397,neg,2,v +低于,12.154909035820957,neg,2,v +前,12.093703762427214,neg,1,f +弥补,12.0781527588927,neg,2,v +地上,12.053061777929866,neg,2,s +正规,12.03930862286254,neg,2,a +青皮,12.036573655141298,neg,2,n +大兴,12.036573655141298,neg,2,b +居,12.027235790561964,neg,1,v +验收,12.024330059386552,neg,2,v +城乡,12.017327566196654,neg,2,n +不用,12.004152177448919,neg,2,v +二级,11.985131240647927,neg,2,b +笑话,11.956846462670564,neg,2,n +出差,11.956846462670564,neg,2,v +天猫,11.90635412726381,neg,2,n 地区,11.860224823483403,neg,2,n -巩义市,11.846876141548103,neg,3,ns -终止,11.846876141548103,neg,2,v -红叶,11.846876141548103,neg,2,n -车,11.827271583608594,neg,1,zg -家中,11.825502490755536,neg,2,s -看不到,11.79748811458594,neg,3,v -味道,11.788844020176686,neg,2,n -允许,11.783682315060908,neg,2,v -女,11.766123943676607,neg,1,b +车,11.852149084003871,neg,1,zg +巩义市,11.844953582599667,neg,3,ns +红叶,11.844953582599667,neg,2,n +终止,11.844953582599667,neg,2,v +家中,11.8235799318071,neg,2,s +味道,11.800096849975983,neg,2,n +看不到,11.795565555637504,neg,3,v +允许,11.781759756112471,neg,2,v 净化器,11.765884450552319,neg,3,n -上司,11.736376600282552,neg,2,n -本站,11.731398924128168,neg,2,n -药店,11.65620625159857,neg,2,n -峰会,11.652437781782657,neg,2,n -限行,11.643342747462972,neg,2,v -回忆,11.636840926731638,neg,2,v -随时,11.58384173571431,neg,2,d -青年,11.574105171383678,neg,2,t -拍摄,11.563909404277787,neg,2,v -时间段,11.557369524353122,neg,3,n -心脑血管,11.557369524353122,neg,4,n -中午,11.555413327387791,neg,2,t -只好,11.543731522340158,neg,2,d -虽说,11.520647909227117,neg,2,c -监管部门,11.475560019698579,neg,4,n -短板,11.460106367292065,neg,2,n -吸收,11.453533713368579,neg,2,v -停建,11.453533713368577,neg,2,v -天河,11.453533713368577,neg,2,ns -消灭,11.431838642269263,neg,2,v -升至,11.43183864226926,neg,2,v -同款,11.43183864226926,neg,2,n -全省各地,11.43183864226926,neg,4,l -发,11.42271612137879,neg,1,v -恒丰,11.421112235676201,neg,2,nz -电脑,11.406227998590222,neg,2,n -不当,11.373806520897846,neg,2,d -手上,11.373806520897846,neg,2,s -木质,11.373806520897846,neg,2,n -行政区域,11.353836130267988,neg,4,n -老年人,11.332302968718347,neg,3,n -工作日,11.332302968718347,neg,3,n -明天,11.318018742712098,neg,2,t -尽量减少,11.316361424849326,neg,4,i -故事,11.313052489818746,neg,2,n -写,11.261696154576308,neg,1,v -哪里,11.257613503393323,neg,2,r -就诊,11.221803427452796,neg,2,v -乡镇,11.221803427452796,neg,2,n -比例,11.118549465655768,neg,2,n -高温,11.109910547381897,neg,2,n -谈,11.10834537766951,neg,1,v -回到,11.105610409948271,neg,2,v -事故,11.088536896589332,neg,2,n -PGOne,11.084299903702862,neg,5,eng -回合,11.084299903702862,neg,2,v -轻,11.080075317841136,neg,1,a -黑龙江,11.080075317841134,neg,3,ns -质押,11.075022090114848,neg,2,v -东西,11.01895602769409,neg,2,ns -电力,11.006074736397355,neg,2,n -支队,11.005573887567163,neg,2,n -对手,10.958769021619002,neg,2,v +上司,11.734454041334116,neg,2,n +本站,11.729476365179732,neg,2,n +药店,11.654283692650134,neg,2,n +峰会,11.65051522283422,neg,2,n +限行,11.641420188514536,neg,2,v +女,11.634918367783204,neg,1,b +回忆,11.634918367783202,neg,2,v +随时,11.581919176765874,neg,2,d +拍摄,11.56198684532935,neg,2,v +心脑血管,11.555446965404686,neg,4,n +时间段,11.555446965404686,neg,3,n +中午,11.553490768439355,neg,2,t +只好,11.541808963391722,neg,2,d +虽说,11.51872535027868,neg,2,c +监管部门,11.473637460750142,neg,4,n +短板,11.458183808343628,neg,2,n +吸收,11.451611154420142,neg,2,v +天河,11.45161115442014,neg,2,ns +停建,11.45161115442014,neg,2,v +消灭,11.429916083320826,neg,2,v +同款,11.429916083320824,neg,2,n +升至,11.429916083320824,neg,2,v +全省各地,11.429916083320824,neg,4,l +发,11.420793562430354,neg,1,v +恒丰,11.419189676727765,neg,2,nz +电脑,11.404305439641785,neg,2,n +木质,11.37188396194941,neg,2,n +不当,11.37188396194941,neg,2,d +手上,11.37188396194941,neg,2,s +行政区域,11.351913571319551,neg,4,n +老年人,11.33038040976991,neg,3,n +工作日,11.33038040976991,neg,3,n +明天,11.316096183763662,neg,2,t +尽量减少,11.31443886590089,neg,4,i +故事,11.31112993087031,neg,2,n +青年,11.309148206601446,neg,2,t +写,11.259773595627872,neg,1,v +哪里,11.255690944444884,neg,2,r +就诊,11.21988086850436,neg,2,v +乡镇,11.21988086850436,neg,2,n +比例,11.116626906707332,neg,2,n +高温,11.107987988433461,neg,2,n +回到,11.103687850999835,neg,2,v +事故,11.086614337640896,neg,2,n +回合,11.082377344754425,neg,2,v +PGOne,11.082377344754425,neg,5,eng +轻,11.0781527588927,neg,1,a +黑龙江,11.078152758892696,neg,3,ns +质押,11.073099531166411,neg,2,v +东西,11.017033468745653,neg,2,ns +电力,11.004152177448919,neg,2,n +支队,11.003651328618727,neg,2,n +对手,10.956846462670566,neg,2,v 这些,10.951078253934515,neg,2,r -就餐,10.935685408505961,neg,2,v -蓝色,10.92933830174008,neg,2,n -时段,10.908276686212247,neg,2,n -玩,10.871306180368663,neg,1,v -人群,10.86857121264742,neg,2,n -雪乡,10.854071642952308,neg,2,n -公交,10.846876141548103,neg,2,n -交通管理,10.846876141548103,neg,4,n -龙牧,10.843291804199065,neg,2,nr -抢,10.843291804199065,neg,1,v -往,10.818447733221587,neg,1,zg -普及,10.804440875227707,neg,2,ns -市级,10.800960306492746,neg,2,n -申请,10.773413979607081,neg,2,v -限,10.731398924128168,neg,1,v -意见建议,10.716568119202373,neg,4,l -什么样,10.684146641509994,neg,3,r +就餐,10.933762849557523,neg,2,v +蓝色,10.927415742791643,neg,2,n +时段,10.90635412726381,neg,2,n +玩,10.869383621420226,neg,1,v +人群,10.866648653698984,neg,2,n +雪乡,10.852149084003871,neg,2,n +限,10.844953582599668,neg,1,v +交通管理,10.844953582599667,neg,4,n +公交,10.844953582599667,neg,2,n +龙牧,10.841369245250629,neg,2,nr +抢,10.841369245250629,neg,1,v +往,10.816525174273151,neg,1,zg +普及,10.80251831627927,neg,2,ns +市级,10.79903774754431,neg,2,n +申请,10.771491420658645,neg,2,v +意见建议,10.714645560253937,neg,4,l +什么样,10.682224082561557,neg,3,r 直接,10.65575431014422,neg,2,ad -利益,10.652437781782657,neg,2,n -call,10.624483720211657,neg,4,eng -处处,10.624483720211657,neg,2,v -加严,10.624483720211655,neg,2,nr -挪威,10.543731522340158,neg,2,ns -地板,10.543731522340158,neg,2,n -拜拜,10.543731522340158,neg,2,ns -涉气,10.524948046660741,neg,2,v -雪地,10.520647909227117,neg,2,n -突破,10.482512780340345,neg,2,vn -代表,10.464504831189583,neg,2,n -头条,10.461754220173546,neg,2,n -家长,10.437231901039477,neg,2,n -天府,10.43183864226926,neg,2,n -元旦,10.421112235676201,neg,2,t -啦,10.373806520897846,neg,1,y -村里,10.373806520897846,neg,2,s -列入,10.344375801018922,neg,2,v -住,10.30669232503931,neg,1,v -唐山市,10.261913640826949,neg,3,ns -建材,10.261913640826949,neg,2,n -企业名单,10.261913640826949,neg,4,n -有点,10.242561987619593,neg,2,n -晚上,10.221803427452796,neg,2,t -买,10.205861883583774,neg,1,v -讯,10.194799444968412,neg,1,ng -内心,10.171498345604729,neg,2,n -国家海洋局,10.155853164727892,neg,5,nt -配置,10.153631924811217,neg,2,v -任何人,10.151414099561398,neg,3,r -好像,10.147725283844494,neg,2,v -天津市,10.146436423407012,neg,3,ns -汉中,10.131605618481215,neg,2,ns -零时,10.109910547381899,neg,2,d -省辖市,10.109910547381899,neg,3,n -醒目,10.109910547381899,neg,2,v -小米,10.109910547381899,neg,2,n -梦,10.10078802649143,neg,1,n -亦,10.046716720894704,neg,1,d -无人驾驶,10.042600612422472,neg,4,nr -陈,10.006074736397357,neg,1,nr -契机,9.99410209473128,neg,2,n -指挥部,9.972407023631963,neg,3,n +利益,10.65051522283422,neg,2,n +call,10.62256116126322,neg,4,eng +处处,10.62256116126322,neg,2,v +加严,10.622561161263219,neg,2,nr +地板,10.541808963391722,neg,2,n +拜拜,10.541808963391722,neg,2,ns +挪威,10.541808963391722,neg,2,ns +涉气,10.523025487712305,neg,2,v +雪地,10.51872535027868,neg,2,n +突破,10.480590221391909,neg,2,vn +代表,10.462582272241146,neg,2,n +头条,10.45983166122511,neg,2,n +家长,10.43530934209104,neg,2,n +天府,10.429916083320824,neg,2,n +元旦,10.419189676727765,neg,2,t +啦,10.37188396194941,neg,1,y +村里,10.37188396194941,neg,2,s +列入,10.342453242070485,neg,2,v +住,10.304769766090873,neg,1,v +建材,10.259991081878512,neg,2,n +唐山市,10.259991081878512,neg,3,ns +企业名单,10.259991081878512,neg,4,n +有点,10.240639428671157,neg,2,n +晚上,10.21988086850436,neg,2,t +讯,10.192876886019976,neg,1,ng +内心,10.169575786656292,neg,2,n +国家海洋局,10.153930605779456,neg,5,nt +配置,10.15170936586278,neg,2,v +任何人,10.14949154061296,neg,3,r +好像,10.145802724896058,neg,2,v +天津市,10.144513864458576,neg,3,ns +汉中,10.129683059532779,neg,2,ns +小米,10.107987988433463,neg,2,n +零时,10.107987988433463,neg,2,d +省辖市,10.107987988433463,neg,3,n +醒目,10.107987988433463,neg,2,v +梦,10.098865467542993,neg,1,n +亦,10.044794161946268,neg,1,d +无人驾驶,10.040678053474036,neg,4,nr +陈,10.00415217744892,neg,1,nr +契机,9.992179535782844,neg,2,n +指挥部,9.970484464683526,neg,3,n 空气质量,9.950381659701549,neg,4,n -大规模,9.901738076582623,neg,3,b -前面,9.78884402017669,neg,2,f -构建,9.783682315060908,neg,2,v -转发,9.771142018443232,neg,2,v -公共交通,9.731398924128168,neg,4,nt -内,9.687955795827591,neg,1,n -市区,9.68767754669885,neg,2,n -安慰,9.685750527212585,neg,2,v -分享,9.679345655246173,neg,2,v -现象,9.676153850756466,neg,2,n -钱,9.617732103783935,neg,1,n -哈哈,9.612994184777271,neg,2,o -聊,9.589535211953283,neg,1,v -空气,9.585885402935428,neg,2,n -准备,9.577947237678071,neg,2,v -实在,9.573115329121253,neg,2,v -细节,9.549195592907418,neg,2,n -呼吸道,9.501101304706374,neg,3,l -呼吸,9.495112817119978,neg,2,v -突然,9.393984402835475,neg,2,ad -喜欢,9.374170076068506,neg,2,v -博,9.35390696346014,neg,1,nr -次,9.302555625324295,neg,1,q -看来,9.240539990034382,neg,2,v -函,9.231141292032131,neg,1,n -不好,9.231141292032131,neg,2,d -微,9.225816489959419,neg,1,n -全省,9.199292425992791,neg,2,n -来自,9.190727023586222,neg,2,v -乘坐,9.183911128825676,neg,2,v -这样,9.183015000882756,neg,2,r -论坛,9.109910547381899,neg,2,n -望,9.109910547381897,neg,1,v -以前,9.10078802649143,neg,2,f -老板,9.08429990370286,neg,2,n -风,9.052134216102695,neg,1,n -白色,8.846876141548105,neg,2,n -免费,8.826976584110401,neg,2,vn -爱情,8.762371808815498,neg,2,n -大雾,8.759413300297766,neg,2,n -天津,8.753766737156623,neg,2,ns -看到,8.73470962022584,neg,2,v -极端,8.716568119202371,neg,2,n -成都,8.698241471395782,neg,2,ns -不错,8.6919824809241,neg,2,a +大规模,9.899815517634186,neg,3,b +实在,9.866648653698986,neg,2,v +前面,9.786921461228253,neg,2,f +构建,9.781759756112471,neg,2,v +转发,9.769219459494796,neg,2,v +公共交通,9.729476365179732,neg,4,nt +钱,9.693812056836771,neg,1,n +内,9.687955795827598,neg,1,n +市区,9.685754987750414,neg,2,n +安慰,9.683827968264149,neg,2,v +分享,9.677423096297737,neg,2,v +现象,9.674231291808029,neg,2,n +哈哈,9.65916591402988,neg,2,o +聊,9.587612653004847,neg,1,v +空气,9.585885402935425,neg,2,n +准备,9.576024678729635,neg,2,v +细节,9.547273033958982,neg,2,n +呼吸道,9.499178745757938,neg,3,l +呼吸,9.49319025817154,neg,2,v +突然,9.392061843887038,neg,2,ad +博,9.351984404511704,neg,1,nr +次,9.300633066375859,neg,1,q +看来,9.238617431085945,neg,2,v +喜欢,9.236666552318573,neg,2,v +不好,9.229218733083695,neg,2,d +函,9.229218733083695,neg,1,n +微,9.223893931010982,neg,1,n +全省,9.197369867044355,neg,2,n +来自,9.188804464637785,neg,2,v +这样,9.183015000882758,neg,2,r +乘坐,9.18198856987724,neg,2,v +望,9.18198856987724,neg,1,v +论坛,9.107987988433463,neg,2,n +以前,9.098865467542993,neg,2,f +风,9.083378521089461,neg,1,n +老板,9.082377344754423,neg,2,n +争取,8.907290638196331,neg,2,v +列,8.844953582599668,neg,1,v +白色,8.844953582599668,neg,2,n +免费,8.825054025161965,neg,2,vn +爱情,8.760449249867062,neg,2,n +大雾,8.75749074134933,neg,2,n +天津,8.751844178208186,neg,2,ns +看到,8.734709620225836,neg,2,v +极端,8.714645560253935,neg,2,n +成都,8.696318912447346,neg,2,ns +不错,8.690059921975664,neg,2,a 易地,8.578938713093388,neg,2,n -常务会议,8.51493425803272,neg,4,n +常务会议,8.513011699084284,neg,4,n 扶贫,8.386293635150992,neg,2,v -水中,8.346513733165636,neg,2,s -插入,8.346513733165633,neg,2,v -折下来,8.346513733165633,neg,3,v -两侧,8.302555625324294,neg,2,f -办,8.282091522764578,neg,1,v -黑色,8.222385276640312,neg,2,n +水中,8.346513733165638,neg,2,s +折下来,8.346513733165635,neg,3,v +插入,8.346513733165635,neg,2,v +两侧,8.300633066375857,neg,2,f +办,8.280168963816141,neg,1,v +黑色,8.220462717691875,neg,2,n 即便,8.150915572220065,neg,2,c 负责,8.123259229317197,neg,2,v -现,8.074286637651177,neg,1,tg +现,8.07236407870274,neg,1,tg 成交量,8.05866825034057,neg,3,n 高,8.04837130831639,neg,1,a -希望,8.021310506245412,neg,2,v +希望,8.021310506245413,neg,2,v 愈加,7.90086680798075,neg,2,d 搬迁,7.90086680798075,neg,2,v -讲,7.8855200395883625,neg,1,v -最高,7.877249790591625,neg,2,a -熟悉,7.871306180368663,neg,2,v -习惯,7.843291804199065,neg,2,n -微笑,7.815811067776959,neg,2,vn -外,7.7769189515611,neg,1,f -每天,7.7318038890504255,neg,2,r -纳入,7.731398924128168,neg,2,v +讲,7.883597480639926,neg,1,v +最高,7.875327231643189,neg,2,a +熟悉,7.869383621420226,neg,2,v +习惯,7.841369245250629,neg,2,n +微笑,7.813888508828521,neg,2,vn +每天,7.731803889050427,neg,2,r +纳入,7.729476365179732,neg,2,v 该,7.706990761498751,neg,1,r -调研,7.646178791310975,neg,2,vn +调研,7.644256232362537,neg,2,vn 整理,7.6257088430644675,neg,2,n -高度重视,7.612231459387637,neg,4,l -只是,7.609619457996617,neg,2,c -放进,7.539158811108031,neg,2,v -到位,7.368371727153729,neg,2,v +高度重视,7.6103089004392,neg,4,l +只是,7.607696899048177,neg,2,c +放进,7.539158811108033,neg,2,v +到位,7.368371727153722,neg,2,v 准确,7.348325784951964,neg,2,a -经历,7.314912831844277,neg,2,n +经历,7.31299027289584,neg,2,n 实际上,7.303780748177104,neg,3,d 选,7.303780748177104,neg,1,zg -寻找,7.276124405274235,neg,2,v +寻找,7.276124405274237,neg,2,v 吗,7.222794902868111,neg,1,y -台,7.222385276640312,neg,1,q +台,7.220462717691875,neg,1,q 之,7.1111356702347095,neg,1,u 打算,7.076815597050832,neg,2,v -其他,6.964054788781148,neg,2,r +其他,6.965977347729584,neg,2,r 过去,6.918863237274593,neg,2,t 是否,6.90086680798075,neg,2,v -当,6.843291804199065,neg,1,t 部分,6.82845570620951,neg,2,n -批准,6.676951140105793,neg,2,v +当,6.827563445725598,neg,1,t +批准,6.675028581157356,neg,2,v 不得,6.578938713093388,neg,2,v -身边,6.539158811108031,neg,2,s -及其,6.537020878961316,neg,2,c +身边,6.539158811108033,neg,2,s +及其,6.53509832001288,neg,2,c 审核,6.508549385201986,neg,2,v 香港,6.4964258261194985,neg,2,ns -连续,6.466054357607174,neg,2,a +连续,6.464131798658737,neg,2,a 仅,6.392317422778758,neg,1,d 超过,6.315904307259595,neg,2,v 集团,6.3037807481771,neg,2,n 未,6.279835052552071,neg,1,d -出行,6.128057894092157,neg,2,v +出行,6.12613533514372,neg,2,v 震荡,6.1111356702347095,neg,2,v -督察组,5.983213778588544,neg,3,n 判断,5.981852653289742,neg,2,v -口袋,5.954196310386873,neg,2,n +督察组,5.981291219640108,neg,3,n +口袋,5.954196310386875,neg,2,n 微生物,5.941210668792397,neg,3,l -啊,5.914374902260548,neg,1,zg +啊,5.912452343312111,neg,1,zg 发现,5.90086680798075,neg,2,v -t,5.869596218048189,neg,1,x +t,5.8676736590997525,neg,1,x 测试,5.845490050944376,neg,2,vn -cn,5.7855799622792325,neg,2,eng +cn,5.783657403330796,neg,2,eng 须,5.730941806538437,neg,1,d 复苏,5.718818247455946,neg,2,v -整个,5.62662165235837,neg,2,b -什么,5.587868231896568,neg,2,r -呢,5.539158811108031,neg,1,y +整个,5.626621652358372,neg,2,b +什么,5.58786823189657,neg,2,r +呢,5.539158811108033,neg,1,y 经验,5.429311630260962,neg,2,n -天,5.344375801018922,neg,1,q -北京,5.315494681031792,neg,2,ns +么,5.412781525338479,neg,1,y +天,5.342453242070485,neg,1,q +北京,5.313572122083356,neg,2,ns 处于,5.303780748177104,neg,2,v 全国,5.283383051225595,neg,2,n -史,5.129283016944964,neg,1,nr +史,5.1292830169449655,neg,1,nr +那,5.017921907997263,neg,1,r 真实,5.017921907997263,neg,2,d -那,5.017921907997261,neg,1,r 真的,5.002905731175412,neg,2,d 营商,4.984279390684279,neg,2,n 甚至,4.981852653289746,neg,2,d @@ -783,7 +788,7 @@ cn,5.7855799622792325,neg,2,eng 证,4.7488637145357,neg,1,n 出门,4.730941806538437,neg,2,v 区域,4.697123176356628,neg,2,n -家里,4.584962500721156,neg,2,s +家里,4.584962500721158,neg,2,s 大大,4.564784618783525,neg,2,b 维持,4.4964258261195,neg,2,v 交流,4.4964258261194985,neg,2,n @@ -804,44 +809,43 @@ MACD,4.166277224427169,neg,4,eng 打击,4.163901213814544,neg,2,v 已经,4.087277860756387,neg,2,d 状态,4.085503988212288,neg,2,n -房间,4.079727192470735,neg,2,n +房间,4.079727192470736,neg,2,n 共享,4.070389327891396,neg,2,v 明确提出,4.026397690064609,neg,4,n -凸显,3.9818526532897422,neg,2,a 处,3.9818526532897422,neg,1,n -有着,3.954196310386873,neg,2,v -充满,3.784271308944561,neg,2,a -你们,3.7548875021634682,neg,2,r -感受,3.7318038890504273,neg,2,v -所以,3.7318038890504255,neg,2,c +凸显,3.9818526532897422,neg,2,a +有着,3.954196310386875,neg,2,v +充满,3.7842713089445628,neg,2,a +你们,3.75488750216347,neg,2,r +感受,3.731803889050429,neg,2,v +所以,3.7318038890504273,neg,2,c 形势,3.7188182474559497,neg,2,n 名单,3.709246735439118,neg,2,n 上线,3.642132539580583,neg,2,n -语言,3.6322682154995114,neg,2,n +语言,3.632268215499513,neg,2,n 确定,3.5647846187835253,neg,2,v 方案,3.5342998825069873,neg,2,n 找,3.5174789040051717,neg,1,v 