Merge pull request #229 from shrit/organize

Organize

.ci/linux-steps.yaml

@@ -66,26 +66,20 @@ steps:
 # Download datasets.
 - script: |
     python -m pip install tqdm requests
-    cd tools/
+    cd scripts/
     ./download_data_set.py
     cd ../
     ls data/
 
   displayName: "Download datasets"
 
-# Now build the examples.
+# Now build the examples, only c++.
 - script: |
-    for f in */Makefile; do
+    for f in cpp/*/*/*; do
       dir=`dirname $f`;
 
       # TODO: the dataset cannot be loaded.
-      if [ "$dir" == "mnist_vae_cnn" ];
-      then
-        continue;
-      fi
-
-      # TODO: the dataset cannot be loaded.
-      if [ "$dir" == "spam" ];
+      if [ "$dir" == "c++/neural_networks/mnist_vae_cnn" ];
       then
         continue;
       fi
@@ -102,7 +96,7 @@ steps:
       else
         echo "Example succeeded; ${PIPESTATUS[0]}, ${PIPESTATUS[1]}";
       fi
-      cd ../;
+      cd ../../../;
     done
 
     # Print any failures.

.ci/macos-steps.yaml

@@ -70,28 +70,22 @@ steps:
 # Download datasets.
 - script: |
     python -m pip install tqdm requests
-    cd tools/
+    cd scripts/
     ./download_data_set.py
     cd ../
     ls data/
 
   displayName: "Download datasets"
 
-# Now build the examples.
+# Now build the examples, only c++.
 - script: |
-    for f in */Makefile; do
+    for f in cpp/*/*/*; do
       dir=`dirname $f`;
       # Remove the -fopenmp flag from the Makefile.
       sed -i '' 's/ -fopenmp//' $f;
 
       # TODO: the dataset cannot be loaded.
-      if [ "$dir" == "mnist_vae_cnn" ];
-      then
-        continue;
-      fi
-
-      # TODO: the dataset cannot be loaded.
-      if [ "$dir" == "spam" ];
+      if [ "$dir" == "c++/neural_networks/mnist_vae_cnn" ];
       then
         continue;
       fi
@@ -108,7 +102,7 @@ steps:
       else
         echo "Example succeeded; ${PIPESTATUS[0]}, ${PIPESTATUS[1]}";
       fi
-      cd ../;
+      cd ../../../;
     done
 
     # Print any failures.

.gitignore

@@ -12,4 +12,10 @@ cmake-build-*
 *.so
 data
 plots
-*__pycache__*
+mnist_*
+nn_*
+cifar_*
+covertype-rf
+dominant_colors
+pima_indians_diabetes
+*__pycache__*

README.md

@@ -1,19 +1,22 @@
 The mlpack **examples** repository contains simple example usages of mlpack.
 You can take the code here and adapt it into your application, or compile it and
-see what it does and play with it.
-
-Each of the examples are meant to be as simple as possible, and they are
-extensively documented.
+see what it does and play with it. Each of the examples are meant to be as
+simple as possible, and they are extensively documented.
 
 All the notebooks in this repository can be easily run on
 https://lab.mlpack.org/.
 
+mlpack is a C++ library that provides machine learning support, but it also
+provides bindings to other languages, including Python and Julia, and it also
+provides command-line programs, see the [main mlpack
+repository](https://github.com/mlpack/mlpack) and [mlpack
+website](https://www.mlpack.org/) for more information on how to install mlpack.
+
 ### 0. Contents
 
   1. [Overview](#1-overview)
   2. [Building the examples and usage](#2-Building-the-examples-and-usage)
-  3. [List of examples](#3-List-of-examples)
-  4. [Datasets](#4-datasets)
+  3. [Datasets](#3-datasets)
 
 ###  1. Overview
 
@@ -23,73 +26,44 @@ for your machine learning task, this repository would definitely be a good place
 to look, along with the [mlpack
 documentation](https://www.mlpack.org/docs.html).
 
-mlpack is a C++ library that provides machine learning support, but it also
-provides bindings to other languages, including Python and Julia, and it also
-provides command-line programs.
+Therefore, this repository contains examples that are using common datasets.
+This repository is organized per language and per method that is used. 
 
-Therefore, this repository contains examples not just in C++ but also in other
-languages.  C++ mlpack usage examples are contained in the `c++/` directory;
-Python examples in the `python/` directory, command-line examples in the
-`command-line/` directory, and so forth.
+* `cpp/`: various mlpack C++ examples showing different machine learning
+  algorithms. 
+* `jupyter_notebook/`: mlpack examples C++ or Python written in jupyter
+  notebook format.
+* `embedded/`: directory contains mlpack C++ examples with more focus on
+  embedded system in the case of compilation and optimized binary and sensor
+  input. 
+* `cli/` directory contains mlpack methods executed directly from the terminal
+  command line, suitable if you have a ready to use dataset and you do not want
+  to jump to the code.
 
 ### 2. Building the examples and usage
 
 In order to keep this repository as simple as possible, there is no build
 system, and all examples are minimal.  For the C++ examples, there is a Makefile
 in each example's directory; if you have mlpack installed on your system,
-running `make` should work fine.  Some other examples may also use other
+running `make` should work fine.  Rarely, some other examples may also use other
 libraries, and the Makefile expects those dependencies to also be available.
-See the README in each directory for more information, and see the [main mlpack
-repository](https://github.com/mlpack/mlpack) and [mlpack
-website](https://www.mlpack.org/) for more information on how to install mlpack.
-
-For Python examples and other-language examples, it's expected that you have
-mlpack and its dependencies installed.
+See the README in each directory for more information, For Python examples and
+other-language examples, it's expected that you have mlpack and its
+dependencies installed.
 
 Each example should be easily runnable and should perform a simple machine
 learning task on a dataset.  You might need to download the dataset first---so
 be sure to check any README for the example.
 
-### 3. List of examples
-
-Below is a list of examples available in this repository along with a quick
-description (just a little bit more than the title):
-
- - `lstm_electricity_consumption`: use an LSTM-based recurrent neural network to
-   predict electricity consumption
-
- - `lstm_stock_prediction`: predict Google's historical stock price (daily high
-   _and_ low) using an LSTM-based recurrent neural network
-
- - `spam`: predict whether a mobile phone text message in Indonesian is spam 
-    or not using logistic regression
-
- - `mnist_batch_norm`: use batch normalization in a simple feedforward neural
-   network to recognize the MNIST digits
-
- - `mnist_cnn`: use a convolutional neural network (CNN) similar to LeNet-5 to
-   recognize the MNIST digits
-
- - `mnist_simple`: use a very simple three-layer feedforward neural network with
-   dropout to recognize the MNIST digits
-
- - `mnist_vae_cnn`: use a variational autoencoder with convolutional neural
-   networks in the encoder and reparametrization networks to recognize the MNIST
-   digits
-
- - `neural_network_regression`: use neural network to do regression on Body fat 
-    dataset
-
- - `q_learning`: train a simple deep Q-network agent on CartPole environment
-
-### 4. Datasets
+### 3. Datasets
 
 All the required dataset needed by the examples can