蓝天,3.473069620650257,neg,2,nr -怎么,3.469549990224209,neg,2,r +怎么,3.4714725491726455,neg,2,r 公布,3.4329594072761065,neg,2,v 破坏,3.406102116231171,neg,2,v 严格,3.399786858148982,neg,2,ad 重,3.3340028260192973,neg,1,a 低价,3.303780748177104,neg,2,n -建立,3.2710174595126453,neg,2,v +建立,3.2729400184610835,neg,2,v 核实,3.2455149793681937,neg,2,n 缓解,3.237691557719332,neg,2,v -事情,3.2172307162206693,neg,2,n +事情,3.217230716220671,neg,2,n 听从,3.169925001442312,neg,2,v -KDJ,3.1662772244271693,neg,3,eng 危害,3.1662772244271693,neg,2,n -带,3.145979305817283,neg,1,v +KDJ,3.1662772244271693,neg,3,eng +带,3.145979305817285,neg,1,v 差距,3.133855746734792,neg,2,n 实体,3.0780025120012695,neg,2,n 困难,3.0255350921071376,neg,2,an 调整,3.0047026189652897,neg,2,vn -进一步,2.9474104352104717,neg,3,d -然后,2.9466704975938747,neg,2,c +进一步,2.94933299415891,neg,3,d 环境影响,2.9114633253983406,neg,4,n 国务院,2.9008668079807496,neg,3,nt 儿童,2.8910128511045166,neg,2,n @@ -855,20 +859,20 @@ KDJ,3.1662772244271693,neg,3,eng 报,2.6173839784135318,neg,1,n 广告,2.615659297944072,neg,2,n 气象条件,2.6020360140800953,neg,4,n -预警,2.577794139695369,neg,2,vn +预警,2.5797166986438054,neg,2,vn 合格,2.485426827170242,neg,2,n 机场,2.4405725913859797,neg,2,n -新鲜,2.3692338096657153,neg,2,ns +新鲜,2.369233809665717,neg,2,ns 极大,2.3219280948873617,neg,2,a 黄色,2.3202044446932195,neg,2,n 抓,2.3125902303080252,neg,1,v 专利,2.3039316655935167,neg,2,n 谨慎,2.3037807481771058,neg,2,a -后,2.297065147451672,neg,1,f +后,2.2989877064001085,neg,1,f 有关,2.264089247783941,neg,2,vn 审查,2.248790111401057,neg,2,vn -打,2.237691557719332,neg,1,v 气温,2.237691557719332,neg,2,n +打,2.237691557719332,neg,1,v 建设项目,2.2284414660092526,neg,4,n 丰富,2.222392421336446,neg,2,a 退出,2.222392421336446,neg,2,v @@ -878,15 +882,15 @@ http,2.083657929046911,neg,4,eng 应急,2.063046605196094,neg,2,vn 采取相应,2.027422695465372,neg,4,nr 高性能,1.959358015502655,neg,3,n -手机,1.9059129108544006,neg,2,n +手机,1.9078354698028388,neg,2,n 能源,1.8744691179161386,neg,2,n 值得,1.866248611111173,neg,2,v -温,1.8347776175229775,neg,1,nr +全市,1.8347776175229775,neg,2,n 重度,1.8347776175229775,neg,2,n 曝光,1.8347776175229775,neg,2,nz -全市,1.8347776175229775,neg,2,n -活动,1.8221969408507555,neg,2,vn -温暖,1.766569307211105,neg,2,an +温,1.8347776175229775,neg,1,nr +活动,1.8101803088413178,neg,2,vn +温暖,1.7665693072111068,neg,2,an 关系,1.7655347463629774,neg,2,n 灰尘,1.7162070339994084,neg,2,n 认可,1.6599245584023805,neg,2,v @@ -901,30 +905,31 @@ http,2.083657929046911,neg,4,eng 修复,1.4474589769712196,neg,2,v 时候,1.4381211123918831,neg,2,n 长效机制,1.4090137116510757,neg,4,n -其次,1.3923174227787598,neg,2,r +其次,1.3923174227787616,neg,2,r 表达,1.3860584323070793,neg,2,v 个人,1.349584437790229,neg,2,n 说话,1.3330543328677429,neg,2,v 高峰,1.237691557719332,neg,2,nr 限产,1.2281200457025037,neg,2,v -表示,1.2085588459405727,neg,2,v +表示,1.2104814048890091,neg,2,v 呵呵,1.179659436347917,neg,2,v -且,1.1495938339222729,neg,1,zg -理由,1.1255308820838597,neg,2,n -高速,1.1076348775011748,neg,2,d +且,1.1515163928707093,neg,1,zg +理由,1.1255308820838614,neg,2,n +高速,1.1095574364496112,neg,2,d 近期,1.044394119358456,neg,2,t -我们,1.0084850961167717,neg,2,r +我们,1.0123302140136659,neg,2,r 多变,0.9639307452924797,neg,2,v 部门,0.9089401085164273,neg,2,n -水平,0.8745024511952124,neg,2,n +水平,0.8764250101436488,neg,2,n 细腻,0.8744691179161403,neg,2,a -整改,0.8526464964956588,neg,2,v +整改,0.8545690554440952,neg,2,v 月底,0.8479969065549486,neg,2,t 一律,0.7923423512025778,neg,2,d -出现,0.6925815836942917,neg,2,v +出现,0.6945041426427281,neg,2,v 发布,0.6887983117056962,neg,2,v 减排,0.654205371881158,neg,2,v 召开,0.6063361515137249,neg,2,v +敦促,0.593438282788501,neg,2,v 旅游景点,0.5849625007211596,neg,4,n 都市,0.5849625007211525,neg,2,ns 赔偿,0.569365645670139,neg,2,v @@ -933,18 +938,16 @@ http,2.083657929046911,neg,4,eng 市政府,0.34792330342030553,neg,3,n 市委,0.34792330342030553,neg,2,n 有限公司,0.3262282323209895,neg,4,n -敦促,0.27151018790113923,neg,2,v 机动车,0.24981511680182145,neg,3,n +行情,0.24945528148729323,neg,2,n 机会,0.2479275134435852,neg,2,n -行情,0.24753272253885683,neg,2,n 攻坚,0.15670571241033926,neg,2,vn -落地,0.1468413883292694,neg,2,n +落地,0.14684138832927118,neg,2,n 坐,0.13326653086346418,neg,1,v 摄取,0.06413033741971752,neg,2,v 适中,0.0,neg,2,v 品质,0.0,neg,2,n -备货,0.0,neg,2,n -起起落落,0.0,neg,4,i -静观其变,0.0,neg,4,l 稳固,0.0,neg,2,a 如影随形,0.0,neg,4,i +备货,0.0,neg,2,n +静观其变,0.0,neg,4,l diff --git a/notebook/output/sopmi_candi_words/pos.txt b/notebook/output/sopmi_candi_words/pos.txt index 6da6c8b..0caca64 100644 --- a/notebook/output/sopmi_candi_words/pos.txt +++ b/notebook/output/sopmi_candi_words/pos.txt @@ -1,3344 +1,3347 @@ -保持,87.28493062512526,pos,2,v -风险,70.15627986116269,pos,2,n -货币政策,66.28476448498694,pos,4,n -发展,64.40272795986517,pos,2,vn -不要,63.71800916752808,pos,2,df -理念,61.20243677573371,pos,2,n -整体,59.41531515671559,pos,2,n -下,59.321140440512984,pos,1,f -引导,58.581720875817105,pos,2,v -投资,57.71720491331896,pos,2,vn -加强,57.067969337267684,pos,2,v -自己,53.2550377249969,pos,2,r -提升,52.80686380719989,pos,2,v -和,52.12334472663675,pos,1,c -稳步,51.581932116557915,pos,2,d -重要,51.095865548255034,pos,2,a -生活,51.006565910407616,pos,2,vn -杠杆,49.84375798767675,pos,2,n -所,49.74232898764617,pos,1,c -成长,49.71836088803788,pos,2,v -货币,49.432961499433695,pos,2,n -树立,48.829530387180434,pos,2,v -避免,48.21132191893369,pos,2,v -提高,47.69005146422065,pos,2,v -中,46.39719269992956,pos,1,f -控制,46.17454857867381,pos,2,v -确保,45.07181460054887,pos,2,v -让,44.9433158488092,pos,1,v -中国,44.89733466993125,pos,2,ns -有,43.995345247716045,pos,1,v -同时,43.462946705611216,pos,2,c -态势,43.445753026523874,pos,2,n -变得,43.379938272824404,pos,2,v -经营,42.90499985695881,pos,2,vn -服务,42.5285131761453,pos,2,vn -供给,42.48050088898478,pos,2,vn -有效,42.17149464383093,pos,2,a -做好,41.79417360445815,pos,2,v -产品,41.582843408229124,pos,2,n -持续,41.49153204643495,pos,2,vd -才能,40.59985971592194,pos,2,v -保障,40.569613448391245,pos,2,v -监管,40.45913912341231,pos,2,vn -温和,40.18657174033069,pos,2,a -来,39.90154391336233,pos,1,v -金融,39.864518206721314,pos,2,n -空调,39.80507744251068,pos,2,n -发力,39.561696330777565,pos,2,n -侧,39.432465623418025,pos,1,v -而,39.42216250402213,pos,1,c -引领,39.408988516032565,pos,2,v -传统,39.3305611064199,pos,2,n -工作,39.21901704712857,pos,2,vn -模式,39.09550820056968,pos,2,n -时刻,38.87010444727194,pos,2,n -社会,38.818476313891544,pos,2,n -都,38.75124747829326,pos,1,d -业绩,38.73921803353327,pos,2,n -稳定,38.66853671703833,pos,2,a -别人,38.54419520571457,pos,2,r -它,38.197385240759246,pos,1,r -改革,38.04747053379606,pos,2,vn -基本,37.99307298174401,pos,2,n -同责,37.85837366621722,pos,2,p -增长,37.84352585861889,pos,2,v -但是,37.66026990576275,pos,2,c -加快,37.56078097421275,pos,2,v -最好,37.538754516718036,pos,2,a -低,37.44533538818605,pos,1,a -客户,37.29677567095841,pos,2,n -治理,36.984347781307974,pos,2,v -推进,36.954306838982745,pos,2,v -肌肤,36.53440947560486,pos,2,n -更多,36.471921865529204,pos,2,d -绿色,36.370070872559886,pos,2,n -大幅,36.31311879762872,pos,2,d -使用,36.016362154322366,pos,2,v -法,36.002504693574,pos,1,j -积极,35.999140029945664,pos,2,ad -珍惜,35.91093609009158,pos,2,a -有望,35.88433193899452,pos,2,v -稳健,35.71223794881475,pos,2,a -治,35.53719815893227,pos,1,v -为主,35.508374904641364,pos,2,v -这种,35.50233133157733,pos,2,r -系统,35.11826406533673,pos,2,n -GDP,35.064827848487646,pos,3,eng -能,35.01979132623009,pos,1,v -发生,35.004737995860545,pos,2,v -条件,34.893874339400035,pos,2,n -作为,34.83621007080971,pos,2,v -但,34.834623026356695,pos,1,c -高压,34.653830055612445,pos,2,n -每次,34.640776217681946,pos,2,r -保洁,34.61189176949478,pos,2,a -生态,34.53727777140489,pos,2,n -不,34.50322571487824,pos,1,d -只求,34.230320919091085,pos,2,v -以及,34.207796048035554,pos,2,c -方法,33.986252635860055,pos,2,n -因为,33.94433609164086,pos,2,c -预计,33.87751081418412,pos,2,vn -一样,33.78676548823299,pos,2,r -安全,33.74831524543912,pos,2,an -需求,33.71798843375477,pos,2,v -重回,33.58081816617522,pos,2,v -提供,33.54426727048135,pos,2,v -认真,33.494503901560456,pos,2,ad -如此,33.346898200491374,pos,2,c -慌张,33.28677185290372,pos,2,a -拿货,33.279648631454656,pos,2,n -创新,33.1932114961863,pos,2,v -实现,33.127544693735885,pos,2,v -公司,33.000543958067254,pos,2,n -教育,32.99107597276968,pos,2,vn -财力,32.717324056345745,pos,2,n -更加,32.70540096465305,pos,2,d -按期,32.65367405632014,pos,2,d -治疗,32.647021787605,pos,2,v -环境保护,32.646972027639904,pos,4,n -坚定,32.601780453527496,pos,2,a -政府,32.58477122049504,pos,2,n -选择,32.57687590304151,pos,2,v -全面,32.434248082677804,pos,2,n -不是,32.169484603248094,pos,2,c -一岗双责,32.16848299448725,pos,4,l -身上,32.01154521237765,pos,2,s -污染,31.999841837500465,pos,2,vn -扩散,31.77370942120747,pos,2,v -良好,31.769812917005012,pos,2,a -企业家,31.67963916408628,pos,3,n -最新,31.63040580102745,pos,2,d -可,31.60696793390614,pos,1,v -投资者,31.538136974222514,pos,3,n -进入,31.429065976850204,pos,2,v -要求,31.221633546990095,pos,2,v -其,31.126135133670967,pos,1,r -只,31.08969874495305,pos,1,d -处理,30.94697184899399,pos,2,v -方式,30.90201147570427,pos,2,n -稳定性,30.561879072480394,pos,3,n -也,30.55255051819016,pos,1,d -房地产,30.520260676346098,pos,3,j -企业,30.508336889216316,pos,2,n -符合,30.461790417080366,pos,2,v -可取,30.41089316473291,pos,2,v -自身,30.348930430352063,pos,2,r -孩子,30.290306282191395,pos,2,n -公园,30.257937540791247,pos,2,n -控,30.23903995323739,pos,1,v -能力,30.12349776429332,pos,2,n -消费,30.10095415414881,pos,2,vn -掐死,30.066470432058463,pos,2,v -规模,30.06365563843908,pos,2,n -及,29.992429015535443,pos,1,c -地方,29.939863362989225,pos,2,n -基础,29.901443463359563,pos,2,n -正常,29.879396161867632,pos,2,d -考核,29.862642332410932,pos,2,vn -全,29.845129142644346,pos,1,a -那么,29.839666097101777,pos,2,r -使,29.712671372367524,pos,1,v -营造,29.697451382116228,pos,2,vn -配方,29.636419836793493,pos,2,n -终结,29.603538242675306,pos,2,v -经济,29.5550845511858,pos,2,n -协调,29.51293402011617,pos,2,v -公共,29.486245616018614,pos,2,b -长治久安,29.469682495940518,pos,4,l -战斗力,29.433613241232994,pos,3,n -事,29.380345264192556,pos,1,n -已然,29.360785394180063,pos,2,d -政策,29.34277847644691,pos,2,n -广大,29.201602580381916,pos,2,a -增强,29.188044852788423,pos,2,v -生态环境,29.14322089569258,pos,4,l -观念,29.11459980404356,pos,2,n -建设,29.096846250923264,pos,2,vn -人行道,28.942608539541638,pos,3,n -时间,28.91766346526786,pos,2,n -影响,28.902279586266992,pos,2,vn -要,28.858883209440748,pos,1,v -特色,28.661149604673305,pos,2,n -关注,28.50797365413151,pos,2,v -保养,28.47446554645976,pos,2,v -更好,28.46892027766912,pos,2,d -政治,28.16515685579484,pos,2,n -基金,28.07136942257143,pos,2,n -如果,28.005214145007642,pos,2,c -保护,27.969732141483433,pos,2,v -三废,27.939710631995403,pos,2,n -宏观,27.907067426737157,pos,2,n -支柱,27.90111816040597,pos,2,n -农业,27.877451878369833,pos,2,n -无,27.870906697363413,pos,1,v -人,27.86223960544966,pos,1,n -请愿,27.82552818248009,pos,2,v -决定,27.729893510400977,pos,2,v -安排,27.728705940362985,pos,2,v -还要,27.70253776044509,pos,2,c -手册,27.68379781459077,pos,2,n -前提,27.680116313085957,pos,2,n -占,27.63951204737347,pos,1,v -资金,27.623356380564758,pos,2,n -节奏,27.622111783477706,pos,2,n -体制改革,27.60860949980647,pos,4,l -好,27.52793139582882,pos,1,a -意识形态,27.483538466564674,pos,4,l -而是,27.471385674314124,pos,2,c -金融市场,27.402568300081978,pos,4,n -贬值,27.39090538255639,pos,2,v -注意,27.386255099713765,pos,2,v -职责,27.379775542777168,pos,2,n -日常,27.19732734677507,pos,2,d -效率,26.990791231727947,pos,2,n -行驶,26.88390458908212,pos,2,v -总是,26.861257708952085,pos,2,c -战略,26.82779636050854,pos,2,n -开放,26.80530172272146,pos,2,v -政绩观,26.716825529004417,pos,3,n -比重,26.709183587753728,pos,2,n -强化,26.661175074410178,pos,2,v -生命,26.598876290480852,pos,2,vn -人民,26.591342357346306,pos,2,n -朋友,26.5165641902844,pos,2,n -变,26.383200382024214,pos,1,v -牢牢,26.36068171877914,pos,2,d -绿洲,26.2997574944982,pos,2,n -贯彻落实,26.253362510617677,pos,4,i -明显,26.245259418815593,pos,2,a -好处,26.238153147683548,pos,2,d -开始,26.21708936069187,pos,2,v -预期,26.14350229832707,pos,2,vn -更,26.138839309425304,pos,1,d -炭黑,26.107112416555804,pos,2,ns -趋于,26.077365073161754,pos,2,v -加持,26.064379431567275,pos,2,v -工程进度,26.029761234697695,pos,4,n -责任感,26.00900628049773,pos,3,n -推动,25.897037061265607,pos,2,v -必不可少,25.854972651825303,pos,4,l -维修,25.71818080861643,pos,2,v -无论是,25.718119177369616,pos,3,c -工业,25.70467423303539,pos,2,n -很,25.674299222686923,pos,1,zg -规划,25.672029802078413,pos,2,n -上升,25.665651757475473,pos,2,v -轨道交通,25.58646472931636,pos,4,n -增速,25.56791548935698,pos,2,v -法定程序,25.556676450050976,pos,4,n -修正,25.483265645520078,pos,2,nr -适度,25.46021577246103,pos,2,a -银行,25.379948339251357,pos,2,n -收到,25.32281789615203,pos,2,v -有序,25.23841574413986,pos,2,n -会议,25.178976172417947,pos,2,n -订单,25.174226612414344,pos,2,n -管制,25.115747417640115,pos,2,vn -中心,25.07638456718421,pos,2,n -却,24.94786537642836,pos,1,d -起,24.94596783332296,pos,1,v -稳中求进,24.93111290070381,pos,4,l -尝试,24.801252690296096,pos,2,vn -依赖于,24.726290632614877,pos,3,v -上帝,24.72452942868265,pos,2,n -心情,24.71913026244529,pos,2,n -群众,24.656717084302873,pos,2,n -会,24.61661759493782,pos,1,v -观,24.57130340018751,pos,1,vg -运行,24.566275580956777,pos,2,v -防,24.531357127703497,pos,1,v -干燥,24.526428109347904,pos,2,a -有力,24.510191311643645,pos,2,n -打造,24.48682624282347,pos,2,v -部署,24.46559709747804,pos,2,n -决心,24.464504994051154,pos,2,v -超,24.462282584064262,pos,1,v -定位,24.427307201669933,pos,2,n -用药,24.41917292630104,pos,2,n -根本原因,24.39951613562243,pos,4,n -毫无,24.353409045934008,pos,2,v -就要,24.329962648516553,pos,2,d -科学,24.301577501489685,pos,2,n -一直,24.299174510003922,pos,2,d -谁,24.21814789940913,pos,1,r -进行,24.19530801420909,pos,2,v -框架,24.191855495029614,pos,2,n -需要,24.06392752678731,pos,2,v -优雅,24.06266295897825,pos,2,a -赛事,24.04821872750224,pos,2,n -我国,24.044115280951868,pos,2,r -创造条件,23.99858795977763,pos,4,n -距离,23.90482530880905,pos,2,n -道路,23.864190029338758,pos,2,n -始终,23.86347615863722,pos,2,d -严禁,23.86277957777666,pos,2,v -颜值,23.855867392692822,pos,2,n -奖励,23.838793879333878,pos,2,vn -提前,23.81267527333632,pos,2,v -履行,23.811302750886686,pos,2,v -坚决,23.74481542579943,pos,2,ad -打开,23.742063567549536,pos,2,v -以来,23.740878407150404,pos,2,f -紧紧围绕,23.6870742362186,pos,4,l -高水平,23.604561349244708,pos,3,nr -水质,23.587519988174275,pos,2,n -大,23.471883971556977,pos,1,a -市场,23.466677770139462,pos,2,n -目标,23.463127643825132,pos,2,n -等待,23.45088539199521,pos,2,v -环境质量,23.43943682580798,pos,4,n -继续,23.404742535886854,pos,2,v -病害,23.4042961321193,pos,2,n -收,23.394619620776176,pos,1,v -可控,23.365196062438553,pos,2,v -省委,23.357567372699073,pos,2,j -大小,23.33503522939138,pos,2,b -每个,23.32665429775336,pos,2,r -办法,23.323129438571645,pos,2,n -扛起,23.314404270462603,pos,2,v -急剧,23.26209884330857,pos,2,d -回升,23.240863805444633,pos,2,v -韩城,23.189910313651133,pos,2,ns -健康,23.149358880794683,pos,2,a -爱护,23.12252516370392,pos,2,v -标准,23.103102555022325,pos,2,n -刚刚,23.08094632753626,pos,2,d -净利润,23.079962285375665,pos,3,n -改变,22.96675845745206,pos,2,v -洒水,22.927570711641575,pos,2,v -稍,22.862673307378707,pos,1,zg -资源,22.83921588463055,pos,2,n -产业,22.820315954248777,pos,2,n -成都市,22.74657684108527,pos,3,ns -总结,22.708368724617607,pos,2,n -分离,22.610494185436615,pos,2,v -财政政策,22.604612076026623,pos,4,n -制造,22.60267524746196,pos,2,v -路,22.582560783312342,pos,1,n -削减,22.537921098200584,pos,2,v -退市,22.52513324914237,pos,2,v -一般,22.50726748253771,pos,2,a -到达,22.50662965433134,pos,2,v -价值,22.495550860639796,pos,2,n -幸福感,22.491544080870554,pos,3,nr -以下,22.409836276390333,pos,2,f -富足,22.38808649756676,pos,2,a -短期,22.387502649285516,pos,2,b -党政,22.37872764719775,pos,2,n -太阳,22.367908615629126,pos,2,ns -霾,22.363984440199964,pos,1,g -做,22.35287882325452,pos,1,v -合规,22.339624352481604,pos,2,vn -家庭,22.31806608725355,pos,2,n -行动,22.314391696457335,pos,2,vn -每,22.309773255017134,pos,1,zg -上到,22.30528275478464,pos,2,f -废纸,22.249498806528937,pos,2,n -城市化,22.204794550737113,pos,3,n -态度,22.185452853729203,pos,2,n -得到,22.126658042664022,pos,2,v -污水,22.07981276435497,pos,2,n -基调,22.053913990032093,pos,2,n -牢固,22.044797210705326,pos,2,a -理财产品,22.041792458342805,pos,4,n -文明,22.025658692780652,pos,2,nr -国内,22.00495456218542,pos,2,s -思想,21.97985993483821,pos,2,n -女人,21.961576833863937,pos,2,n -气象,21.953607297945524,pos,2,n -波动,21.937187415113492,pos,2,vn -土地,21.909057605238996,pos,2,n -国家标准,21.861597437187605,pos,4,n -话,21.858756951843677,pos,1,n -尽量,21.852633105330796,pos,2,d -蔬菜,21.851157596122412,pos,2,n -网络,21.83825214395162,pos,2,n -感动,21.812840664571308,pos,2,v -夯实,21.773211680002365,pos,2,v -净化,21.759245028708893,pos,2,n -现在,21.716303558862663,pos,2,t -美丽,21.715868827128364,pos,2,ns -调节,21.682819904185315,pos,2,vn -降尘,21.676031944645615,pos,2,n -露,21.667852834169345,pos,1,v -大于,21.60072322706825,pos,2,d -管控,21.593033357605677,pos,2,vn -切实,21.559027026198905,pos,2,ad -情绪,21.530655089336108,pos,2,n -体系,21.522529863876947,pos,2,n -清浅,21.497927832727033,pos,2,a -方便,21.49752064150771,pos,2,a -创业,21.49313235289445,pos,2,n -带来,21.49213486673244,pos,2,v -影响力,21.47425522573034,pos,3,n -出口,21.467518467051683,pos,2,vn -真正,21.45455979871672,pos,2,d -方向,21.439838117376304,pos,2,n -二哈,21.427550508686522,pos,2,nrt -支持,21.399117181412638,pos,2,v -坚持,21.380716035487573,pos,2,v -安然,21.37239695064317,pos,2,nr -局部,21.332639088616386,pos,2,n -必要,21.331583742234283,pos,2,d -结构调整,21.297795978164913,pos,4,l -公正,21.29197548260094,pos,2,nr -并,21.281310354841146,pos,1,c -走向,21.280360426362215,pos,2,v -深入人心,21.275661293749636,pos,4,i -规律,21.255677437778097,pos,2,n -记住,21.24298505624036,pos,2,v -那里,21.232140404120756,pos,2,r -特别,21.212079485618858,pos,2,d -跨,21.184974047248346,pos,1,v -管理,21.100037239979372,pos,2,vn -闲雅,21.08289033344819,pos,2,a -上,21.078666417125017,pos,1,f -枯枝,21.017255519453247,pos,2,n -安全感,21.014003012164324,pos,3,nr -环评,20.994852186187867,pos,2,j -遇到,20.991321815064346,pos,2,v -现代化,20.929934709506526,pos,3,vn -缩减,20.92819810675514,pos,2,v -堤防,20.884719995219356,pos,2,n -壳,20.86978142052982,pos,1,n -未来,20.846704564700982,pos,2,t -帮助,20.829271246829933,pos,2,v -红润,20.819855927614395,pos,2,n -水资源,20.712256767901387,pos,3,n -风格,20.676622653765584,pos,2,n -平日,20.667852834169345,pos,2,t -双,20.644033020903365,pos,1,n -铁腕,20.623313643081268,pos,2,n -关怀,20.6022180201744,pos,2,nr -实习,20.600240397173067,pos,2,v -规范,20.594572957209223,pos,2,n -局面,20.57666354978245,pos,2,n -城镇,20.568815687959763,pos,2,ns -深化改革,20.56562855250153,pos,4,j -造成,20.516685664100798,pos,2,v -清姿,20.497927832727033,pos,2,a -柏油,20.497927832727033,pos,2,n -快步,20.497927832727033,pos,2,d -之后,20.489631383356326,pos,2,f -运用,20.43641455445065,pos,2,vn -投放,20.38010391259943,pos,2,v -率,20.37565195177337,pos,1,v -升起,20.37239695064317,pos,2,v -水润,20.30528275478464,pos,2,n -转型,20.30478794192123,pos,2,n -均,20.27893206735097,pos,1,d -驾驶,20.275334358042173,pos,2,v -起到,20.243700457614167,pos,2,v -金融风险,20.22231397720271,pos,4,n -我市,20.20904891238333,pos,2,r -适当,20.208851890356257,pos,2,a -不会,20.199747488003396,pos,2,v -主体,20.173438157293813,pos,2,n -可以,20.16230654902028,pos,2,c -自行车,20.160606892411113,pos,3,n -贯彻,20.136361746882724,pos,2,v -各,20.111496530215547,pos,1,r -存款,20.104297400948752,pos,2,n -安装,20.09226967929713,pos,2,v -follow,20.08289033344819,pos,6,eng -钢厂,20.079573805086625,pos,2,n -重大,20.071336766865805,pos,2,a -周围,20.012149472794718,pos,2,f -绿,20.011121761011097,pos,1,a -雪质,20.000428173256214,pos,2,n -生长,19.990410957652777,pos,2,n -此时,19.983951431864526,pos,2,c -农产品,19.97720147936618,pos,3,n -职能,19.953733198862274,pos,2,n -遗憾,19.844788323448483,pos,2,a -尊重,19.828202316778412,pos,2,a -错误,19.757320307232476,pos,2,n -矿山,19.72633933200359,pos,2,n -这处,19.72032025406348,pos,2,r -维稳,19.712256767901387,pos,2,v -这项,19.694109421191133,pos,2,r -下行,19.68069788816648,pos,2,v -主流,19.67618354450707,pos,2,b -盈利,19.671423216524126,pos,2,n -注重,19.66820853419337,pos,2,v -团队,19.63918390977854,pos,2,n -公平,19.63748854326214,pos,2,n -始终保持,19.6359298838183,pos,4,i -设备,19.6181908643265,pos,2,vn -竟然,19.61524482667787,pos,2,d -对待,19.60494781292998,pos,2,v -屏障,19.59880408753527,pos,2,n -行,19.554753635558107,pos,1,zg -农田,19.54252559950105,pos,2,n -并且,19.52510074591892,pos,2,c -升高,19.524656771445223,pos,2,v -沉稳,19.497927832727033,pos,2,a -走稳,19.497927832727033,pos,2,v -牛气,19.497927832727033,pos,2,n -寡头,19.497927832727033,pos,2,n -款款,19.497927832727033,pos,2,n -山野,19.497927832727033,pos,2,ns -静谧,19.497927832727033,pos,2,nr -负债,19.469280014408845,pos,2,n -更换,19.46600265159025,pos,2,v -炉石,19.447227985094006,pos,2,n -医院,19.423887607523277,pos,2,n -成熟,19.417857445097287,pos,2,a -宜,19.412972212479374,pos,1,vg -税额,19.411654063726356,pos,2,n -总,19.385516544647245,pos,1,b -容易,19.37939188861459,pos,2,a -居民,19.375675622561317,pos,2,n -鼓励,19.374129584755188,pos,2,v -肤质,19.37239695064317,pos,2,n -脚下,19.37239695064317,pos,2,f -雪道,19.37239695064317,pos,2,nr -限制,19.304338420480796,pos,2,v -达标排放,19.28738237130046,pos,4,l -方面,19.273886157692488,pos,2,n -放宽,19.249498806528937,pos,2,v -安保,19.17649826518101,pos,2,nr -区,19.172728168596343,pos,1,n -背后,19.16785971464284,pos,2,f -党中央,19.158896257748033,pos,3,nt -商业,19.135781759387072,pos,2,n -女孩,19.093421419380824,pos,2,n -港股,19.082890333448187,pos,2,n -最终,19.068228146241232,pos,2,d -形成,19.028202860623622,pos,2,v -他们,18.978451893175016,pos,2,r -作业,18.945218343826195,pos,2,n -如今,18.926788818553277,pos,2,t -黑猫,18.912965332005875,pos,2,n -压背,18.912965332005875,pos,2,n -超高压,18.912965332005875,pos,3,b -使身,18.912965332005875,pos,2,v -机构,18.87951551201047,pos,2,n -非,18.8710292272789,pos,1,h -我省,18.857230928987384,pos,2,r -地表水,18.85457017029364,pos,3,n -场地,18.84824825898017,pos,2,s -接触,18.836510820158008,pos,2,v -低温,18.836422823583383,pos,2,n -三脚架,18.819855927614395,pos,3,n -相结合,18.795780839086035,pos,3,v -财产,18.77568583718216,pos,2,n -断面,18.767262413671183,pos,2,n -西藏,18.74313740090085,pos,2,ns -组织,18.711918981203727,pos,2,v -修护,18.709431937920744,pos,2,v -露天,18.70283245875197,pos,2,v -父母,18.684201186585504,pos,2,n -市场主体,18.662327573882912,pos,4,n -同业,18.65336307884782,pos,2,j -监督管理,18.650429453513418,pos,4,n -晴天,18.649930926172082,pos,2,nz -最近,18.614972385129946,pos,2,f -老,18.599669010696246,pos,1,a -抗空,18.57192841417081,pos,2,nz -防范,18.55749309558793,pos,2,v -预算案,18.55039525262117,pos,3,n -环保,18.54882419104156,pos,2,j -热情,18.526883669681887,pos,2,n -到,18.502151317198482,pos,1,v -光采,18.497927832727033,pos,2,n -照常,18.497927832727033,pos,2,d -肉质,18.49792783272703,pos,2,n -压顶,18.49792783272703,pos,2,n -封闭,18.467989980628317,pos,2,v -隐患,18.457307372742775,pos,2,n -合理,18.43256223144906,pos,2,vn -呈现,18.428260805424877,pos,2,v -地质,18.42321464467278,pos,2,n -配戴,18.41992532072576,pos,2,v -复方,18.41992532072576,pos,2,n -总书记,18.40059818400503,pos,3,n -放在,18.396291424450755,pos,2,v -地域,18.379241314641963,pos,2,n -释放,18.369234013095696,pos,2,v -大气,18.36073382142757,pos,2,n -基础设施,18.360168361975894,pos,4,n -比如,18.332774584746176,pos,2,v -柱子,18.32800283128472,pos,2,n -双线,18.32800283128472,pos,2,n -车主,18.320516054665,pos,2,n -恶劣,18.309205708781718,pos,2,a -上市公司,18.304555663001018,pos,4,nt -天然,18.277858012926835,pos,2,b -法治,18.26693353527416,pos,2,n -装修,18.25989716001087,pos,2,v -创造,18.254129145539093,pos,2,v -有车,18.250000319283448,pos,2,v -横梁,18.234893426893237,pos,2,ns -标号,18.234893426893237,pos,2,n -分别,18.23092365195457,pos,2,d -建成,18.229641403707298,pos,2,v -浑然天成,18.20842121553205,pos,4,i -细嫩,18.20842121553205,pos,2,a -所得税法,18.202471949200863,pos,4,n -商家,18.198479901808177,pos,2,n -轻松,18.1906627266957,pos,2,a -感对,18.175999737839668,pos,2,v -围绕,18.12366852860179,pos,2,v -奋斗,18.108720150746144,pos,2,v -认为,18.105872045568084,pos,2,v -理想,18.07093880400232,pos,2,n -芙说,18.064968425450928,pos,2,n -进到,18.057355241341053,pos,2,v -氨基酸,18.038496214089733,pos,3,nz -程度,18.028366559415367,pos,2,n -店,18.014058341256963,pos,1,n -了解,18.003845797985388,pos,2,v -虽然,18.002563772749873,pos,2,c -必须,17.95790703452956,pos,2,d -债券,17.928743971163527,pos,2,n -闹市,17.912965332005875,pos,2,v -没承,17.912965332005875,pos,2,v -对输,17.912965332005875,pos,2,vn -责任心,17.912965332005875,pos,3,n -她,17.90662006680792,pos,1,r -妈妈,17.854817276072957,pos,2,n -新建,17.836422823583383,pos,2,ns -轿车,17.787434449922014,pos,2,n -从而,17.785304551264815,pos,2,c -满堂彩,17.743040330563566,pos,3,i -梦想,17.73728963156413,pos,2,n -摊铺,17.732393086364056,pos,2,n -习近平,17.730863362404698,pos,3,nrfg -代煤,17.72032025406348,pos,2,n -保证,17.717052053691233,pos,2,v -林丕容,17.709431937920748,pos,3,nr -状况,17.704586029345172,pos,2,n -永辉,17.69057291066943,pos,2,nr -较为,17.688205685297905,pos,2,d -接受,17.664849741690063,pos,2,v -菠萝汁,17.66142656500991,pos,3,n -组,17.65090062366511,pos,1,zg -杜清尘,17.649930926172082,pos,3,nr -中海,17.64993092617208,pos,2,ns -意识,17.634571746312012,pos,2,n -拉涨,17.623458714810894,pos,2,v -算是,17.58204843533356,pos,2,v -偏松,17.57192841417081,pos,2,a -工时费,17.57192841417081,pos,3,n -比美,17.57192841417081,pos,2,ns -母公司,17.55039525262117,pos,3,n -达到,17.536412980230672,pos,2,v -转变,17.514347522426732,pos,2,v -发挥,17.50588173087101,pos,2,v -数卡牌,17.497927832727036,pos,3,n -多惨,17.497927832727036,pos,2,m -平滑,17.497927832727033,pos,2,a -弹力,17.497927832727033,pos,2,n -市场繁荣,17.497927832727033,pos,4,n -稳住,17.497927832727033,pos,2,v -必经之路,17.497927832727033,pos,4,l -省情,17.439034143673467,pos,2,n -环境,17.431226158145094,pos,2,n -情欲,17.427538504835635,pos,2,n -天龙八部,17.427538504835635,pos,4,nz -四驱,17.410464991476694,pos,2,m -前低,17.410464991476694,pos,2,f -公益,17.40253348377219,pos,2,n -善良,17.3985571140676,pos,2,a -例如,17.383579991976728,pos,2,v -资金紧张,17.360424308977095,pos,4,n -这个,17.34465824139873,pos,2,r -快,17.340155217940314,pos,1,a -坚挺,17.328002831284717,pos,2,a -正轨,17.328002831284717,pos,2,b -近岸,17.29721926862255,pos,2,s -不去,17.296293971557382,pos,2,d -复合肥,17.296293971557382,pos,3,n -环境治理,17.27774652444572,pos,4,n -双气,17.275535411390585,pos,2,n -当前,17.243047211037062,pos,2,t -沈阳市,17.236234008150568,pos,3,ns -白,17.212525613864784,pos,1,a -ECM,17.202471949200863,pos,3,eng -直销网,17.175999737839668,pos,3,n -门类,17.150004529306727,pos,2,n -加减,17.14429087811233,pos,2,v -磷灰石,17.14429087811233,pos,3,n -预算,17.129776834598644,pos,2,v -延缓,17.124469437199586,pos,2,v -配套,17.117619483928987,pos,2,a -减少,17.11363686484298,pos,2,v -绝对,17.10547354609909,pos,2,d -清洁,17.091423461673998,pos,2,a -焕发,17.08289033344819,pos,2,v -综合,17.07462260160709,pos,2,vn -上签,17.064968425450928,pos,2,f -令人遗憾,17.064968425450928,pos,4,i -相对,17.043807088215075,pos,2,d -中品,17.038496214089733,pos,2,n -反击,17.02399664439462,pos,2,v -米见园,17.012501005556793,pos,3,nr -get,17.012501005556793,pos,3,eng -山姆,17.012501005556793,pos,2,nrt -公共设施,16.98696591344965,pos,4,n -室内运动,16.97436587667002,pos,4,n -肤色,16.967413116028254,pos,2,n -监测数据,16.942783251472722,pos,4,n -关停,16.916397484681617,pos,2,v -大道,16.91296533200588,pos,2,n -黄沙,16.91296533200588,pos,2,ns -智通,16.912965332005875,pos,2,n -不善,16.912965332005875,pos,2,v -抚平,16.912965332005875,pos,2,ns -最严,16.912965332005875,pos,2,a -最全,16.912965332005875,pos,2,a -自,16.885938313357222,pos,1,p -项目,16.876923447498243,pos,2,n -和谐,16.869130714821964,pos,2,a -早间,16.854071642952306,pos,2,t -文件系统,16.854071642952306,pos,4,l -四舍五入,16.842576004114477,pos,4,i -护目镜,16.834962820004606,pos,3,n -新鲜度,16.834962820004606,pos,3,ns -打赢,16.80761817754644,pos,2,v -木片,16.781720798727626,pos,2,n -技校,16.781720798727626,pos,2,n -收一,16.781720798727623,pos,2,v -长期趋势,16.743040330563566,pos,4,l -下雨,16.743040330563566,pos,2,v -审慎,16.743040330563563,pos,2,a -位子,16.732393086364056,pos,2,n -克制,16.728540760868448,pos,2,v -配电,16.72032025406348,pos,2,vn -比武,16.72032025406348,pos,2,nr -命运,16.697075197042533,pos,2,n -破坏力,16.69057291066943,pos,3,n -局外,16.69057291066943,pos,2,n -临门,16.69057291066943,pos,2,ns -不可或缺,16.690572910669427,pos,4,l -收藏,16.690572910669427,pos,2,v -超出,16.690572910669427,pos,2,v -道德观,16.67010880810971,pos,3,nr -一身正气,16.67010880810971,pos,4,i -挑战,16.66535937411366,pos,2,vn -RAID,16.66142656500991,pos,4,eng -尤其,16.657923161793747,pos,2,d -工业污染,16.6212831078539,pos,4,n -出台,16.598644293842675,pos,2,v -认识,16.589792621755326,pos,2,v -计划,16.580213172088015,pos,2,n -建筑,16.55617347063328,pos,2,n -幸福,16.549584981635434,pos,2,a -归属,16.520647909227115,pos,2,v -慢跑,16.518105714664664,pos,2,v -海宴,16.518105714664664,pos,2,n -覆盖,16.514750003958397,pos,2,v -领导小组,16.507633439656086,pos,4,n -学校,16.498067462966084,pos,2,n -学懂,16.497927832727036,pos,2,v -面向未来,16.497927832727036,pos,4,l -积重难返,16.497927832727036,pos,4,l -讳疾忌医,16.497927832727033,pos,4,i -财源,16.497927832727033,pos,2,n -爱豆,16.497927832727033,pos,2,n -下降,16.48877631993854,pos,2,v -努力,16.482549987367463,pos,2,ad -中性,16.472392740619895,pos,2,n -中期,16.472392740619895,pos,2,t -舆论,16.467777989243622,pos,2,n -有用,16.465506355034655,pos,2,v -负面,16.453388641638956,pos,2,n -主导权,16.439034143673467,pos,3,n -前行,16.439034143673467,pos,2,v -凝冻,16.418200640256302,pos,2,v -仁爱,16.4182006402563,pos,2,nr -穿上,16.4182006402563,pos,2,v -神儿,16.410464991476694,pos,2,n -采取,16.39341245296132,pos,2,v -法律法规,16.387115422219793,pos,4,n -如果说,16.37239695064317,pos,3,c -损失,16.354731770002907,pos,2,n -长,16.352312883729482,pos,1,a -式,16.340101355309848,pos,1,k -唐笑,16.32800283128472,pos,2,nr -孙彬,16.32800283128472,pos,2,nrfg -室温,16.313503261589602,pos,2,n -互联网,16.308637539326774,pos,3,n -责任,16.302086395932193,pos,2,n -减轻负担,16.296293971557382,pos,4,n -硅,16.296293971557382,pos,1,nz -忍让,16.296293971557382,pos,2,v -生息,16.281609925800268,pos,2,n -即刻,16.280697116506364,pos,2,d -珠城,16.280697116506364,pos,2,ns -停接,16.275535411390585,pos,2,v -环境工程,16.260888635426184,pos,4,n -制度,16.258960082824203,pos,2,n -制,16.25131311659294,pos,1,n -有所,16.243700457614167,pos,2,n -采煤,16.234893426893237,pos,2,v -垄断,16.234893426893237,pos,2,v -开发,16.23408375414225,pos,2,v -生产,16.220820499368564,pos,2,vn -过错,16.20842121553205,pos,2,v -优良,16.20224367801756,pos,2,z -社区,16.199793869678743,pos,2,n -优秀,16.192165766872186,pos,2,a -流域,16.188249179281208,pos,2,n -正在,16.18347048725947,pos,2,t -最大,16.18050559836932,pos,2,a -各国,16.160606892411113,pos,2,r -储藏,16.155535635279954,pos,2,n -足量,16.155535635279954,pos,2,n -沥青,16.147430585642898,pos,2,n -承受能力,16.14429087811233,pos,4,n -扩围,16.135357753342326,pos,2,v -安适,16.128694023061314,pos,2,a -蚌埠市,16.128694023061314,pos,3,ns -真情,16.128694023061314,pos,2,n -拖起来,16.128694023061314,pos,3,v -宣传,16.127357302944805,pos,2,vn -适合,16.07547258887344,pos,2,v -本源,16.057355241341053,pos,2,n -大概,16.055937469798394,pos,2,d -行动计划,16.052388975760397,pos,4,n -去垢,16.038496214089733,pos,2,n -持币观望,16.038496214089733,pos,4,n -秩序,16.031319926074378,pos,2,n -常规,16.00607473639736,pos,2,n -抱团,15.995427492197852,pos,2,n -显效,15.995427492197852,pos,2,a -疏离,15.991575166702242,pos,2,v -心,15.989718426655386,pos,1,n -中央,15.987004546352855,pos,2,n -衰老,15.986965913449652,pos,2,a -铬铁,15.974365876670019,pos,2,n -力量,15.972512095037343,pos,2,n -年度,15.963293306452963,pos,2,n -ELF,15.958769021619002,pos,3,eng -清馨,15.958769021619002,pos,2,a -收费,15.944278237164992,pos,2,n -复用,15.933143213943508,pos,2,v -生态系统,15.932186734055128,pos,4,l -领域,15.930764458927989,pos,2,n -滑,15.912965332005877,pos,1,v -足,15.912965332005877,pos,1,a -辅导工作,15.912965332005877,pos,4,n -中等,15.912965332005875,pos,2,b -冲淡,15.912965332005875,pos,2,a -激进,15.912965332005875,pos,2,v -多么,15.907344059296493,pos,2,r -像,15.892790989953532,pos,1,v -第一产业,15.860497912111741,pos,4,l -下笔,15.854071642952308,pos,2,v -中医药大学,15.854071642952308,pos,5,nt -遇见,15.846229626816415,pos,2,v -多肽,15.834962820004606,pos,2,nz -牛市,15.834962820004606,pos,2,n -边框,15.834962820004602,pos,2,n -立意,15.807612331859646,pos,2,d -城市,15.80532534462719,pos,2,ns -法律,15.795598692120214,pos,2,n -成效,15.79115104179197,pos,2,a -端游,15.783682315060911,pos,2,n -泥土,15.781720798727624,pos,2,n -社交,15.77546180825594,pos,2,n -算错,15.766123943676607,pos,2,v -游成,15.766123943676607,pos,2,v -硷,15.766123943676607,pos,1,zg -愿变,15.766123943676607,pos,2,v -人情味,15.766123943676607,pos,3,n -出去,15.764918139966106,pos,2,v -全球,15.760697865072522,pos,2,n -通则,15.743040330563565,pos,2,n -预亏,15.743040330563565,pos,2,v -不断深入,15.720320254063482,pos,4,i -刻薄,15.711331470836223,pos,2,a -粘度,15.711331470836223,pos,2,n -感,15.705464984394824,pos,1,v -踏石,15.69057291066943,pos,2,n -大地,15.69057291066943,pos,2,n -App,15.690572910669427,pos,3,eng -细纹,15.690572910669427,pos,2,n -贵人,15.690572910669427,pos,2,n -弟子,15.674805594811112,pos,2,n -恰如其分,15.670108808109712,pos,4,i -居住,15.666591810010976,pos,2,v -闭嘴,15.661426565009913,pos,2,v -肥大,15.653578703187225,pos,2,a -盈利性,15.653578703187225,pos,3,n -竞争,15.651271507429406,pos,2,vn -此次,15.647707836193108,pos,2,r -外经贸,15.632857412813141,pos,3,n -动态,15.6212831078539,pos,2,n -原则,15.61503782999004,pos,2,n -还是,15.593188313230737,pos,2,c -并行,15.591037237118517,pos,2,v -备受,15.591037237118512,pos,2,v -恶意,15.580213172088014,pos,2,v -整治,15.569066006335616,pos,2,n -举证,15.559328377391173,pos,2,v -贼,15.549217061423887,pos,1,n -宿,15.54373152234016,pos,1,nr -了哏,15.54373152234016,pos,2,v -楼下,15.53856981722438,pos,2,s -成功,15.531899598055348,pos,2,a -形象工程,15.518105714664662,pos,4,n -北塘,15.518105714664662,pos,2,ns -细胞膜,15.51607517943729,pos,3,n -借壳,15.51607517943729,pos,2,n -封号,15.51607517943729,pos,2,n -恢复,15.505251114406791,pos,2,v -党,15.503841143774185,pos,1,n -塌陷,15.497927832727031,pos,2,v -资讯,15.497927832727031,pos,2,n -受,15.474529090841504,pos,1,v -管住,15.472392740619895,pos,2,v -能够,15.46988118942354,pos,2,v -过早地,15.453533713368577,pos,3,l -共存,15.439034143673465,pos,2,v -问诊,15.439034143673465,pos,2,v -联合社,15.439034143673465,pos,3,nt -气候,15.430994129682242,pos,2,n -相比,15.429574885749798,pos,2,v -鲜为人知,15.427538504835635,pos,4,i -放缓,15.425519337198981,pos,2,v -自下而上,15.418200640256298,pos,4,l -认定,15.414153969118619,pos,2,v -卡,15.413870502800744,pos,1,n -私募,15.387503843033382,pos,2,a -消极,15.382450615307098,pos,2,n -人员,15.380457119185104,pos,2,n -上来,15.379241314641963,pos,2,t -愿,15.375308546063078,pos,1,v -兵团,15.374944990892422,pos,2,n -治违,15.364072085992241,pos,2,v -安民,15.364072085992241,pos,2,nr -相互尊重,15.364072085992241,pos,4,l -长期有效,15.364072085992241,pos,4,l -实践,15.354118580702524,pos,2,v -评估,15.34821750426885,pos,2,vn -管教,15.34818071322235,pos,2,nz -长途电话,15.34818071322235,pos,4,l -杂事,15.34818071322235,pos,2,n -物超所值,15.34818071322235,pos,4,i -鸡血,15.32800283128472,pos,2,n -言语,15.32800283128472,pos,2,nr -煽扇,15.32800283128472,pos,2,n -草案,15.32800283128472,pos,2,n -旅游胜地,15.328002831284717,pos,4,n -小区,15.310264561871382,pos,2,n -及时,15.30605387448217,pos,2,c -排查,15.297852987801308,pos,2,vn -烹煮,15.280697116506364,pos,2,v -那么出色,15.280697116506364,pos,4,l -留印,15.275535411390587,pos,2,v -美城,15.275535411390583,pos,2,ns -晒,15.267881838847151,pos,1,v -暨,15.260606694779378,pos,1,nt -战略思想,15.258461898031644,pos,4,n -云程,15.250000319283448,pos,2,n -斯柯达,15.250000319283448,pos,3,nr -一切办法,15.250000319283448,pos,4,l -临床实验,15.250000319283448,pos,4,l -领先水平,15.250000319283448,pos,4,n -尤要,15.250000319283448,pos,2,d -闸门,15.250000319283448,pos,2,n -偏好,15.234893426893237,pos,2,d -盲从,15.216641722337014,pos,2,n -脱皮,15.196758298006467,pos,2,n -国家,15.19524953306577,pos,2,n -末期,15.194147084549929,pos,2,f -南北纬,15.194147084549929,pos,3,nr -提王,15.194147084549929,pos,2,nrt -金主,15.194147084549929,pos,2,n -胃酸,15.175999737839671,pos,2,n -曲线,15.175999737839671,pos,2,n -搭台,15.147430585642898,pos,2,v -民族团结,15.147430585642898,pos,4,n -电代,15.135357753342324,pos,2,n -改增,15.135357753342324,pos,2,v -煤气,15.135357753342324,pos,2,n -飞舞,15.128694023061314,pos,2,n -T4,15.105610409948273,pos,2,eng -剑侠情缘,15.105610409948273,pos,4,n -爆牌,15.105610409948273,pos,2,v -文明史,15.105610409948273,pos,3,nr -度假区,15.105610409948273,pos,3,n -意思,15.105610409948273,pos,2,n -室内,15.10340848647629,pos,2,s -海域,15.102350477467938,pos,2,n -床铺,15.096272545368938,pos,2,n -其实,15.08025323256518,pos,2,d -以,15.076464064288755,pos,1,p -情况,15.072955448695954,pos,2,n -得来,15.064968425450926,pos,2,v -真理,15.064968425450925,pos,2,n -劳工法,15.05649264338358,pos,3,n -近,15.052319700637137,pos,1,a -最,15.041941851871014,pos,1,d -基建,15.038496214089736,pos,2,a -宝们,15.038496214089735,pos,2,n -饱满,15.038496214089735,pos,2,v -内托,15.038496214089735,pos,2,n -灸,15.038496214089735,pos,1,g -力强,15.038496214089735,pos,2,v -调控,15.032546947758549,pos,2,vn -弄通,15.012501005556791,pos,2,v -紧迫感,15.012501005556791,pos,3,n -免,15.012501005556791,pos,1,v -想象,15.006074736397355,pos,2,n -岗位补贴,15.001502006607534,pos,4,n -亚泰,15.001502006607534,pos,2,nz -劝阻,14.998695206031762,pos,2,v -干裂,14.974365876670019,pos,2,v -机油,14.974365876670019,pos,2,n -原厂,14.974365876670019,pos,2,n -坚信,14.965432751900012,pos,2,v -得来不易,14.958769021619002,pos,4,l -度空间,14.958769021619002,pos,3,n -东德,14.958769021619002,pos,2,nr -知心,14.958769021619002,pos,2,v -灵动,14.958769021619002,pos,2,a -烟消云散,14.958769021619002,pos,4,i -颐芯牌,14.933143213943508,pos,3,nz -招致,14.933143213943508,pos,2,v -医嘱,14.933143213943504,pos,2,n -栽培,14.933143213943504,pos,2,vn -利用,14.920893560811935,pos,2,n -混入,14.918989119633647,pos,2,v -迁入,14.912965332005877,pos,2,v -服务大局,14.912965332005877,pos,4,n -买菜,14.912965332005877,pos,2,n -假装,14.912965332005877,pos,2,n -对应,14.912965332005877,pos,2,vn -争当,14.912965332005875,pos,2,v -措施,14.904854029135022,pos,2,n -集中,14.895594482096934,pos,2,v -饼干,14.887430239898741,pos,2,n -共同努力,14.868571212647424,pos,4,l -现状,14.866395605690434,pos,2,n -兵地,14.860497912111741,pos,2,n -现金流,14.857112097272356,pos,3,n -发誓,14.854071642952308,pos,2,v -果肉,14.854071642952308,pos,2,n -量入为出,14.854071642952308,pos,4,i -再次,14.84533666356027,pos,2,d -家乡,14.84257600411448,pos,2,n -作出,14.839612617090076,pos,2,v -不到,14.836064034613358,pos,2,v -受过,14.83323813953514,pos,2,v -浇筑,14.825502490755536,pos,2,v -盆地,14.825502490755536,pos,2,n -蓄电池,14.82236278322497,pos,3,n -珍贵,14.808628672191142,pos,2,a -方针政策,14.807612331859646,pos,4,n -火球,14.783682315060911,pos,2,n -理智,14.781720798727623,pos,2,n -设施,14.774155124216977,pos,2,n -极易,14.773035070861402,pos,2,a -配资,14.766986026188595,pos,2,vn -制下,14.766986026188595,pos,2,v -处理程序,14.766986026188595,pos,4,n -赫本,14.766123943676607,pos,2,ns -国别,14.763218212501192,pos,2,n -监测,14.763077797081273,pos,2,vn -IP,14.743040330563566,pos,2,eng -讽刺,14.743040330563565,pos,2,v -德纳,14.743040330563565,pos,2,ns -同意权,14.743040330563565,pos,3,n -分析方法,14.743040330563565,pos,4,n -燃油,14.743040330563563,pos,2,n -处境,14.743040330563563,pos,2,n -彬,14.743040330563563,pos,1,zg -砼,14.732393086364056,pos,1,n -连续性,14.732393086364056,pos,3,n -海洋环境,14.732314420242643,pos,4,l -挽回,14.716568119202373,pos,2,v -涨价,14.716568119202373,pos,2,n -发泄,14.716568119202373,pos,2,v -只要,14.711444924692472,pos,2,c -噱头,14.690572910669431,pos,2,n -赵强,14.69057291066943,pos,2,nr -三码,14.69057291066943,pos,2,n -充,14.69057291066943,pos,1,v -公猫,14.69057291066943,pos,2,n -小单,14.69057291066943,pos,2,n -桌凳,14.69057291066943,pos,2,n -切实增强,14.69057291066943,pos,4,l -餐桌上,14.690572910669427,pos,3,n -去,14.674034534112614,pos,1,v -领导权,14.670108808109712,pos,3,n -育人,14.653578703187225,pos,2,nrt -简约,14.63167922161586,pos,2,d -周一,14.627563113143628,pos,2,t -奇迹,14.620183582778033,pos,2,n -比如说,14.613063543448511,pos,3,l -列为,14.60553680681363,pos,2,v -路于,14.597061024746283,pos,2,n -诈骗罪,14.597061024746283,pos,3,n -姿势,14.596108226665935,pos,2,n -范围,14.595269510656415,pos,2,n -运动,14.593284895404397,pos,2,vn -放纵,14.591037237118512,pos,2,v -滨州,14.591037237118512,pos,2,ns -夫妻关系,14.583657706752916,pos,4,n -弱冷空气,14.580389992919006,pos,4,n -受尽,14.570573134558797,pos,2,v -堆砌,14.570573134558797,pos,2,v -钢坯,14.559328377391177,pos,2,n -潜能,14.55039525262117,pos,2,v -拍打着,14.54373152234016,pos,3,l -千层底,14.54373152234016,pos,3,l -耐寒,14.54373152234016,pos,2,a -抗病毒,14.54373152234016,pos,3,n -超越,14.543731522340158,pos,2,v -胸腔,14.543731522340156,pos,2,n -打扫,14.53856981722438,pos,2,v -性,14.532494869136992,pos,1,n -缓冲,14.51607517943729,pos,2,v -可燃冰,14.51607517943729,pos,3,n -忍痛,14.51607517943729,pos,2,v -下层,14.51607517943729,pos,2,n -狭幅,14.51607517943729,pos,2,b -市酬,14.51607517943729,pos,2,n -个人财产,14.503951620354803,pos,4,j -执法,14.496421887493527,pos,2,v -长期,14.49268054190058,pos,2,d -朋友圈,14.490274260183599,pos,3,n -人生观,14.485684236972284,pos,3,n -榆林,14.468180489332982,pos,2,ns -下载,14.453533713368579,pos,2,v -停滞,14.453533713368577,pos,2,v -辱骂,14.453533713368577,pos,2,v -靠,14.452542237953262,pos,1,v -工程,14.442735056717122,pos,2,n -產品,14.442645397225842,pos,2,n -抑或,14.427538504835635,pos,2,c -污染物,14.413297549950277,pos,3,n -使得,14.411558991536177,pos,2,v -碎步,14.410464991476694,pos,2,n -受静,14.410464991476694,pos,2,a -制鞋,14.410464991476694,pos,2,n -大船,14.410464991476694,pos,2,n -逆温,14.410464991476694,pos,2,n -致远,14.410464991476694,pos,2,a -事维,14.410464991476694,pos,2,n -翻挖,14.410464991476694,pos,2,v -好极了,14.410464991476694,pos,3,l -各异,14.410464991476694,pos,2,a -更行,14.410464991476694,pos,2,v -明确,14.409832610311716,pos,2,ad -容量,14.406612665981084,pos,2,n -中小城市,14.404415946803889,pos,4,j -两融,14.404415946803889,pos,2,n -企业法人,14.404415946803887,pos,4,n -选用,14.402003412728497,pos,2,v -被窝,14.391728428895108,pos,2,n -巴士,14.373806520897844,pos,2,ns -国内外,14.368644815782066,pos,3,s -长春亚泰,14.364072085992241,pos,4,nz -安于现状,14.364072085992241,pos,4,nr -甲醛,14.352871068371888,pos,2,nz -正午,14.34818071322235,pos,2,t -荒漠,14.331650608299865,pos,2,n -专利申请,14.32800283128472,pos,4,n -建构,14.32800283128472,pos,2,n -画质,14.32800283128472,pos,2,n -邓亲华,14.32800283128472,pos,3,nr -紧抓,14.32800283128472,pos,2,v -每股,14.328002831284719,pos,2,r -减小,14.328002831284717,pos,2,v -一盘棋,14.318604133282472,pos,3,n -结构性,14.30368758505032,pos,3,n -减压,14.29629397155738,pos,2,v -锻炼身体,14.29629397155738,pos,4,n -获悉,14.291564025967832,pos,2,v -非常,14.284672249371473,pos,2,d -顷刻间,14.280697116506364,pos,3,t -任由,14.280697116506364,pos,2,n -异,14.280697116506364,pos,1,zg -滑雪,14.280697116506362,pos,2,nr -齐全,14.275535411390585,pos,2,nr -合作社,14.26910914223115,pos,3,l -勤俭节约,14.26910914223115,pos,4,l -这次,14.26005630974042,pos,2,r -亮度,14.250000319283448,pos,2,n -想尽,14.250000319283448,pos,2,v -鲜明,14.231141292032133,pos,2,a -透水,14.2178199135343,pos,2,v -赢得,14.212525613864784,pos,2,v -民生,14.207066203503771,pos,2,n -接入,14.205146083499187,pos,2,v -皮毛,14.194147084549929,pos,2,n -愈来愈,14.194147084549929,pos,3,d -稳定期,14.194147084549929,pos,3,n -创世,14.194147084549929,pos,2,v -完全,14.187263686757204,pos,2,ad -文明执法,14.182023525467438,pos,4,n -曼省,14.18116144295545,pos,2,ns -滋,14.17961099139205,pos,1,n -使命感,14.175999737839671,pos,3,n -业务,14.17578975360339,pos,2,n -颇,14.169456891972898,pos,1,d -实施,14.148171333336062,pos,2,v -支撑,14.141448361490093,pos,2,v -外语,14.128694023061314,pos,2,n -菜肴,14.128694023061314,pos,2,n -缺乏,14.126459865693228,pos,2,v -工具,14.126368970115069,pos,2,n -立,14.126368970115069,pos,1,v -举世瞩目,14.105610409948273,pos,4,i -鄂州,14.105610409948273,pos,2,ns -塞罕坝,14.105610409948273,pos,3,nrt -教案,14.105610409948271,pos,2,n -文具,14.105610409948271,pos,2,n -黑板,14.105610409948271,pos,2,n -奥斯卡,14.096272545368937,pos,3,nr -夜色,14.096272545368937,pos,2,n -坚持不懈,14.085146307388557,pos,4,i -定期,14.080075317841136,pos,2,vn -肯定,14.07114634946694,pos,2,v -充裕,14.068616202466067,pos,2,a -中枢,14.068616202466067,pos,2,n -人来人往,14.068616202466067,pos,4,i -II,14.068616202466067,pos,2,eng -年后,14.065575748146019,pos,2,m -定时,14.062541688056386,pos,2,d -平台,14.055047431232428,pos,2,n -比较稳定,14.038496214089735,pos,4,n -反之,14.038496214089735,pos,2,c -不冷不热,14.038496214089735,pos,4,l -食品,14.035221082056871,pos,2,n -顺势,14.026252618334988,pos,2,n -申报,14.004601868408564,pos,2,nz -精雕,14.001502006607534,pos,2,n -思维,13.999921555866262,pos,2,n -清洗,13.993660566023662,pos,2,v -喷施,13.974365876670019,pos,2,v -日元,13.974365876670019,pos,2,n -优秀者,13.974365876670019,pos,3,n -叶面,13.974365876670019,pos,2,n -绿泥石,13.974365876670019,pos,3,n -心绞痛,13.974365876670019,pos,3,n -加以,13.974365876670019,pos,2,v -总体,13.97252566319144,pos,2,n -倒下,13.958769021619002,pos,2,v -扬起,13.958769021619002,pos,2,v -世界,13.958520671451986,pos,2,n -目前,13.948451229491688,pos,2,t -生猪,13.940979708175474,pos,2,n -反常,13.93568540850596,pos,2,d -技术,13.93507936038739,pos,2,n -打非,13.931112678716133,pos,2,v -河岸,13.931112678716133,pos,2,s -排放量,13.929453454794443,pos,3,n -HJT91,13.918989119633645,pos,5,eng -依法办事,13.918989119633645,pos,4,l -组共对,13.918989119633645,pos,3,l -所行,13.912965332005879,pos,2,v -均价,13.912965332005879,pos,2,n -座位,13.912965332005875,pos,2,n -回落,13.89856595294526,pos,2,v -回避,13.896363364668746,pos,2,v -因素,13.885349097485891,pos,2,n -火爆,13.883217988611825,pos,2,a -特殊性,13.883217988611824,pos,3,n -没想到,13.86857121264742,pos,3,l -下水道,13.862753886052106,pos,3,n -酸性,13.858888659250082,pos,2,n -回归,13.857682896504688,pos,2,v -质,13.85407164295231,pos,1,ng -艰苦奋斗,13.854071642952308,pos,4,i -隔开,13.854071642952308,pos,2,v -酸甜,13.854071642952308,pos,2,a -原告,13.84883499458616,pos,2,n -亲人,13.84585113614734,pos,2,n -圣母,13.833607540392594,pos,2,n -商户,13.828076434419362,pos,2,n -半程,13.825502490755536,pos,2,n -适用,13.813063357053089,pos,2,v -煤焦油,13.804440875227707,pos,3,n -形容,13.783682315060911,pos,2,n -第二产业,13.78368231506091,pos,4,nz -圆通,13.779109585271087,pos,2,n -共同富裕,13.779109585271087,pos,4,nz -疗程,13.779109585271087,pos,2,n -赤道,13.779109585271083,pos,2,n -深入,13.778807516361594,pos,2,v -仍,13.777041699048594,pos,1,zg -提倡,13.775461808255942,pos,2,v -宜人,13.77546180825594,pos,2,nrt -泡沫,13.77546180825594,pos,2,n -银山,13.774637175457903,pos,2,nr -紧,13.773035070861402,pos,1,a -浓,13.766123943676607,pos,1,a -窃听,13.766123943676607,pos,2,v -水滴,13.766123943676607,pos,2,n -慢慢来,13.766123943676607,pos,3,b -德,13.764573492113207,pos,1,ns -随机应变,13.763218212501192,pos,4,i -坚,13.763218212501192,pos,1,v -危险废物,13.762882358565413,pos,4,n -绿水青山,13.745020694691805,pos,4,nr -如有,13.743040330563565,pos,2,v -误差,13.743040330563565,pos,2,n -健身,13.743040330563565,pos,2,v -恶人,13.743040330563565,pos,2,n -农田水利,13.743040330563565,pos,4,nz -序列,13.743040330563565,pos,2,n -新风,13.743040330563561,pos,2,n -流出,13.716568119202371,pos,2,v -以为,13.711331470836223,pos,2,c -正确,13.706228233916363,pos,2,ad -结构,13.70121584768014,pos,2,n -藏,13.699971608671678,pos,1,j -奶粉,13.696104016642657,pos,2,n -老大,13.69057291066943,pos,2,a -自觉,13.69057291066943,pos,2,d -局外人,13.69057291066943,pos,3,n -林田湖,13.69057291066943,pos,3,nr -年轻,13.69057291066943,pos,2,a -车程,13.69057291066943,pos,2,n -科研成果,13.69057291066943,pos,4,n -业已,13.69057291066943,pos,2,d -共圆,13.69057291066943,pos,2,n -加上,13.690572910669427,pos,2,v -平稳,13.690572910669427,pos,2,a -摊区,13.684523865996622,pos,2,n -苦难,13.670108808109712,pos,2,a -降息,13.66503781856229,pos,2,n -纳米技术,13.66503781856229,pos,4,n -因子,13.659984590836004,pos,2,n -一定,13.657912291297407,pos,2,d -繁衍生息,13.653578703187225,pos,4,i -金山,13.645485021140892,pos,2,nr -倡导,13.63994683759946,pos,2,v -增加,13.62215413001066,pos,2,v -情景,13.610845718198693,pos,2,n -交期,13.609184583828771,pos,2,n -堪称,13.603110069419092,pos,2,v -践行,13.60311006941909,pos,2,v -落实到人,13.597061024746283,pos,4,l -挂账,13.597061024746283,pos,2,v -夜市,13.597061024746283,pos,2,n -绩差股,13.597061024746283,pos,3,n -咖餐,13.597061024746283,pos,2,n -头顶,13.591037237118513,pos,2,n -足够,13.591037237118513,pos,2,v -差不多,13.591037237118513,pos,3,l -观望,13.579064595452438,pos,2,v -秒,13.576872728975157,pos,1,m -大气污染,13.567403340843509,pos,4,i -机,13.564565025757322,pos,1,n -子孙后代,13.553069386919494,pos,4,nr -人缘,13.54373152234016,pos,2,n -诚实,13.543731522340156,pos,2,a -环保部门,13.532746433252246,pos,4,n -格外,13.520647909227115,pos,2,d -度假,13.520647909227115,pos,2,v -小康社会,13.517380195393207,pos,4,n -成为,13.506180934835344,pos,2,v -置身于,13.5001838066674,pos,3,l -路子,13.5001838066674,pos,2,n -亏损,13.489283738317782,pos,2,vn -逐步,13.47767625767252,pos,2,d -质控,13.471530142662422,pos,2,j -预算法,13.471530142662422,pos,3,n -理性,13.461754220173548,pos,2,n -养育,13.453533713368577,pos,2,vn -首创,13.453533713368577,pos,2,n -平缓,13.453533713368577,pos,2,a -牛,13.453533713368577,pos,1,n -透明,13.444125337558278,pos,2,v -经理,13.442645397225844,pos,2,n -揉,13.442645397225842,pos,1,v -交给,13.439034143673465,pos,2,v -应该,13.4244329663239,pos,2,v -兑付,13.410464991476694,pos,2,v -马拉松赛,13.410464991476694,pos,4,nr -监督,13.408539055488855,pos,2,vn -做工,13.404415946803889,pos,2,v -土地规划,13.404415946803889,pos,4,l -轰轰烈烈,13.404415946803889,pos,4,i -实施规划,13.404415946803889,pos,4,n -人才,13.40361433372369,pos,2,n -攻坚战,13.398801236473037,pos,3,i -土豆,13.394640024315011,pos,2,n -冰冻期,13.389403375948861,pos,3,t -心性,13.389403375948861,pos,2,n -平等,13.382556512840148,pos,2,a -规模化,13.380140454619895,pos,3,n -时分,13.373806520897844,pos,2,n -妖精,13.373806520897844,pos,2,n -八方,13.373806520897844,pos,2,n -微澜,13.364072085992241,pos,2,n -持续性,13.364072085992241,pos,3,n -省界,13.364072085992241,pos,2,n -传统节日,13.34818071322235,pos,4,n -证据确凿,13.34818071322235,pos,4,n -青年人,13.34818071322235,pos,3,n -打爆,13.34818071322235,pos,2,v -所交,13.34818071322235,pos,2,c -宣贯,13.34818071322235,pos,2,nr -必学,13.34818071322235,pos,2,n -遵从,13.34818071322235,pos,2,v -责备,13.34818071322235,pos,2,n -临界,13.34818071322235,pos,2,b -避光,13.34818071322235,pos,2,v -一呼百应,13.34818071322235,pos,4,l -冲,13.340075663585296,pos,1,v -顶格,13.334026618912487,pos,2,n -控尘,13.334026618912487,pos,2,v -工商企业,13.328002831284719,pos,4,j -品类,13.324000900727224,pos,2,n -荷尔蒙,13.321339101003709,pos,3,nrt -归属于,13.302467739177585,pos,3,l -太,13.281591870749018,pos,1,d -静静的,13.280697116506364,pos,3,z -共同体,13.275535411390585,pos,3,n -新闻出版,13.275132929858922,pos,4,n -自愈,13.26910914223115,pos,2,d -解决问题,13.254224905145172,pos,4,n -陈列,13.250000319283448,pos,2,nr -领导者,13.250000319283448,pos,3,n -抗氧化,13.250000319283448,pos,3,nz -空污,13.250000319283448,pos,2,n -族,13.250000319283448,pos,1,ng -景林,13.250000319283448,pos,2,nr -上涨,13.243316005978231,pos,2,v -瑜伽,13.237400282503815,pos,2,n -牛奶,13.231141292032131,pos,2,n -帽子,13.231141292032131,pos,2,n -确定性,13.23114129203213,pos,3,n -火炉,13.221803427452798,pos,2,n -助推,13.212525613864782,pos,2,vn -科目,13.212525613864782,pos,2,n -退牧,13.194147084549929,pos,2,v -防沙,13.194147084549929,pos,2,ns -秦巴,13.194147084549929,pos,2,nr -价格上涨,13.194147084549929,pos,4,l -治沙,13.194147084549929,pos,2,ns -持久,13.194147084549929,pos,2,a -发展性,13.194147084549929,pos,3,l -AOE,13.194147084549929,pos,3,eng -宏观政策,13.194147084549929,pos,4,n -递增,13.194147084549929,pos,2,v -养生,13.190499307534784,pos,2,v -克服,13.18586998186373,pos,2,v -ETF,13.182023525467441,pos,3,eng -公共部门,13.182023525467441,pos,4,n -基础教育,13.182023525467441,pos,4,l -击沉,13.182023525467438,pos,2,v -施加,13.182023525467438,pos,2,v -原因,13.180106444096316,pos,2,n -成分,13.162537478033109,pos,2,n -循环系统,13.128694023061314,pos,4,l -糟糕,13.118549465655772,pos,2,a -西游,13.105610409948273,pos,2,f -公用,13.105610409948271,pos,2,n -搞定,13.105610409948271,pos,2,v -家常菜,13.105610409948271,pos,3,n -立体化,13.105610409948271,pos,3,vn -压,13.105610409948271,pos,1,v -吉运,13.105610409948271,pos,2,nz -绝育,13.105610409948271,pos,2,v -户外,13.076464064288757,pos,2,s -痛苦,13.071663078024935,pos,2,an -室内装饰,13.05649264338358,pos,4,n -保密,13.05649264338358,pos,2,n -不受,13.04214399110488,pos,2,d -专项资金,13.031953368222963,pos,4,n -份额,13.027607897947002,pos,2,n -就行了,13.01357483890811,pos,3,l -强制措施,13.012098524025125,pos,4,n -图景,13.006074736397359,pos,2,n -弱,13.006074736397359,pos,1,a -沉默,13.006074736397359,pos,2,a -业态,13.006074736397357,pos,2,n -极小,13.001502006607534,pos,2,d -突变,13.001502006607534,pos,2,v -多彩,13.001502006607534,pos,2,b -太多,13.001502006607534,pos,2,d -转移,12.999921555866262,pos,2,v -节日期间,12.975572648000014,pos,4,l -针对性,12.974365876670019,pos,3,n -减缓,12.974365876670019,pos,2,v -随身带,12.974365876670019,pos,3,n -保暖,12.974365876670019,pos,2,v -血压,12.974365876670019,pos,2,n -就让,12.969669089968619,pos,2,v -营,12.965432751900014,pos,1,n -废弃,12.965432751900014,pos,2,v -审批,12.961133110738524,pos,2,v -煦,12.958769021619002,pos,1,nr -寒意,12.958769021619002,pos,2,n -海风,12.958769021619002,pos,2,n -LYCRA,12.958769021619002,pos,5,eng -水如,12.958769021619002,pos,2,l -光照,12.958769021619002,pos,2,n -志向,12.958769021619002,pos,2,n -下半身,12.958769021619002,pos,3,n -善举,12.958769021619002,pos,2,v -健康活泼,12.958769021619002,pos,4,n -妇儿,12.958769021619002,pos,2,n -窗帘,12.958769021619002,pos,2,n -发生意外,12.958769021619002,pos,4,l -后半段,12.958769021619002,pos,3,t -放射出,12.958769021619002,pos,3,v -Daliah,12.958769021619002,pos,6,eng -玩累,12.958769021619002,pos,2,v -归零,12.958769021619002,pos,2,v -纪念日,12.958769021619002,pos,3,n -山洼,12.958769021619002,pos,2,ns -多得多,12.958769021619002,pos,3,d -幼苗,12.958769021619002,pos,2,n -暖洋洋,12.958769021619002,pos,3,z -有钱出钱,12.958769021619002,pos,4,n -雪化,12.958769021619002,pos,2,nz -西双版纳傣族自治州,12.958769021619002,pos,9,ns -扣子,12.958769021619002,pos,2,n -anniversary,12.958769021619002,pos,11,eng -畔,12.958769021619002,pos,1,ng -依恋,12.958769021619002,pos,2,v -煤火,12.958769021619002,pos,2,n -不加区分,12.958769021619002,pos,4,n -莱卡,12.958769021619002,pos,2,nrt -嗖嗖,12.958769021619002,pos,2,o -悦达起亚,12.958769021619,pos,4,nz -异常,12.953607316503222,pos,2,d -家庭环境,12.951033372839396,pos,4,l -定义,12.933143213943506,pos,2,n -教师队伍,12.931112678716133,pos,4,n -公平正义,12.931112678716133,pos,4,n -宣妍,12.928072224396086,pos,2,nr -性价比,12.925038164306452,pos,3,n -准入条件,12.918989119633645,pos,4,n -合法财产,12.918989119633645,pos,4,l -陆海,12.918989119633645,pos,2,ns -威慑,12.918989119633645,pos,2,vn -锻炼,12.915472187616452,pos,2,v -阿什,12.912965332005877,pos,2,nrt -青山绿水,12.883217988611825,pos,4,ns -发展观,12.883217988611824,pos,3,l -意义,12.882815488522462,pos,2,n -水分,12.880766509617729,pos,2,n -畜禽,12.880543854313498,pos,2,n -反馈,12.871828159714584,pos,2,v -留住,12.860497912111741,pos,2,v -留言,12.857682896504688,pos,2,v -在职,12.854071642952308,pos,2,v -数据中心,12.854071642952308,pos,4,n -积极进取,12.854071642952308,pos,4,l -气管炎,12.854071642952308,pos,3,n -住宅,12.849955534480078,pos,2,n -自信,12.849270656688487,pos,2,v -专业,12.845737111242212,pos,2,n -系统化,12.842576004114479,pos,3,n -通风,12.84044372432702,pos,2,n -坏事,12.836862352920084,pos,2,n -宽,12.82550249075554,pos,1,a -不求,12.825502490755536,pos,2,d -物力,12.819453446082731,pos,2,n -完备,12.81610379275329,pos,2,v -第三产业,12.816103792753289,pos,4,nz -超市,12.807929861307587,pos,2,v -女孩子,12.806765928173952,pos,3,n -金色,12.806765928173952,pos,2,n -小时,12.794064366497015,pos,2,n -柔软,12.78884402017669,pos,2,a -救,12.783682315060911,pos,1,v -物业,12.78368231506091,pos,2,n -主动,12.780974367056574,pos,2,b -盐酸,12.779109585271087,pos,2,n -湿滑,12.779109585271083,pos,2,v -任务,12.774138545329038,pos,2,n -面试,12.773413979607083,pos,2,v -嗅觉,12.763218212501192,pos,2,n -诊断,12.763218212501192,pos,2,v -环境优美,12.751973455333571,pos,4,n -预盈,12.743040330563565,pos,2,n -劳基法,12.743040330563565,pos,3,n -逻辑,12.743040330563565,pos,2,n -耐心,12.738594425532373,pos,2,a -渐趋,12.716568119202373,pos,2,d -内容,12.713791514510843,pos,2,n -天性,12.711331470836225,pos,2,n -张庆伟,12.69057291066943,pos,3,nr -亲子关系,12.69057291066943,pos,4,l -样样,12.69057291066943,pos,2,n -万象,12.69057291066943,pos,2,n -理清,12.69057291066943,pos,2,v -策略,12.69057291066943,pos,2,n -殷切期望,12.69057291066943,pos,4,nr -含有,12.687998967005601,pos,2,v -汽车配件,12.66503781856229,pos,4,n -老牌,12.66503781856229,pos,2,n -农行,12.66503781856229,pos,2,j -即使,12.662111524787699,pos,2,c -无所事事,12.63684092673164,pos,4,i -哥,12.62756311314363,pos,1,n -颁布,12.62756311314363,pos,2,v -产权,12.623533236107475,pos,2,n -自律,12.623458714810893,pos,2,n -所有,12.617628731340956,pos,2,b -一条街,12.611215119056146,pos,3,n -BUFF,12.609184583828771,pos,4,eng -幽静,12.603110069419088,pos,2,a -征程,12.603110069419088,pos,2,n -灭火剂,12.597061024746283,pos,3,n -河北省委,12.597061024746283,pos,4,nt -所得税,12.597061024746283,pos,3,n -同属,12.597061024746283,pos,2,n -仪,12.597061024746283,pos,1,ng -信访工作,12.597061024746283,pos,4,n -保全,12.597061024746283,pos,2,v -非法经营,12.597061024746283,pos,4,l -洒漏,12.597061024746283,pos,2,v -延时,12.597061024746283,pos,2,ns -陆域,12.597061024746283,pos,2,n -工作进度,12.597061024746283,pos,4,n -干到底,12.597061024746283,pos,3,l -险情,12.597061024746283,pos,2,n -最高点,12.597061024746283,pos,3,n -线缆,12.591037237118515,pos,2,n -鄱阳湖,12.591037237118515,pos,3,ns -得以,12.591037237118515,pos,2,v -蒸,12.591037237118513,pos,1,v -TVOC,12.57265870780366,pos,4,eng -我,12.572482674924409,pos,1,r -抓住,12.57192841417081,pos,2,v -向上,12.562468084921743,pos,2,d -中要,12.54373152234016,pos,2,b -爱惜,12.54373152234016,pos,2,v -逐年,12.527534294812355,pos,2,d -深层次,12.520647909227117,pos,3,b -再创,12.520647909227115,pos,2,v -生于,12.520647909227115,pos,2,v -诸如,12.520647909227115,pos,2,nr -即,12.520647909227115,pos,1,v -雪花,12.51819643023302,pos,2,n -湿润,12.511310044647779,pos,2,n -更快,12.5001838066674,pos,2,d -方针,12.493707366408836,pos,2,n -延续,12.48446557292047,pos,2,v -投入,12.482534987298475,pos,2,v -信息,12.475809363205817,pos,2,n -伤,12.47334219444876,pos,1,v -组合,12.471865536140838,pos,2,v -充电,12.471865536140838,pos,2,v -下大力气,12.468180489332982,pos,4,l -呜呜,12.457181490383725,pos,2,l -差异,12.453533713368579,pos,2,n -社会主义,12.453533713368579,pos,4,n -鸭,12.453533713368579,pos,1,ns -激烈,12.453533713368577,pos,2,a -亲密,12.453533713368577,pos,2,a -倒排,12.445057931301234,pos,2,v -创优,12.442645397225842,pos,2,j -车间,12.442645397225842,pos,2,n -一如既往,12.442645397225842,pos,4,i -保健,12.442645397225842,pos,2,nr -政绩,12.44129011761383,pos,2,n -财经,12.437231901039477,pos,2,n -区分,12.427136023303971,pos,2,n -每日,12.423786369974525,pos,2,r -逗比,12.410464991476694,pos,2,v -支柱产业,12.410464991476694,pos,4,n -稳妥,12.404415946803889,pos,2,a -确信,12.389403375948861,pos,2,v -校区,12.389403375948861,pos,2,n -欧元,12.389403375948861,pos,2,nz -股份,12.3859228072139,pos,2,n -正是,12.38314438547718,pos,2,d -开窗,12.382946131383026,pos,2,n -不快,12.373806520897848,pos,2,d -环卫工,12.373806520897846,pos,3,n -天际线,12.373806520897844,pos,3,nz -如期而至,12.373806520897844,pos,4,i -紧紧,12.368644815782067,pos,2,d -小主,12.368644815782067,pos,2,n -相得益彰,12.368644815782067,pos,4,v -国际贸易,12.364072085992241,pos,4,nz -生态旅游,12.364072085992241,pos,4,n -房价,12.358376480328237,pos,2,n -握笔,12.34818071322235,pos,2,v -一方水土养一方人,12.34818071322235,pos,8,l -振臂高呼,12.34818071322235,pos,4,i -上油,12.34818071322235,pos,2,f -三观,12.348180713222348,pos,2,nz -较高,12.336935956054727,pos,2,d -严防,12.33402661891249,pos,2,v -场外,12.334026618912487,pos,2,s -更是,12.333020906051345,pos,2,d -具有,12.33054226105826,pos,2,v -频次,12.330509686895294,pos,2,d -迷茫,12.328002831284723,pos,2,z -疑惑,12.32800283128472,pos,2,v -春天,12.314912831844277,pos,2,t -间,12.295353082409042,pos,1,f -围巾,12.280697116506364,pos,2,n -无缝,12.275535411390587,pos,2,n -突击,12.275132929858922,pos,2,vn -繁殖,12.272149596551202,pos,2,v -东风,12.26910914223115,pos,2,n -古镇,12.26071787197201,pos,2,ns -获得,12.25858320088635,pos,2,v -陕西,12.257613503393323,pos,2,ns -无限,12.257613503393323,pos,2,v -岁时,12.250000319283448,pos,2,t -降准,12.250000319283448,pos,2,v -单位,12.245034053702796,pos,2,n -严厉,12.240917214521009,pos,2,ad -不利,12.24053999003438,pos,2,a -步行,12.231141292032133,pos,2,n -产量,12.231141292032133,pos,2,n -呦,12.231141292032133,pos,1,e -年份,12.231141292032133,pos,2,n -董,12.221803427452798,pos,1,nr -提,12.2178199135343,pos,1,v -未能,12.205146083499187,pos,2,v -重点,12.204324219182345,pos,2,n -技术难题,12.194147084549929,pos,4,n -西藏高原,12.194147084549929,pos,4,ns -调节作用,12.194147084549929,pos,4,n -变量,12.194147084549929,pos,2,vn -水土,12.194147084549929,pos,2,n -红火,12.194147084549929,pos,2,n -脉冲星,12.194147084549929,pos,3,n -吸附剂,12.194147084549929,pos,3,nz -光年,12.194147084549929,pos,2,n -招用,12.194147084549929,pos,2,v -脱颖而出,12.194147084549929,pos,4,i -河势,12.194147084549929,pos,2,n -ESP,12.194147084549929,pos,3,eng -美国进口,12.194147084549929,pos,4,ns -科技人才,12.194147084549929,pos,4,n -长远规划,12.194147084549929,pos,4,l -中青年,12.194147084549929,pos,3,j -和永宁,12.194147084549929,pos,3,nr -草场,12.194147084549929,pos,2,n -眼睛,12.191106630229879,pos,2,n -柴油机,12.182023525467441,pos,3,n -严实,12.182023525467441,pos,2,ad -亲生,12.182023525467441,pos,2,n -压茬,12.182023525467441,pos,2,n -政治责任,12.175999737839671,pos,4,n -摄影师,12.170273126812713,pos,3,n -补齐,12.154520010429218,pos,2,v -狗,12.151414099561398,pos,1,n -富有,12.151414099561398,pos,2,v -留恋,12.151414099561396,pos,2,v -焚烧,12.133113924986493,pos,2,v -通胀,12.131605618481215,pos,2,j -阳台,12.128694023061314,pos,2,n -日子,12.128330486448355,pos,2,n -议案,12.118549465655773,pos,2,n -作用,12.118284560653796,pos,2,v -废物,12.110427426246048,pos,2,n -新,12.10837671192937,pos,1,a -简陋,12.105610409948273,pos,2,a -刘女士,12.105610409948273,pos,3,nr -洗衣机,12.105610409948273,pos,3,n -见证,12.105610409948273,pos,2,v -草,12.105610409948273,pos,1,n -拉长,12.105610409948271,pos,2,v -重大意义,12.105610409948271,pos,4,ns -种树,12.105610409948271,pos,2,n -讲究,12.100253199778765,pos,2,vn -海沧,12.088536896589332,pos,2,ns -积聚,12.088536896589332,pos,2,v -电子商务,12.088536896589332,pos,4,n -保有,12.080075317841136,pos,2,v -金融业,12.080075317841136,pos,3,n -一段话,12.080075317841136,pos,3,l -防治,12.070272400440519,pos,2,v -峰值,12.0674752810615,pos,2,n -充分发挥,12.064968425450926,pos,4,n -朗,12.056643560799994,pos,1,nr -副本,12.05649264338358,pos,2,n -快攻,12.054984336878306,pos,2,vn -很小,12.054984336878302,pos,2,a -好奇,12.051878426010482,pos,2,a -过度,12.046716720894704,pos,2,n -难免,12.046716720894702,pos,2,d -事半功倍,12.026252618334988,pos,4,l -优异,12.026252618334988,pos,2,a -车友,12.026252618334988,pos,2,n -注定,12.026252618334986,pos,2,v -放松,12.021181628787566,pos,2,v -空间布局,12.012098524025125,pos,4,n -爱,11.998730136040095,pos,1,v -权益,11.990133192528338,pos,2,n -环境监测,11.977778672828927,pos,4,n -Hardeep,11.974365876670019,pos,7,eng -独立性,11.974365876670019,pos,3,n -关税,11.974365876670019,pos,2,n -时长,11.974365876670019,pos,2,n -便利,11.974365876670019,pos,2,a -准入,11.966294834412002,pos,2,v -价值观,11.958769021619004,pos,3,n -善待,11.958769021619004,pos,2,v -沁入,11.958769021619002,pos,2,v -黄姜,11.958769021619002,pos,2,nr -心田,11.958769021619002,pos,2,n -重见天日,11.958769021619002,pos,4,i -蜗居,11.958769021619002,pos,2,n -久违,11.958769021619002,pos,2,v -不厌其烦,11.958769021619002,pos,4,i -守信,11.958769021619002,pos,2,v -咖啡屋,11.958769021619002,pos,3,n -携,11.958769021619002,pos,1,v -热气腾腾,11.958769021619002,pos,4,z -相当于,11.958769021619,pos,3,v -她们,11.951033372839397,pos,2,r -支出,11.946219571106342,pos,2,v -太晚,11.93568540850596,pos,2,nz -终点,11.933143213943508,pos,2,d -变差,11.931112678716135,pos,2,n -铺场,11.931112678716133,pos,2,n -对抗,11.928072224396084,pos,2,v -研发,11.921044938944416,pos,2,j -民,11.918989119633647,pos,1,ng -圆满完成,11.918989119633645,pos,4,l -产污,11.918989119633645,pos,2,n -堆场,11.918989119633645,pos,2,n -咖啡,11.914374902260548,pos,2,n -可能性,11.912965332005875,pos,3,n -山水,11.906301601724866,pos,2,ns -床,11.894638684199284,pos,1,n -灵魂,11.894638684199284,pos,2,n -塑造,11.888749094585052,pos,2,v -阴险,11.883217988611824,pos,2,n -基础科学,11.872218989662567,pos,4,l -变异,11.872218989662567,pos,2,n -加速度,11.872218989662567,pos,3,n -超级,11.868571212647424,pos,2,b -小女孩,11.868571212647423,pos,3,n -添加剂,11.868571212647423,pos,3,n -区块,11.85407164295231,pos,2,n -预防,11.85407164295231,pos,2,v -久久,11.843291804199067,pos,2,d -辽宁省,11.836862352920082,pos,3,ns -动力,11.825502490755538,pos,2,n -三者,11.825502490755536,pos,2,n -迷人,11.821265497869067,pos,2,n -总经理,11.817040912007341,pos,3,n -肥,11.804440875227707,pos,1,n -站位,11.797488114585942,pos,2,n -开办,11.795597708059654,pos,2,v -雨山,11.790568700646151,pos,2,nr -增量,11.790568700646151,pos,2,n -昂贵,11.789706102688678,pos,2,a -场内,11.789706102688678,pos,2,s -梦幻,11.783682315060911,pos,2,n -沙化,11.779109585271087,pos,2,n -带有,11.772732015500367,pos,2,v -年纪,11.772732015500367,pos,2,t -发送,11.763218212501192,pos,2,v -别怕,11.763218212501192,pos,2,c -写字,11.763218212501192,pos,2,n -纷扰,11.763218212501192,pos,2,v -慧眼,11.763218212501192,pos,2,nr -交通,11.759835573106542,pos,2,n -舒适,11.758236034246293,pos,2,a -污,11.758147222953772,pos,1,vn -进而,11.751973455333573,pos,2,c -夏季,11.749734983137571,pos,2,t -行道树,11.749064118191333,pos,3,n -财政,11.72864095150295,pos,2,n -生产总值,11.716568119202373,pos,4,n -凡事,11.716568119202373,pos,2,n -坚定信心,11.708720257379685,pos,4,i -冒,11.704324523447625,pos,1,v -手套,11.695734615785208,pos,2,n -洁净,11.692721377248006,pos,2,z -木业,11.69057291066943,pos,2,n -明码标价,11.69057291066943,pos,4,n -商铺,11.69057291066943,pos,2,n -持有,11.682315809994128,pos,2,v -故,11.679573911720173,pos,1,n -送到,11.678661102426268,pos,2,v -想要,11.675091483226215,pos,2,v -抗霾,11.670108808109712,pos,2,n -继续加强,11.665037818562292,pos,4,n -解决,11.659029728380668,pos,2,v -沙漠,11.646178791310977,pos,2,n -水军,11.646178791310975,pos,2,n -责,11.638881200440911,pos,1,n -吃力,11.63684092673164,pos,2,n -窗外,11.63684092673164,pos,2,s -相伴,11.63684092673164,pos,2,v -各级,11.625499064411855,pos,2,r -憧憬,11.611215119056142,pos,2,v -高校,11.609184583828773,pos,2,n -妨碍,11.609184583828771,pos,2,v -骄傲,11.609184583828771,pos,2,a -金毛,11.609184583828771,pos,2,n -长不大,11.609184583828771,pos,3,l -这,11.604725672699196,pos,1,r -ing,11.603110069419088,pos,3,eng -应予,11.597061024746283,pos,2,v -代建,11.597061024746283,pos,2,nr -音像,11.597061024746283,pos,2,n -领证,11.597061024746283,pos,2,n -工序,11.597061024746283,pos,2,n -染剂,11.597061024746283,pos,2,n -技术标准,11.597061024746283,pos,4,n -节能降耗,11.597061024746283,pos,4,n -事件处理,11.597061024746283,pos,4,l -砖瓦窑,11.597061024746283,pos,3,nz -建筑施工,11.597061024746283,pos,4,n -确保安全,11.597061024746283,pos,4,ns -预决算,11.597061024746283,pos,3,v -财政赤字,11.597061024746283,pos,4,n -相悖,11.597061024746283,pos,2,v -严谨,11.597061024746283,pos,2,a -界限,11.597061024746283,pos,2,n -船只,11.597061024746283,pos,2,n -层层落实,11.597061024746283,pos,4,z -盗版,11.597061024746283,pos,2,v -刨花板,11.597061024746283,pos,3,n -全程,11.593293211058882,pos,2,n -思考,11.591037237118513,pos,2,v -终于,11.591037237118512,pos,2,d -库存,11.58204845389126,pos,2,n -夜晚,11.580257398365271,pos,2,t -中药,11.579064595452438,pos,2,n -下去,11.579064595452436,pos,2,t -反而,11.556391094311623,pos,2,c -机械,11.555413327387791,pos,2,n -优先,11.553215771683547,pos,2,vn -生物科技,11.549560601142355,pos,4,n -首选,11.54373152234016,pos,2,v -二次污染,11.542070387970238,pos,4,nz -穿衣,11.532143548064946,pos,2,n -王子,11.520647909227117,pos,2,nr -两院,11.51607517943729,pos,2,j -成人,11.51493425803272,pos,2,n -充足,11.499337402981704,pos,2,a -出来,11.498679432140172,pos,2,v -天气,11.487127051773768,pos,2,n -金融机构,11.477781259615254,pos,4,n -发声,11.47334219444876,pos,2,v -出货,11.471865536140838,pos,2,v -根本,11.46777798924362,pos,2,a -持证,11.459557500996349,pos,2,v -趋严,11.457181490383725,pos,2,n -此,11.4565175718074,pos,1,zg -暴力,11.45353371336858,pos,2,n -减弱,11.453533713368577,pos,2,v -并非,11.442645397225846,pos,2,c -销售额,11.442645397225844,pos,3,n -尽可能,11.438312976429808,pos,3,d -青春,11.427998621261443,pos,2,ns -公司业绩,11.421112235676203,pos,4,n -雷射,11.416539505886378,pos,2,nr -高新,11.416539505886378,pos,2,d -四川省,11.410464991476696,pos,3,ns -外贸,11.410464991476694,pos,2,n -事业性,11.404415946803889,pos,3,n -基本农田,11.404415946803889,pos,4,n -拍,11.40283724347508,pos,1,v -公告,11.399085929346203,pos,2,n -夜间,11.390565434038988,pos,2,t -嘈杂,11.389403375948863,pos,2,a -胜诉,11.389403375948861,pos,2,vn -钢材,11.389403375948861,pos,2,n -上游,11.386792162492325,pos,2,f -低谷,11.386792162492323,pos,2,n -建房,11.374668603409836,pos,2,n -严重威胁,11.373806520897848,pos,4,n -好玩,11.373806520897848,pos,2,v -没吃过,11.373806520897844,pos,3,l -泡菜,11.373806520897844,pos,2,n -最南端,11.373806520897844,pos,3,f -煤化,11.373806520897844,pos,2,n -夸奖,11.373806520897844,pos,2,v -勐腊,11.373806520897844,pos,2,ns -相爱,11.373806520897844,pos,2,v -轻触,11.373806520897844,pos,2,v -手指,11.373806520897844,pos,2,n -如何,11.371942685262914,pos,2,r -工作思路,11.368644815782067,pos,4,n -根基,11.368644815782067,pos,2,n -城事,11.34818071322235,pos,2,n -敏锐,11.34818071322235,pos,2,a -卡组,11.346150177994978,pos,2,n -开放型,11.346150177994978,pos,3,nz -宝宝,11.344303165588807,pos,2,nr -排污,11.34389625461502,pos,2,v -增长速度,11.338056495948642,pos,4,n -泰州市,11.334026618912489,pos,3,ns -致癌物,11.334026618912487,pos,3,n -网式,11.334026618912487,pos,2,n -要件,11.334026618912487,pos,2,n -永远,11.322289180090326,pos,2,d -氮,11.31967796663379,pos,1,n -滤芯,11.319262981720561,pos,2,nz -扬尘,11.316953105553548,pos,2,vg -五大,11.312061287415698,pos,2,j -契约,11.307554407551299,pos,2,ns -跨境,11.294987774056759,pos,2,n -志愿者,11.288917623311331,pos,3,n -不利于,11.28725648894141,pos,3,v -国务院令,11.283608711926268,pos,4,n -央行,11.283608711926266,pos,2,j -热带雨林,11.280697116506364,pos,4,n -税款,11.277791385330952,pos,2,n -严控,11.275132929858922,pos,2,v -用途,11.275132929858922,pos,2,n -今年冬天,11.273926158528926,pos,4,t -养殖,11.269109142231152,pos,2,vn -模型,11.26071787197201,pos,2,n -院子,11.25832930347791,pos,2,n -立足,11.257613503393323,pos,2,d -人与自然,11.257613503393323,pos,4,i -电线电缆,11.257613503393321,pos,4,n -纷纷,11.25000031928345,pos,2,d -个股,11.25000031928345,pos,2,n -理财,11.250000319283448,pos,2,v -保护环境,11.245262400276783,pos,4,n -全域,11.243424070131582,pos,2,n -兴奋,11.242561987619593,pos,2,v -城市群,11.240539990034382,pos,3,n -版本,11.239950774163054,pos,2,n -狠抓,11.231141292032133,pos,2,v -明亮,11.221803427452796,pos,2,a -规范性,11.212525613864784,pos,3,n -销售,11.206703419199355,pos,2,vn -雨林,11.194147084549929,pos,2,nr -压线,11.194147084549929,pos,2,n -速递,11.194147084549929,pos,2,v -眼红,11.194147084549929,pos,2,n -调入,11.194147084549929,pos,2,v -日趋,11.194147084549929,pos,2,d -系统安全,11.194147084549929,pos,4,nz -FDA,11.194147084549929,pos,3,eng -逸,11.194147084549929,pos,1,zg -制动,11.194147084549929,pos,2,v -抚育,11.194147084549929,pos,2,v -南四湖,11.194147084549929,pos,3,nr -治疆,11.194147084549929,pos,2,ns -平衡点,11.194147084549929,pos,3,n -才华,11.194147084549929,pos,2,nr -手,11.19110663022988,pos,1,n -配套改革,11.182023525467441,pos,4,n -当班,11.182023525467441,pos,2,v -打死,11.178255711780038,pos,2,v -题材,11.153631924811215,pos,2,n -抗菌,11.151414099561398,pos,2,n -知识产权,11.146895302063838,pos,4,n -量,11.141353595032777,pos,1,n -好吃,11.135984058991792,pos,2,v -慢慢,11.131605618481215,pos,2,d -线,11.12898491832208,pos,1,n -术,11.12375775665853,pos,1,v -高标准,11.11383091675324,pos,3,n -论证,11.111634197576041,pos,2,n -严密,11.11163419757604,pos,2,a -比高,11.11163419757604,pos,2,v -吸烟,11.111141515921501,pos,2,v -风暴,11.110772115064051,pos,2,n -水土流失,11.10668424329959,pos,4,i -运作,11.105610409948273,pos,2,vn -全面完成,11.105610409948273,pos,4,l -L,11.105610409948273,pos,1,x -生日快乐,11.105610409948273,pos,4,l -疫苗,11.105610409948273,pos,2,n -指示精神,11.105610409948271,pos,4,n -人口,11.099896758753877,pos,2,n -停工,11.0945606842171,pos,2,v -责问,11.0945606842171,pos,2,n -才,11.090762903797001,pos,1,d -潜力,11.08853689658933,pos,2,n -着力,11.084690416780516,pos,2,n -相应,11.080075317841136,pos,2,v -提起,11.078201687654559,pos,2,v -围,11.077053965865897,pos,1,v -值班,11.066546308047503,pos,2,v -冬日,11.047305696220658,pos,2,t -较强,11.038496214089735,pos,2,d -汉斯,11.024222083107617,pos,2,nz -依法,11.023649386696563,pos,2,n -四中全会,11.012098524025125,pos,4,j -孤独,11.007678622099949,pos,2,a -水污染,10.994102094731282,pos,3,b -红线,10.99133996385833,pos,2,n -从严治党,10.990133192528337,pos,4,nr -普遍存在,10.990133192528337,pos,4,i -不仅,10.987985532050002,pos,2,c -施行,10.980539644290221,pos,2,v -正式,10.979274677053644,pos,2,ad -收敛,10.974365876670019,pos,2,v -大步,10.971754663213481,pos,2,d -决定性,10.971754663213481,pos,3,n -岛,10.96902980213324,pos,1,n -凤凰,10.96902980213324,pos,2,nr -履职,10.96902980213324,pos,2,v -光明,10.958769021619004,pos,2,n -按键,10.958769021619002,pos,2,n -怀抱,10.958769021619002,pos,2,v -姨妈,10.958769021619002,pos,2,n -榜首,10.958769021619002,pos,2,n -实际困难,10.958769021619002,pos,4,n -转弯,10.958769021619002,pos,2,v -脱离,10.95586329044359,pos,2,v -培育,10.947181047343788,pos,2,vn -融合,10.946219571106344,pos,2,vn -技能,10.944618533275968,pos,2,n -起死回生,10.935685408505961,pos,4,i -久,10.931112678716136,pos,1,a -出,10.926545794613101,pos,1,v -蓝,10.926347543926623,pos,1,nr -依次,10.918989119633645,pos,2,d -开花,10.914374902260548,pos,2,n -全民,10.912965332005879,pos,2,n -较大,10.90760812183637,pos,2,a -采用,10.906179098617732,pos,2,v -淘汰,10.887430239898741,pos,2,v -食物,10.884975586848562,pos,2,n -生命周期,10.883217988611825,pos,4,n -新疆维吾尔自治区,10.883217988611825,pos,8,ns -唯有,10.883217988611825,pos,2,b -矿业权,10.880853990746873,pos,3,n -向前,10.872218989662567,pos,2,t -头上,10.872218989662567,pos,2,s -结,10.872218989662567,pos,1,n -达标,10.872218989662567,pos,2,v -人会,10.872218989662565,pos,2,n -健康成长,10.871306180368663,pos,4,nz -将,10.868827358837088,pos,1,d -严明,10.86009543058008,pos,2,a -依法查处,10.86009543058008,pos,4,l -施肥,10.86009543058008,pos,2,v -重视,10.859245100274972,pos,2,v -金钱,10.857682896504688,pos,2,n -土壤污染,10.846876141548105,pos,4,i -涉企,10.843291804199067,pos,2,n -气流,10.843291804199065,pos,2,n -青藏高原,10.825502490755538,pos,4,ns -样子,10.820208191086024,pos,2,n -泄露,10.819453446082733,pos,2,v -pm2,10.819453446082733,pos,3,eng -大致,10.815635461296198,pos,2,d -不至于,10.815635461296198,pos,3,c -过程,10.813238423446645,pos,2,n -积极探索,10.804440875227707,pos,4,n -角色,10.797983630661872,pos,2,n -见,10.797488114585942,pos,1,v -合法,10.78970610268868,pos,2,n -大海,10.78884402017669,pos,2,ns -把握,10.787263686467846,pos,2,v -排头兵,10.78368231506091,pos,3,n -馆,10.78368231506091,pos,1,ng -说明,10.77546180825594,pos,2,v -相信,10.773413979607085,pos,2,v -硬化,10.766986026188595,pos,2,n -医生,10.76660880170197,pos,2,n -冲刺,10.763218212501192,pos,2,vn -长期性,10.763218212501192,pos,3,n -雨,10.747499978754266,pos,1,n -刺激,10.736376600282554,pos,2,v -没有,10.730373050697644,pos,2,v -深圳,10.727098786694542,pos,2,ns -按时,10.726438363226434,pos,2,d -奖惩,10.722591906830145,pos,2,n -责任制,10.722591906830145,pos,3,n -年末,10.720215896217516,pos,2,t -么,10.715912497722837,pos,1,y -填海,10.711232044665595,pos,2,v -海鲜,10.704324523447625,pos,2,ns -销号,10.690170429137764,pos,2,n -监测技术,10.690170429137764,pos,4,n -古代,10.684146641509995,pos,2,t -若干意见,10.679523184938256,pos,4,i -想法,10.673499397310488,pos,2,v -路线,10.67106160619006,pos,2,n -坏,10.668205097640975,pos,1,a -最佳,10.665987272391154,pos,2,z -手续,10.665037818562293,pos,2,v -详查,10.665037818562292,pos,2,v -营养,10.664510614083234,pos,2,n -免于,10.652437781782657,pos,2,v -汇率,10.652437781782657,pos,2,n -适时,10.652437781782655,pos,2,ad -近年来,10.651496892634315,pos,3,t -掌握,10.647740995081257,pos,2,v -中脉,10.647740995081257,pos,2,ns -湖北,10.646178791310977,pos,2,ns -全过程,10.64597062522723,pos,3,n -做到,10.637459059234036,pos,2,v -很足,10.63684092673164,pos,2,a -征服,10.63684092673164,pos,2,v -光芒,10.63684092673164,pos,2,n -至上,10.63684092673164,pos,2,d -周二,10.63684092673164,pos,2,t -思路,10.633935195556226,pos,2,n -链条,10.625714688751257,pos,2,n -看起来,10.613757313618597,pos,3,v -出水,10.609184583828771,pos,2,v -卓,10.609184583828771,pos,1,nr -打滑,10.609184583828771,pos,2,v -居民消费,10.609184583828771,pos,4,n -减慢,10.609184583828771,pos,2,v -明珠,10.609184583828771,pos,2,nr -转贷,10.609184583828771,pos,2,v -妻子,10.606144129508722,pos,2,n -冰箱,10.60311006941909,pos,2,n -浏阳市,10.60311006941909,pos,3,ns -坚定不移,10.60311006941909,pos,4,i -水电,10.60311006941909,pos,2,n -泥,10.597061024746285,pos,1,n -优惠条件,10.597061024746283,pos,4,n -火点,10.597061024746283,pos,2,n -编写,10.597061024746283,pos,2,v -干部带头,10.597061024746283,pos,4,n -一丝不苟,10.597061024746283,pos,4,i -超长,10.597061024746283,pos,2,v -期权,10.597061024746283,pos,2,n -PlayStation,10.597061024746283,pos,11,eng -严肃查处,10.597061024746283,pos,4,nr -细心,10.597061024746283,pos,2,n -分明,10.597061024746283,pos,2,v -严防死守,10.597061024746283,pos,4,i -协同工作,10.597061024746283,pos,4,n -低点,10.597061024746283,pos,2,n -质量标准,10.597061024746283,pos,4,n -柜员,10.597061024746283,pos,2,n -凛冽,10.597061024746283,pos,2,a -请,10.59223998420017,pos,1,v -指责,10.573115329121253,pos,2,v -旅游,10.57311532912125,pos,2,vn -得不到,10.569656219642136,pos,3,v -变更,10.568373516994182,pos,2,v -市场监管,10.561289893724462,pos,4,n -信心,10.56128989372446,pos,2,n -耕地,10.555240849051655,pos,2,n -研究,10.552216741036244,pos,2,vn -工地,10.549755309967928,pos,2,n -较好,10.549560601142355,pos,2,d -购物,10.547614956827388,pos,2,n -追,10.546434951676314,pos,1,v -农药,10.532930687326566,pos,2,n -较差,10.531182071827502,pos,2,a -铁,10.520647909227117,pos,1,n -上海,10.517098657291015,pos,2,ns -药物,10.51607517943729,pos,2,n -场合,10.514934258032723,pos,2,n -外套,10.514934258032723,pos,2,n -危化品,10.509598183495944,pos,3,n -重工业,10.509598183495944,pos,3,n -认真落实,10.503574395868174,pos,4,z -移动,10.503574395868172,pos,2,vn -王,10.502945907493658,pos,1,nr -文字,10.5001838066674,pos,2,n -影片,10.499337402981704,pos,2,n -秋冬季,10.497525351195367,pos,3,t -遵循,10.497525351195367,pos,2,v -共生,10.468180489332983,pos,2,n -工业化,10.461754220173548,pos,3,vn -上岗,10.459557500996349,pos,2,ns -蓝图,10.459557500996349,pos,2,nr -错过,10.45626868108982,pos,2,v -问责,10.45410307090424,pos,2,n -转向,10.453533713368579,pos,2,v -顺应,10.442645397225844,pos,2,v -依,10.436596352553037,pos,1,d -新型,10.42780688825021,pos,2,b -成员,10.42271612137879,pos,2,n -空气污染,10.418245862659031,pos,4,i -过敏,10.416539505886378,pos,2,nr -优化,10.416428213820435,pos,2,vn -会商,10.410464991476694,pos,2,n -气象局,10.410464991476694,pos,3,n -面临,10.401899054882533,pos,2,v -升级,10.398560393686353,pos,2,vn -我家,10.39464002431501,pos,2,r -雄安,10.389403375948863,pos,2,a -群体,10.386792162492323,pos,2,n -主,10.380717648082642,pos,1,b -水泥,10.374668603409836,pos,2,n -所得,10.374668603409836,pos,2,v -主导,10.373806520897846,pos,2,b -天翔,10.373806520897846,pos,2,nz -食材,10.373806520897846,pos,2,n -发烧,10.373806520897844,pos,2,v -可乐,10.373806520897844,pos,2,a -党和政府,10.373806520897844,pos,4,nt -安逸,10.368644815782066,pos,2,nr -管理制度,10.362595771109259,pos,4,n -周边,10.359656032554811,pos,2,f -余额,10.35764581683281,pos,2,n -也就是说,10.350722907784805,pos,4,l -复杂性,10.34818071322235,pos,3,n -雨雪,10.341704272963787,pos,2,n -天空,10.33985918897451,pos,2,n -当然,10.336935956054724,pos,2,d -规,10.33402661891249,pos,1,n -机器,10.322645621115214,pos,2,n -近些年,10.31967796663379,pos,3,t -预告,10.316775575861467,pos,2,v -出让,10.307554407551297,pos,2,v -煤改,10.301605141220112,pos,2,n -显得,10.29825548789067,pos,2,v -奠定,10.295804008896573,pos,2,v -显,10.283608711926266,pos,1,v -国土资源部,10.283608711926266,pos,5,nt -特别强调,10.275132929858922,pos,4,n -绘本,10.275132929858922,pos,2,n -产品品质,10.275132929858922,pos,4,n -功效,10.273926158528926,pos,2,n -同样,10.26005398394737,pos,2,d -你,10.258577906114354,pos,1,r -层层,10.249137721325976,pos,2,n -称,10.244789463035739,pos,1,v -补短,10.240539990034382,pos,2,v -合计,10.234789069047274,pos,2,vn -强度,10.234789069047274,pos,2,n -学科,10.231141292032131,pos,2,n -二要,10.218549401492552,pos,2,b -背街,10.218549401492552,pos,2,n -取暖,10.210215453177582,pos,2,v -三级,10.19414708454993,pos,2,b -蓝鲸,10.194147084549929,pos,2,nr -部手机,10.194147084549929,pos,3,n -成长期,10.194147084549929,pos,3,n -充分体现,10.194147084549929,pos,4,v -划入,10.194147084549929,pos,2,v -表彰大会,10.194147084549929,pos,4,n -经常性,10.194147084549929,pos,3,n -朝向,10.194147084549929,pos,2,n -要不然,10.194147084549929,pos,3,c -疏解,10.194147084549929,pos,2,v -iOS,10.194147084549929,pos,3,eng -行洪,10.194147084549929,pos,2,vn -iPhone,10.194147084549929,pos,6,eng -全力,10.18776951477921,pos,2,n -起来,10.183344588452838,pos,2,v -收运,10.182023525467441,pos,2,n -事前,10.182023525467441,pos,2,t -出厂,10.182023525467441,pos,2,n -包装,10.180515218962162,pos,2,v -从事,10.180341285024898,pos,2,v -考量,10.167010954612415,pos,2,n -着手,10.167010954612415,pos,2,v -碱性,10.167010954612415,pos,2,n -冬季,10.161580677926707,pos,2,t -批评,10.158077829842409,pos,2,v -频繁,10.154618720363292,pos,2,a -行车,10.154618720363292,pos,2,n -这般,10.151414099561396,pos,2,r -懂得,10.151414099561396,pos,2,v -气化,10.151414099561396,pos,2,n -远远,10.137629406108985,pos,2,d -培训,10.133063622689079,pos,2,vn -县,10.13035707228396,pos,1,zg -固定,10.122267753901882,pos,2,a -感染,10.118549465655772,pos,2,v -外卖,10.105610409948273,pos,2,v -社会各界,10.105610409948273,pos,4,l -科学技术,10.105610409948273,pos,4,n -现实,10.091053591585824,pos,2,n -强力,10.069814021881413,pos,2,n -一次性,10.061008124506072,pos,3,d -评审,10.061008124506072,pos,2,vn -愿望,10.051878426010482,pos,2,v -流感,10.046716720894704,pos,2,n -碳,10.046716720894704,pos,1,n -资本,10.031851371330779,pos,2,n -家庭教育,10.026252618334988,pos,4,l -星球,10.026252618334988,pos,2,n -优劣,10.026252618334988,pos,2,a -警惕,10.026252618334988,pos,2,v -近视,10.024222083107617,pos,2,v -动员,10.024222083107617,pos,2,n -员工,10.021181628787566,pos,2,n -强烈,10.012501005556791,pos,2,a -区域环境,10.012098524025129,pos,4,n -兑现,10.012098524025129,pos,2,v -保护措施,10.012098524025129,pos,4,n -永久,10.012098524025129,pos,2,d -行政复议,10.012098524025129,pos,4,n -上路,10.012098524025127,pos,2,ns -周边城市,10.012098524025125,pos,4,ns -要从严,10.012098524025125,pos,3,l -律,10.012098524025125,pos,1,nr -待机,10.012098524025125,pos,2,n -经营户,10.012098524025125,pos,3,n -遵照,10.012098524025125,pos,2,v -划为,10.012098524025125,pos,2,v -每款,10.012098524025125,pos,2,r -迷鹿,10.012098524025125,pos,2,n -民营,10.004572711232127,pos,2,b -看着,10.004572711232127,pos,2,v -经济体,9.99410209473128,pos,3,n -家电,9.990133192528337,pos,2,j -另外,9.990133192528337,pos,2,c -新疆生产建设兵团,9.990133192528337,pos,8,nt -位居,9.958769021619004,pos,2,v -热量,9.958769021619002,pos,2,n -镇村,9.958769021619002,pos,2,ns -冲洗,9.953204834971558,pos,2,vn -防护,9.94910592426828,pos,2,v -扬,9.938849541994488,pos,1,vg -更新,9.935685408505961,pos,2,d -逐渐,9.935685408505957,pos,2,d -赚,9.921128590143514,pos,1,v -税,9.92083407242464,pos,1,n -此外,9.920499004691283,pos,2,c -产品质量,9.909005031061023,pos,4,n -吐,9.899875332565433,pos,1,v -国标,9.89662130660519,pos,2,n -歌曲,9.88690303541968,pos,2,n -进,9.88690303541968,pos,1,v -批示,9.883217988611825,pos,2,v -即可,9.883217988611825,pos,2,d -求,9.883217988611825,pos,1,v -战战兢兢,9.872218989662567,pos,4,i -用人单位,9.872218989662567,pos,4,n -金融危机,9.872218989662567,pos,4,n -转入,9.872218989662567,pos,2,v -取出,9.872218989662567,pos,2,v -小学,9.872218989662565,pos,2,n -脱,9.872218989662565,pos,1,v -公平竞争,9.860095430580078,pos,4,l -林业,9.860095430580076,pos,2,n -责任人,9.858888659250084,pos,3,n -室外,9.856327616892674,pos,2,s -过滤,9.832690625475928,pos,2,v -成,9.831703829923855,pos,1,n -治污,9.831526278383306,pos,2,v -踩,9.825502490755538,pos,1,zg -人心,9.821265497869065,pos,2,n -儿,9.821265497869065,pos,1,n -工厂,9.819453446082731,pos,2,n -告知,9.81278971580172,pos,2,v -时间表,9.81278971580172,pos,3,n -来临,9.804440875227707,pos,2,v -储备,9.801829661771169,pos,2,vn -退化,9.801829661771169,pos,2,v -已,9.799229451802457,pos,1,d -修建,9.789706102688681,pos,2,v -顶,9.78970610268868,pos,1,v -记忆里,9.78884402017669,pos,3,n -蚌埠,9.78884402017669,pos,2,ns -那一刻,9.78884402017669,pos,3,r -环卫工人,9.78884402017669,pos,4,n -太冷,9.78884402017669,pos,2,ns -入,9.788844020176688,pos,1,v -经济社会,9.788154724874092,pos,4,n -树,9.78368231506091,pos,1,v -长效,9.779109585271087,pos,2,a -想,9.767053506885219,pos,1,v -投,9.766608801701969,pos,1,vn -季节,9.7623718088155,pos,2,n -压抑,9.75713516044935,pos,2,v -口味,9.753093995227486,pos,2,n -发达,9.753093995227486,pos,2,v -传说,9.74805840533019,pos,2,n -商品,9.746688107578706,pos,2,n -一致,9.746688107578706,pos,2,d -利润,9.746688107578706,pos,2,n -改善,9.738989889531599,pos,2,v -严肃,9.736464081411699,pos,2,a -稳,9.734992773946336,pos,1,a -生源,9.734715465912632,pos,2,n -缴纳,9.729270880577857,pos,2,v -限时,9.722591906830145,pos,2,n -依法行政,9.722591906830145,pos,4,n -加,9.707579335975117,pos,1,v -融,9.690170429137764,pos,1,vn -倒逼,9.67106160619006,pos,2,v -压实,9.67106160619006,pos,2,n -流动,9.670585128492915,pos,2,vn -源头,9.670108808109712,pos,2,n -加速,9.669262404424016,pos,2,v -改造,9.665037818562292,pos,2,v -民众,9.658094184309718,pos,2,n -城镇化,9.655577489313226,pos,3,n -身高,9.652437781782655,pos,2,v -滨海,9.647740995081257,pos,2,ns -攻略,9.646178791310975,pos,2,v -残忍,9.63684092673164,pos,2,a -自热,9.63684092673164,pos,2,p -耀眼,9.63684092673164,pos,2,a -太久,9.63684092673164,pos,2,nr -午后,9.63684092673164,pos,2,t -天真,9.63684092673164,pos,2,nz -聪明,9.636840926731638,pos,2,a -链接,9.629362465766402,pos,2,n -认证,9.62859103982829,pos,2,v -空间,9.625598140259868,pos,2,n -不得不,9.612994184777273,pos,3,d -化学,9.612231459387637,pos,2,n -磷,9.609184583828775,pos,1,n -保,9.609184583828775,pos,1,v -下功夫,9.609184583828773,pos,3,n -保守,9.609184583828771,pos,2,v -交易成本,9.609184583828771,pos,4,n -国服,9.609184583828771,pos,2,n -悄然,9.609184583828771,pos,2,d -学习,9.607122364422262,pos,2,v -国民经济,9.60311006941909,pos,4,n -仿佛,9.601217017000918,pos,2,d -采样,9.597061024746285,pos,2,v -不折不扣,9.597061024746285,pos,4,i -力争,9.597061024746285,pos,2,nz -依证,9.597061024746283,pos,2,v -纪检,9.597061024746283,pos,2,j -施工方,9.597061024746283,pos,3,n -查清,9.597061024746283,pos,2,nr -项目管理,9.597061024746283,pos,4,n -统筹安排,9.597061024746283,pos,4,n -传播,9.597061024746283,pos,2,vn -庞大,9.597061024746283,pos,2,a -各个领域,9.597061024746283,pos,4,l -保存,9.597061024746282,pos,2,v -阳光,9.589535211953283,pos,2,nr -好转,9.561878869050416,pos,2,v -还,9.559096267660244,pos,1,d -爱心,9.556670578047658,pos,2,n -心中,9.552776661943165,pos,2,s -查,9.550290894775204,pos,1,v -走上,9.540825791164746,pos,2,v -平安,9.521721742578434,pos,2,a -毛东利,9.520647909227117,pos,3,nr -防止,9.51733383227555,pos,2,v -变动,9.51607517943729,pos,2,vn -真,9.510308520802708,pos,1,d -症状,9.509648910277859,pos,2,n -年龄,9.503951620354803,pos,2,n -独特,9.499337402981705,pos,2,a -冷漠,9.499337402981705,pos,2,n -袭击,9.499337402981705,pos,2,v -吹,9.499337402981705,pos,1,v -快乐,9.499337402981704,pos,2,a -题,9.493707366408836,pos,1,n -安全性,9.493707366408836,pos,3,nr -门槛,9.459557500996347,pos,2,n -管道,9.456268681089819,pos,2,n -金融监管,9.450803920613005,pos,4,j -督查组,9.445057931301232,pos,3,n -副厅长,9.442645397225844,pos,3,b -纲要,9.442645397225844,pos,2,n -证照,9.442645397225844,pos,2,n -避开,9.44129011761383,pos,2,v -诉讼,9.432992644496547,pos,2,vn -们,9.431726496827027,pos,1,k -典范,9.427136023303971,pos,2,n -小巷,9.427136023303971,pos,2,n -尽,9.427136023303971,pos,1,v -实,9.417554416263014,pos,1,n -助力,9.412787371025269,pos,2,n -房子,9.40517069180718,pos,2,n -资料,9.40517069180718,pos,2,n -尘,9.395427163576633,pos,1,n -扩大,9.39235432952701,pos,2,v -雨量,9.386792162492323,pos,2,n -民主,9.386792162492323,pos,2,n -负荷,9.386792162492323,pos,2,n -一向,9.386792162492323,pos,2,d -制度性,9.386792162492323,pos,3,n -支,9.386792162492323,pos,1,n -构筑物,9.386792162492323,pos,3,n -体会,9.38548832594548,pos,2,n -告诉,9.37701114169974,pos,2,v -海岸线,9.374668603409837,pos,3,n -确认,9.374668603409836,pos,2,v -早晚,9.373806520897846,pos,2,t -本周,9.373806520897846,pos,2,t -经查,9.368644815782066,pos,2,vn -有害,9.366328059932608,pos,2,a -分工,9.360021827445435,pos,2,vn -采取有效,9.349133511302698,pos,4,n -标杆,9.34818071322235,pos,2,n -无论,9.344059177503793,pos,2,c -中度,9.330509686895294,pos,2,ns -底线,9.324042530339868,pos,2,n -小小,9.323002150226355,pos,2,b -总站,9.323002150226355,pos,2,n -产业链,9.319677966633787,pos,3,n -油,9.31695310555355,pos,1,n -清晨,9.314912831844277,pos,2,t -被动,9.311658805884035,pos,2,vn -钢铁,9.304279275518436,pos,2,n -指引,9.29825548789067,pos,2,v -参与,9.298255487890666,pos,2,v -开采,9.287256488941411,pos,2,v -感谢,9.280697116506364,pos,2,v -渣土,9.275132929858922,pos,2,nz -明说,9.275132929858922,pos,2,v -惩戒,9.275132929858922,pos,2,vn -开辟,9.275132929858922,pos,2,v -纠错,9.275132929858922,pos,2,v -竞争性,9.275132929858922,pos,3,n -宋清辉,9.275132929858922,pos,3,nr -环卫,9.27513292985892,pos,2,j -承受,9.268147665993705,pos,2,v -嘴里,9.25832930347791,pos,2,s -辽宁,9.255547629214071,pos,2,ns -临时,9.249137721325976,pos,2,b -路面,9.239950774163054,pos,2,n -合同,9.2395090201282,pos,2,a -施工,9.230738810500467,pos,2,vn -温度,9.221803427452793,pos,2,n -赢,9.201011625847745,pos,1,v -依靠,9.198719814339753,pos,2,v -心态,9.194375377143313,pos,2,n -天目湖,9.194147084549929,pos,3,ns -痒,9.194147084549929,pos,1,a -将近,9.194147084549929,pos,2,t -当量,9.193006163145359,pos,2,n -大力,9.168611992442791,pos,2,n -全会,9.167010954612415,pos,2,n -暖,9.167010954612415,pos,1,a -祖国,9.167010954612415,pos,2,n -大众,9.158077829842409,pos,2,n -环,9.158077829842409,pos,1,v -气体,9.149752965191475,pos,2,n -公路,9.141475862505278,pos,2,n -之下,9.137629406108985,pos,2,f -上述,9.129857956194948,pos,2,b -依然,9.128330486448355,pos,2,d -身份,9.11901372794164,pos,2,n -各类,9.107061467786219,pos,2,r -表彰,9.10668424329959,pos,2,v -指示,9.105610409948273,pos,2,v -逛,9.100253199778765,pos,1,v -建,9.100253199778763,pos,1,v -模仿,9.094560684217102,pos,2,v -油品,9.094560684217102,pos,2,n -通告,9.094560684217102,pos,2,n -力度,9.09104342027325,pos,2,n -必然,9.089810424735193,pos,2,d -此前,9.088057902808458,pos,2,t -依旧,9.085622627771759,pos,2,z -更为,9.07586306655422,pos,2,d -周,9.07586306655422,pos,1,nr -令,9.07061498859207,pos,1,v -速度,9.056643560799992,pos,2,n -互动,9.05291298685861,pos,2,d -从此,9.051878426010482,pos,2,c -爸爸妈妈,9.051878426010482,pos,4,nr -查处,9.046863942185803,pos,2,v -所谓,9.039521219490501,pos,2,b -养,9.024222083107619,pos,1,v -大气层,9.024222083107617,pos,3,n -方略,9.024222083107617,pos,2,n -管网,9.024222083107615,pos,2,n -拉,9.012098524025129,pos,1,v -考评,9.012098524025127,pos,2,v -肩负,9.012098524025125,pos,2,n -党员,9.012098524025125,pos,2,n -查找,9.012098524025125,pos,2,v -内设,9.012098524025125,pos,2,n -起初,9.012098524025125,pos,2,d -喝,8.996090647824735,pos,1,vg -恐惧,8.974365876670019,pos,2,an -不断,8.97115968202452,pos,2,d -一带,8.970450826666637,pos,2,n -督查,8.969029802133242,pos,2,vn -麻麻,8.958769021619002,pos,2,n -仔细,8.95586329044359,pos,2,ad -形象,8.953204834971558,pos,2,n -新一轮,8.951033372839396,pos,3,nz -股东,8.94214043064326,pos,2,n -景观,8.936401208590548,pos,2,n -意味着,8.935412816149762,pos,3,v -市场准入,8.934096012023856,pos,4,n -传导,8.934096012023856,pos,2,n -电动,8.934096012023854,pos,2,n -拓展,8.931112678716135,pos,2,v -违法,8.929636363833154,pos,2,vn -唯一,8.92314511188828,pos,2,b -又,8.909096719801187,pos,1,d -视频,8.906119849218541,pos,2,n -喵,8.899875332565435,pos,1,o -对接,8.883217988611825,pos,2,v -泄漏,8.87459500027519,pos,2,v -燃气,8.872218989662567,pos,2,n -车身,8.872218989662567,pos,2,n -转让,8.872218989662567,pos,2,v -第一季度,8.872218989662567,pos,4,l -纤维,8.871306180368663,pos,2,n -功能区,8.869140570183085,pos,3,n -尽管,8.868571212647423,pos,2,c -移送,8.860095430580078,pos,2,v -电器,8.849827095126251,pos,2,n -证据,8.842173522582815,pos,2,n -雾霾,8.831581264024713,pos,2,n -可爱,8.829486004674035,pos,2,v -帮扶,8.829486004674035,pos,2,v -似乎,8.829042848460785,pos,2,d -伟大,8.824618757165336,pos,2,a -程序,8.824471520849356,pos,2,n -吃,8.81719521365649,pos,1,v -颗粒,8.815635461296198,pos,2,n -杨,8.815635461296198,pos,1,nr -矛盾,8.809677523593853,pos,2,an -强大,8.804963685539965,pos,2,a -随后,8.804440875227707,pos,2,d -报告,8.801412094874372,pos,2,n -施工单位,8.78970610268868,pos,4,n -停止使用,8.789706102688678,pos,4,i -宏,8.789706102688678,pos,1,n -追责,8.789706102688678,pos,2,v -禁令,8.789706102688678,pos,2,n -马克,8.789706102688678,pos,2,nr -寸滩,8.789706102688678,pos,2,n -法国,8.789706102688678,pos,2,ns -划定,8.789706102688678,pos,2,v -药,8.78884402017669,pos,1,n -笑,8.783682315060911,pos,1,v -高质量,8.76774077119189,pos,3,n -改正,8.760559757029164,pos,2,v -水务,8.734715465912632,pos,2,n -活跃,8.73471546591263,pos,2,vn -移交,8.731990604832394,pos,2,v -硬件,8.726438363226434,pos,2,n -同意,8.72067251753511,pos,2,d -拆违,8.71441797538444,pos,2,v -增添,8.710841508175417,pos,2,v -地处,8.710841508175417,pos,2,s -矿产资源,8.709535754004696,pos,4,n -新区,8.703511966376928,pos,2,ns -污水处理,8.702293988220253,pos,4,n -考核组,8.690170429137764,pos,3,n -公安机关,8.67106160619006,pos,4,n -年轻人,8.670585128492915,pos,3,n -含,8.66345678274281,pos,1,v -行业,8.65549882319181,pos,2,n -考虑,8.646850307490316,pos,2,v -充分,8.632345799074121,pos,2,ad -防治法,8.619781101246366,pos,3,n -司法,8.619781101246366,pos,2,n -收入,8.61779771413348,pos,2,v -处罚,8.61216791713649,pos,2,v -汉江,8.609184583828773,pos,2,ns -抗菌肽,8.609184583828773,pos,3,nz -繁荣,8.609184583828773,pos,2,a -严厉打击,8.609184583828773,pos,4,i -星座,8.609184583828773,pos,2,n -刚性,8.609184583828773,pos,2,n -热潮,8.597061024746283,pos,2,n -放开,8.597061024746283,pos,2,v -愈发,8.597061024746283,pos,2,d -检测,8.597061024746283,pos,2,vn -智造,8.597061024746283,pos,2,n -加密,8.597061024746283,pos,2,ns -寒风,8.597061024746283,pos,2,n -保卫战,8.586535844883574,pos,3,nz -不但,8.569656219642138,pos,2,c -密闭,8.566451598840242,pos,2,v -无力,8.566451598840242,pos,2,n -振兴,8.555413327387793,pos,2,v -有时,8.548101121967921,pos,2,r -分类,8.54394968828672,pos,2,n -放弃,8.540825791164746,pos,2,v -河,8.537925900658951,pos,1,ns -生存,8.512512791729437,pos,2,v -欧美,8.509598183495944,pos,2,ns -工程机械,8.509598183495944,pos,4,n -政务,8.501903791705942,pos,2,n -外面,8.499337402981705,pos,2,f -核心,8.499337402981705,pos,2,n -毛利率,8.493707366408836,pos,3,n -出生,8.466915925289326,pos,2,v -心里,8.466915925289326,pos,2,s -培养,8.46622662998673,pos,2,v -之间,8.463754144307208,pos,2,f -体验,8.444125337558276,pos,2,n -高效,8.444109101907683,pos,2,a -包,8.44129011761383,pos,1,v -离,8.44129011761383,pos,1,v -没法,8.435207065561988,pos,2,v -边,8.428023362860996,pos,1,d -尾气,8.427136023303973,pos,2,n -内河,8.427136023303971,pos,2,ns -挑选,8.427136023303971,pos,2,v -电压,8.427136023303971,pos,2,n -多管齐下,8.427136023303971,pos,4,l -巡查,8.412636453608854,pos,2,vn -各项,8.39368954021083,pos,2,r -悲伤,8.389403375948863,pos,2,a -睡,8.38809717844824,pos,1,v -手术,8.386792162492325,pos,2,n -高新区,8.386792162492325,pos,3,nr -走出,8.386792162492325,pos,2,v -承诺,8.379830308525614,pos,2,v -少,8.374668603409837,pos,1,a -模样,8.373806520897846,pos,2,n -女性,8.35965603255481,pos,2,n -执行,8.356052925242489,pos,2,v -重要性,8.34818071322235,pos,3,n -原,8.344059177503793,pos,1,n -问题,8.343538399001583,pos,2,n -农民,8.330274484051381,pos,2,n -反弹,8.324042530339868,pos,2,v -近日,8.32133910100371,pos,2,t -构成,8.31695310555355,pos,2,v -享受,8.314912831844278,pos,2,v -纯净,8.314912831844277,pos,2,a -背景,8.289632499554035,pos,2,n -新西兰,8.28725648894141,pos,3,ns -体育产业,8.28725648894141,pos,4,n -便,8.281181974531727,pos,1,d -边界,8.275132929858922,pos,2,n -沿途,8.275132929858922,pos,2,b -灭火,8.275132929858922,pos,2,v -公式,8.275132929858922,pos,2,n -PLSON,8.275132929858922,pos,5,eng -上门,8.275132929858922,pos,2,ns -略有,8.27513292985892,pos,2,v -人民币,8.273926158528926,pos,3,n -消耗,8.25832930347791,pos,2,n -大雪,8.25832930347791,pos,2,n -替代,8.24644542210682,pos,2,n -山区,8.239950774163054,pos,2,ns -省,8.23227007766209,pos,1,n -越,8.229944156092666,pos,1,d -河北,8.22296736058643,pos,2,ns -类型,8.21483947970303,pos,2,n -然而,8.214324966487558,pos,2,c -修订,8.206181551893092,pos,2,v -过于,8.203881519455534,pos,2,v -今年以来,8.194147084549929,pos,4,i -宽容,8.194147084549929,pos,2,a -牧师,8.194147084549929,pos,2,n -化工,8.191068665070446,pos,2,n -伤害,8.186179517722076,pos,2,a -餐饮,8.170796270044185,pos,2,n -小,8.157426030101712,pos,1,a -照,8.151414099561398,pos,1,n -电池,8.151414099561398,pos,2,n -实效,8.137629406108987,pos,2,v -浙大,8.137629406108987,pos,2,ns -河西区,8.137629406108987,pos,3,ns -该区,8.137629406108987,pos,2,r -查实,8.137629406108987,pos,2,v -尺度,8.137629406108987,pos,2,n -干嘛,8.137629406108987,pos,2,n -费改税,8.137629406108985,pos,3,n -备案,8.137629406108985,pos,2,n -任何,8.125452437950122,pos,2,r -明白,8.119362022726468,pos,2,nr -违法犯罪,8.111634197576041,pos,4,l -超收,8.10668424329959,pos,2,v -大局,8.10668424329959,pos,2,n -渗,8.10668424329959,pos,1,v -环节,8.100635198626785,pos,2,n -好好,8.07612597225716,pos,2,d -回头,8.07349906868927,pos,2,v -镇,8.06861620246607,pos,1,n -是从,8.062778494360101,pos,2,v -人性,8.051878426010482,pos,2,n -运输,8.04862440005024,pos,2,vn -手段,8.04862440005024,pos,2,n -既,8.046716720894704,pos,1,c -沟通,8.040341748470892,pos,2,v -突出,8.032451375153686,pos,2,v -西北地区,8.024222083107617,pos,4,ns -门前三包,8.012098524025127,pos,4,i -把关,8.012098524025127,pos,2,v -化解,8.012098524025127,pos,2,v -优惠政策,8.004572711232127,pos,4,n -自然,8.00407238348621,pos,2,d -颗粒物,8.000375341153248,pos,3,n -登记,7.997148182559155,pos,2,v -船,7.990628708604266,pos,1,n -传达,7.990628708604266,pos,2,v -有个,7.971754663213483,pos,2,r -担忧,7.958769021619002,pos,2,v -当事人,7.953204834971558,pos,3,n -截至,7.938646351401541,pos,2,v -购买,7.935412816149762,pos,2,v -人类,7.9157858510682555,pos,2,n -痛,7.914374902260548,pos,1,a -在建,7.896621306605191,pos,2,v -上报,7.896621306605191,pos,2,v -冰虫,7.896621306605191,pos,2,n -建筑材料,7.896621306605191,pos,4,n -清楚,7.888456369608724,pos,2,a -展开,7.874595000275191,pos,2,v -想想,7.871306180368663,pos,2,v -电影,7.844187054020464,pos,2,n -立案,7.842173522582817,pos,2,n -合一,7.842173522582815,pos,2,vn -提出,7.837873385149184,pos,2,v -关键,7.836595079931847,pos,2,n -清单,7.828876699969356,pos,2,a -学会,7.809677523593853,pos,2,n 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+眼,7.668662569544479,pos,1,n +治安,7.647904009377683,pos,2,ns +物质,7.637635673923855,pos,2,n +满,7.6349183677832,pos,1,a +合适,7.6349183677832,pos,2,a 用力,7.629356620079609,pos,2,n -中华人民共和国,7.627434673789802,pos,7,ns -滤液,7.609184583828773,pos,2,nz -专注,7.609184583828773,pos,2,v -法制,7.609184583828773,pos,2,n -食用,7.597061024746283,pos,2,n -面源,7.597061024746283,pos,2,n -可可树,7.597061024746283,pos,3,l -地面,7.589285026391069,pos,2,n -乐观,7.57943724043472,pos,2,a -老师,7.56682099969769,pos,2,n -北方,7.566451598840242,pos,2,f -智慧,7.56313124054863,pos,2,nr -侵权,7.552666905387829,pos,2,v -跟踪,7.552666905387829,pos,2,v -气,7.54394968828672,pos,1,n -数量,7.537925900658951,pos,2,n -处理厂,7.535935601798133,pos,3,n -河道,7.512323044576185,pos,2,n -门,7.509598183495946,pos,1,n -做起,7.509598183495944,pos,2,v -反对,7.509598183495944,pos,2,d -存单,7.509598183495944,pos,2,n -普,7.509598183495944,pos,1,ad -阳江市,7.509598183495944,pos,3,ns -就是,7.484139028888098,pos,2,d -家人,7.483035590652603,pos,2,n -清理,7.471530142662424,pos,2,a -餐厅,7.466915925289326,pos,2,n -寒冷,7.466915925289326,pos,2,a -有机,7.466915925289326,pos,2,n -产能,7.461754220173548,pos,2,v -市民,7.454148629215448,pos,2,n -许可证,7.4473139052416,pos,3,nr -女儿,7.439259582386461,pos,2,n -现有,7.439259582386461,pos,2,b -财富,7.439259582386461,pos,2,n -和硕县,7.427136023303971,pos,3,nr -示范,7.427136023303971,pos,2,v -责令,7.427136023303971,pos,2,n -从严,7.427136023303971,pos,2,d -股票,7.427136023303971,pos,2,n -战争,7.427136023303971,pos,2,n -人体,7.419610210510971,pos,2,n -精神,7.414448505395191,pos,2,n -协同,7.4051706918071805,pos,2,n -位置,7.404415946803889,pos,2,v -感冒,7.404180169941364,pos,2,v -达标率,7.386792162492325,pos,3,n -挂,7.386792162492325,pos,1,v -动能,7.386792162492325,pos,2,n -吸引力,7.386792162492325,pos,3,n -信息化,7.3491335113027,pos,3,n -客服,7.349133511302698,pos,2,n -贫困,7.34818071322235,pos,2,a -压力,7.341120744018717,pos,2,n -高达,7.336166089422356,pos,2,a -变化,7.333550141215342,pos,2,vn -清新,7.330274484051381,pos,2,a -地点,7.325812900193895,pos,2,n -海洋,7.313421414583294,pos,2,ns -当地,7.31109139448713,pos,2,s -服务体系,7.27513292985892,pos,4,n -水利,7.27513292985892,pos,2,n -外国,7.27513292985892,pos,2,ns -不放过,7.27513292985892,pos,3,l -滥用,7.27513292985892,pos,2,v -容忍,7.27513292985892,pos,2,a -顶层,7.27513292985892,pos,2,n -秸秆,7.27513292985892,pos,2,n -毕竟,7.260717871972011,pos,2,d -感觉,7.258329303477911,pos,2,n -之前,7.2575464153400695,pos,2,f -予以,7.257211021861659,pos,2,v -首先,7.2501486302618225,pos,2,d -他人,7.239950774163054,pos,2,r -PM2,7.236077765015846,pos,3,eng -导致,7.214786850608965,pos,2,v -政策措施,7.2047436019675235,pos,4,n -犯罪,7.2047436019675235,pos,2,vn -违规,7.204743601967522,pos,2,vn -下游,7.194147084549929,pos,2,f -罚款,7.174528988920514,pos,2,n -弄虚作假,7.170796270044185,pos,4,i -市,7.159677699771688,pos,1,n -可能,7.158628421465439,pos,2,v -推出,7.150804794856718,pos,2,v -优势,7.149752965191475,pos,2,n -性能,7.149752965191475,pos,2,n -网络空间,7.137629406108985,pos,4,n -哪些,7.120898102519291,pos,2,r -发育,7.10668424329959,pos,2,vn -待,7.100788026491429,pos,1,v -已有,7.094560684217102,pos,2,v -文化,7.0907928705296985,pos,2,n -火锅,7.0761259722571594,pos,2,n -水域,7.073499068689269,pos,2,n -事后,7.073499068689269,pos,2,t -功能,7.070764669044648,pos,2,n -教师,7.064864067604962,pos,2,n -追究,7.032276405962758,pos,2,v -噪声,7.027205416415335,pos,2,n -无人,7.024222083107617,pos,2,n -城区,7.0241713563257,pos,2,n -违法行为,7.022152188689052,pos,4,l -里,7.017262353442451,pos,1,f -守法,7.012098524025127,pos,2,n -行政拘留,7.012098524025127,pos,4,i -探索,7.008581592007932,pos,2,v -正,7.004603987478202,pos,1,d -担心,7.004572711232127,pos,2,v -给予,6.997085953170101,pos,2,v -面积,6.9865634319179915,pos,2,n -开工,6.98469371892098,pos,2,v -企,6.98469371892098,pos,1,n -河北省,6.982351180631074,pos,3,ns -拥有,6.964260126666558,pos,2,v -严格执行,6.953204834971558,pos,4,nr -烧烤,6.953204834971558,pos,2,v -宜昌,6.953204834971558,pos,2,ns -联合,6.946009333567355,pos,2,v -满足,6.9180226792756905,pos,2,v -世纪,6.914374902260548,pos,2,n -湿度,6.90874486568768,pos,2,n -达,6.907761864210391,pos,1,v -看,6.906491070889052,pos,1,v -入海,6.8966213066051925,pos,2,ns -办结,6.896621306605191,pos,2,v -建工,6.896621306605191,pos,2,vn -原有,6.896621306605191,pos,2,v -起诉,6.896621306605191,pos,2,v -冬天,6.892679831161232,pos,2,t -交警,6.89267983116123,pos,2,j -首都,6.890366336372825,pos,2,d -做出,6.872447282255951,pos,2,v -纳税,6.872447282255951,pos,2,n -效益,6.872218989662565,pos,2,n -待遇,6.872218989662565,pos,2,n -评论,6.850244564840832,pos,2,n -抱歉,6.850244564840832,pos,2,v -京津冀,6.8450828597250535,pos,3,n -到来,6.836595079931845,pos,2,d -环境保护部,6.815701311221623,pos,5,n -电,6.806984094120516,pos,1,n -队伍,6.801829661771169,pos,2,n -全新,6.801829661771169,pos,2,d -预约,6.78970610268868,pos,2,v -建立健全,6.78970610268868,pos,4,nr -常常,6.78884402017669,pos,2,d -抱怨,6.778325104891401,pos,2,v -督促,6.7726325893297386,pos,2,v -超标,6.7726325893297386,pos,2,v -浓度,6.7683070891739945,pos,2,n -补贴,6.767882329847831,pos,2,n -帮,6.763218212501194,pos,1,v -先进,6.7512035887012,pos,2,a -赋予,6.7390800296187106,pos,2,v -条款,6.7390800296187106,pos,2,n -积累,6.734715465912631,pos,2,v -复合,6.734715465912631,pos,2,v -管理水平,6.722591906830141,pos,4,n -害怕,6.710841508175417,pos,2,v -冷,6.710841508175417,pos,1,a -没什么,6.702293988220253,pos,3,l -食品药品,6.702293988220253,pos,4,n -尚未,6.702293988220253,pos,2,d -搜救,6.690170429137764,pos,2,v -损害赔偿,6.683316758096703,pos,4,n -垃圾,6.681453212036654,pos,2,n -问,6.6805843803085665,pos,1,n -条,6.6805843803085665,pos,1,n -江苏,6.668205097640975,pos,2,ns -指数,6.658094184309718,pos,2,n -测算,6.642864714359408,pos,2,vn -执法检查,6.642864714359408,pos,4,n -额度,6.642864714359408,pos,2,n -印染,6.639558232872291,pos,2,vn -泪,6.636840926731638,pos,1,n -案件,6.617238906683912,pos,2,n -不再,6.609184583828775,pos,2,d -化工企业,6.597061024746283,pos,4,n -垃圾焚烧,6.597061024746283,pos,4,vn -武汉市,6.597061024746283,pos,3,ns -行政,6.580759212417181,pos,2,n -比较,6.570260965402568,pos,2,d -完成,6.5637237771978345,pos,2,v -另,6.559941064808925,pos,1,r -土壤,6.552666905387829,pos,2,n -雪,6.549378085481299,pos,1,n -随机,6.521721742578434,pos,2,d -设计,6.517958868208913,pos,2,vn -约,6.493707366408838,pos,1,d -属于,6.493707366408838,pos,2,v -质量,6.468570915235892,pos,2,n -担当,6.467778007801316,pos,2,v -重新,6.46622662998673,pos,2,a -新安江,6.439259582386461,pos,3,ns -应当,6.435207065561988,pos,2,v -处置,6.435173342369389,pos,2,v -入冬,6.427136023303971,pos,2,v -数据,6.420699022840582,pos,2,n -苹果,6.412787371025269,pos,2,n -本来,6.412787371025269,pos,2,t -潜在,6.387607659117334,pos,2,t -虽,6.387607659117334,pos,1,zg -密集,6.387607659117334,pos,2,n -农村,6.387607659117332,pos,2,n -证明,6.368242334250402,pos,2,n -山,6.368242334250402,pos,1,n -材料,6.368242334250402,pos,2,n -安静,6.358856179431873,pos,2,nr -能量,6.344059177503793,pos,2,n -应,6.30479871206682,pos,1,v -事实,6.3037865938638955,pos,2,n -先,6.282091522764578,pos,1,d -财政收入,6.276609244741904,pos,4,n -接下来,6.27513292985892,pos,3,l -乱,6.2512861879045545,pos,1,d -权限,6.2395090201281995,pos,2,n -跑,6.22688673194118,pos,1,v -例,6.2047436019675235,pos,1,v -形式,6.2047436019675235,pos,2,n -缓慢,6.170796270044185,pos,2,d -强,6.166241087980044,pos,1,a -排放,6.15848801100665,pos,2,v -水源地,6.149752965191475,pos,3,n -这里,6.1449878304019645,pos,2,r -取缔,6.105207928416608,pos,2,v -室内空气,6.105207928416608,pos,4,n -各种,6.093730794106534,pos,2,r -觉得,6.0907928705296985,pos,2,v -垃圾处理,6.073499068689269,pos,4,n -关心,6.064864067604962,pos,2,n -甘肃,6.042472173068646,pos,2,ns -快速,6.03481860052521,pos,2,d -村民,6.028031684056115,pos,2,n -绿化,6.027205416415335,pos,2,n -产生,6.024222083107615,pos,2,n -采砂,6.012098524025127,pos,2,n -为什么,5.991372301975709,pos,3,r -电子,5.98469371892098,pos,2,n -一旦,5.98469371892098,pos,2,d -河流,5.982351180631074,pos,2,n -具备,5.965328394054048,pos,2,v -岗位,5.953204834971558,pos,2,n -黑,5.947541766195748,pos,1,a -禁止,5.938849541994488,pos,2,v -也许,5.9364012085905475,pos,2,d -衔接,5.924635682774788,pos,2,v -纳税人,5.905237217373621,pos,3,n -台账,5.896621306605191,pos,2,n -国外,5.896621306605191,pos,2,s -有利于,5.890366336372825,pos,3,v -油烟,5.885489641378488,pos,2,n -饮用水,5.872218989662565,pos,3,n -制定,5.853327583450616,pos,2,v -污染源,5.833295371236364,pos,3,n -是,5.823977440826155,pos,1,v -厅,5.806984094120516,pos,1,n -面对,5.805084661183901,pos,2,v -不过,5.804440875227707,pos,2,c -武汉,5.801829661771169,pos,2,ns -实行,5.801067168041879,pos,2,v -法规,5.78970610268868,pos,2,n -就业,5.780519155525758,pos,2,v -行政处罚,5.769242000128962,pos,4,n -区别,5.764171010581542,pos,2,n -河长,5.716642640498955,pos,2,n -环保局,5.714417975384443,pos,3,j -维护,5.713498014953702,pos,2,v -表现,5.696531013727759,pos,2,v -立法,5.690170429137764,pos,2,d -伦敦,5.690170429137764,pos,2,ns -强调,5.686895297104904,pos,2,v -新增,5.6863524443512325,pos,2,v -只有,5.682663150334665,pos,2,c -人民法院,5.666323687183397,pos,4,nt -历史,5.647740995081257,pos,2,n -局,5.619781101246366,pos,1,n -举措,5.586172708603547,pos,2,v -成绩,5.57943724043472,pos,2,n -预测,5.5647904644703186,pos,2,vn -天数,5.563638023208833,pos,2,n -那些,5.553872149149715,pos,2,r -发行,5.552666905387829,pos,2,v -如,5.547202712987634,pos,1,v -贡献,5.521721742578434,pos,2,n -辖区,5.509598183495944,pos,2,n -专项,5.509598183495944,pos,2,n -记者,5.492695754261584,pos,2,n -口罩,5.471461124821166,pos,2,n -显示,5.470770555131679,pos,2,v -因此,5.460924336760256,pos,2,c -用于,5.452680098148782,pos,2,v -关闭,5.407236465866266,pos,2,v -降低,5.374535665431161,pos,2,v -立即,5.349133511302698,pos,2,d -那种,5.349133511302698,pos,2,r -种植,5.330274484051381,pos,2,v -人民政府,5.330274484051381,pos,4,nt +中华人民共和国,7.625512114841365,pos,7,ns +滤液,7.607262024880336,pos,2,nz +专注,7.607262024880336,pos,2,v +法制,7.607262024880336,pos,2,n +面源,7.595138465797847,pos,2,n +可可树,7.595138465797847,pos,3,l +食用,7.595138465797847,pos,2,n +地面,7.587362467442633,pos,2,n +乐观,7.577514681486283,pos,2,a +智慧,7.571192770172816,pos,2,nr +老师,7.564898440749253,pos,2,n +北方,7.564529039891802,pos,2,f +气,7.559514556067125,pos,1,n +跟踪,7.550744346439393,pos,2,v +侵权,7.550744346439393,pos,2,v +数量,7.536003341710515,pos,2,n +处理厂,7.534013042849697,pos,3,n +河道,7.510400485627748,pos,2,n +门,7.50767562454751,pos,1,n +普,7.507675624547508,pos,1,ad +做起,7.507675624547508,pos,2,v +反对,7.507675624547508,pos,2,d +存单,7.507675624547508,pos,2,n 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+贡献,5.534013042849697,pos,2,n +专项,5.507675624547508,pos,2,n +辖区,5.507675624547508,pos,2,n +记者,5.488850636364708,pos,2,n +口罩,5.469538565872728,pos,2,n +显示,5.468847996183243,pos,2,v +因此,5.459001777811819,pos,2,c +用于,5.450757539200346,pos,2,v +就,5.421237503783047,pos,1,d +关闭,5.405313906917829,pos,2,v +降低,5.372613106482724,pos,2,v +那种,5.347210952354262,pos,2,r +立即,5.347210952354262,pos,2,d +人民政府,5.328351925102945,pos,4,nt +种植,5.328351925102945,pos,2,v 天天,5.326165087773834,pos,2,t -联动,5.311658805884035,pos,2,v -全区,5.311658805884035,pos,2,n -据悉,5.311658805884035,pos,2,v -新疆,5.28546320711836,pos,2,ns -体制,5.281784637731347,pos,2,n -环境污染,5.277242700313389,pos,4,n -吧,5.22138132187105,pos,1,y -每年,5.216867161050011,pos,2,r -则,5.182919829126675,pos,1,d -就,5.165427932418954,pos,1,d -媒体,5.137629406108985,pos,2,n -主要,5.116486489559712,pos,2,b -法院,5.064864067604962,pos,2,n -滤网,5.064864067604962,pos,2,nz -进展,5.042472173068646,pos,2,vn -环境卫生,4.997148182559155,pos,4,n -年底,4.994474739713564,pos,2,t -科技,4.949667830815168,pos,2,n -当时,4.855594038345137,pos,2,t -目的,4.842173522582815,pos,2,n -共同,4.767882329847831,pos,2,d -印发,4.7390800296187106,pos,2,nz -监控,4.702243261438339,pos,2,vn -月份,4.690170429137764,pos,2,n -时,4.654744703739496,pos,1,n -日本,4.642864714359408,pos,2,ns -机制,4.6298606904942705,pos,2,n -收益,4.586816770800317,pos,2,n -自然保护区,4.574693211717829,pos,5,n -介绍,4.504303883826431,pos,2,v -街道,4.499028941785756,pos,2,n -长江,4.46622662998673,pos,2,ns -人们,4.427434146989672,pos,2,n -试点,4.397388679909918,pos,2,n -全部,4.386792162492325,pos,2,n -规定,4.354727527775919,pos,2,n -较,4.3507444322978905,pos,1,zg -阶段,4.330274484051381,pos,2,n -值守,4.293280276569181,pos,2,v -而且,4.182919829126675,pos,2,c -许可,4.162432797109558,pos,2,nr +全区,5.309736246935598,pos,2,n +据悉,5.309736246935598,pos,2,v +联动,5.309736246935598,pos,2,v +新疆,5.283540648169923,pos,2,ns +体制,5.2798620787829105,pos,2,n +环境污染,5.275320141364952,pos,4,n +吧,5.2175362039741735,pos,1,y +每年,5.214944602101575,pos,2,r +则,5.1809972701782385,pos,1,d +媒体,5.135706847160549,pos,2,n +主要,5.110718812714403,pos,2,b +法院,5.062941508656525,pos,2,n +滤网,5.062941508656525,pos,2,nz +进展,5.040549614120209,pos,2,vn +环境卫生,4.995225623610718,pos,4,n +年底,4.992552180765127,pos,2,t +科技,4.947745271866729,pos,2,n +当时,4.8536714793967,pos,2,t +目的,4.840250963634379,pos,2,n +共同,4.765959770899395,pos,2,d +印发,4.737157470670274,pos,2,nz +监控,4.700320702489902,pos,2,vn +月份,4.688247870189327,pos,2,n +时,4.666843244721235,pos,1,n +日本,4.640942155410972,pos,2,ns +机制,4.627938131545832,pos,2,n +收益,4.58489421185188,pos,2,n +自然保护区,4.5727706527693925,pos,5,n +介绍,4.5023813248779945,pos,2,v +街道,4.49710638283732,pos,2,n +长江,4.464304071038292,pos,2,ns +人们,4.4255115880412355,pos,2,n +试点,4.395466120961482,pos,2,n +全部,4.384869603543889,pos,2,n +规定,4.352804968827483,pos,2,n +较,4.346899314401018,pos,1,zg +阶段,4.328351925102945,pos,2,n +值守,4.291357717620745,pos,2,v +而且,4.1809972701782385,pos,2,c +许可,4.160510238161121,pos,2,nr 点击,4.1591985948492525,pos,2,v -抓好,4.151632265507962,pos,2,v -不少,4.128057894092157,pos,2,d -今年,4.110601187186827,pos,2,t +抓好,4.149709706559525,pos,2,v +不少,4.12613533514372,pos,2,d +今年,4.1086786282383905,pos,2,t 郑州,4.007195501404205,pos,2,ns -督察,3.9936369881775935,pos,2,v +督察,3.991714429229157,pos,2,v 活力,3.978626349207433,pos,2,n -汽车,3.953743254474313,pos,2,n -各地,3.940911906784404,pos,2,r -统筹,3.9096853413088173,pos,2,v -由于,3.9041282376173108,pos,2,c +汽车,3.9518206955258766,pos,2,n +各地,3.9389893478359674,pos,2,r +统筹,3.907762782360381,pos,2,v +由于,3.9022056786688744,pos,2,c +然后,3.822158828644289,pos,2,c 疾病,3.818161677014187,pos,2,n 不同,3.7859812712650385,pos,2,a -完善,3.768395596443268,pos,2,v +完善,3.76647303749483,pos,2,v 河南,3.7441610955704085,pos,2,ns 之中,3.736965594166204,pos,2,r -意见,3.541778589245096,pos,2,n -村,3.5291665547396,pos,1,zg -知道,3.5181900516081797,pos,2,v +意见,3.5550691242431665,pos,2,n +村,3.5272439957911637,pos,1,zg +知道,3.5162674926597433,pos,2,v 地铁,3.4286592698424805,pos,2,n -共,3.417151934731349,pos,1,d -加大,3.3228011873133596,pos,2,v -开展,3.2705115042667803,pos,2,v +共,3.4152293757829124,pos,1,d +加大,3.320878628364923,pos,2,v +开展,3.2705424934040987,pos,2,v 结合,3.1793236994445646,pos,2,v 确实,3.169925001442312,pos,2,ad 调,3.0158249671616844,pos,1,v 真是,3.006663730281012,pos,2,d -启动,2.9363918915602323,pos,2,vn -没,2.9356854085059627,pos,1,v -一切,2.882376259564815,pos,2,r +启动,2.9405438470214786,pos,2,vn +没,2.9402178716948235,pos,1,v +一切,2.8804537006163784,pos,2,r nice,2.8166927866369385,pos,4,eng -省政府,2.7754319717065616,pos,3,n -之一,2.723151801597572,pos,2,r -大家,2.6715971909973373,pos,2,n -严重,2.669676602281223,pos,2,a -不能,2.646920837150711,pos,2,v +省政府,2.7735094127581252,pos,3,n +之一,2.7212292426491356,pos,2,r +大家,2.669674632048899,pos,2,n +严重,2.6677540433327867,pos,2,a +不能,2.6449982782022747,pos,2,v 导向,2.631612594019977,pos,2,n -成本,2.468698362591107,pos,2,n +成本,2.548717417178846,pos,2,n 生,2.371968777386959,pos,1,vn 西安,2.334285301407343,pos,2,ns -水,2.2883429312978016,pos,1,n -雾,2.2722554206935275,pos,1,n -结果,2.2166348727201903,pos,2,n -国际,2.2057076871128736,pos,2,n -车辆,2.162349406742729,pos,2,n +水,2.291844044304387,pos,1,n +雾,2.270332861745091,pos,1,n +国际,2.217679569822817,pos,2,n +结果,2.214712313771754,pos,2,n +车辆,2.1604268477942927,pos,2,n 期待,2.1468413883292694,pos,2,v -取得,2.032223491181961,pos,2,v -指标,1.9534302736845532,pos,2,n -相关,1.9434526344617797,pos,2,v -湿地,1.9011163238231017,pos,2,z +取得,2.0303009322335246,pos,2,v +指标,1.9515077147361168,pos,2,n +相关,1.9415300755133433,pos,2,v +湿地,1.8991937648746653,pos,2,z 今天,1.8961641890154617,pos,2,t 促,1.8865419502167171,pos,1,v -脸,1.8744691179161403,pos,1,n -看见,1.8488584742371028,pos,2,v -骑,1.7842713089445628,pos,1,v +脸,1.8744691179161386,pos,1,n +看见,1.848858474237101,pos,2,v +骑,1.784271308944561,pos,1,v 惠民,1.6938968722743208,pos,2,nr -以上,1.6223766044369796,pos,2,f +以上,1.663098382897033,pos,2,f 东北地区,1.584962500721156,pos,4,ns -走,1.561995092159112,pos,1,v -记录,1.4871504773643842,pos,2,n +走,1.5600725332106755,pos,1,v 原来,1.4871504773643842,pos,2,d +记录,1.4871504773643842,pos,2,n 难,1.430634354329861,pos,1,a -建议,1.3435486503172704,pos,2,n -中速,1.2399507927207534,pos,2,n -这么,1.231699463595298,pos,2,r +建议,1.341626091368834,pos,2,n +中速,1.2399507927207516,pos,2,n +这么,1.2297769046468598,pos,2,r 错峰,1.2096165018354768,pos,2,n -需,1.1742954108351835,pos,1,v +需,1.1723728518867471,pos,1,v 粉丝,1.0324214776923757,pos,2,n -趋势,1.0219279524543694,pos,2,n +趋势,1.020005393505933,pos,2,n 情,0.96829114027266,pos,1,n 节点,0.8804929055439104,pos,2,n -项,0.6951885039043901,pos,1,n +项,0.6932659449559537,pos,1,n 处在,0.6589630821649308,pos,2,v 交易,0.6057210608879515,pos,2,n -价格,0.5882971551009177,pos,2,n -促进,0.5515136961391551,pos,2,v +价格,0.5924236408252881,pos,2,n +促进,0.5495911371907187,pos,2,v 一策,0.487150477364386,pos,2,n 符合实际,0.4594316186373,pos,4,n 无法,0.45777048426737643,pos,2,n 抓实,0.3719687773869591,pos,2,v 以后,0.31034012061214966,pos,2,f -品牌,0.30373367908309845,pos,2,n +品牌,0.30181112013466205,pos,2,n 文件,0.2895066171949843,pos,2,n 上班,0.2223924213364512,pos,2,v -扎实,0.20474360196752173,pos,2,a +扎实,0.20282104301908532,pos,2,a 一企,0.039691500393162826,pos,2,m 不足,0.036069254707520315,pos,2,a -检查,0.029573128806250182,pos,2,vn +检查,0.027650569857813778,pos,2,vn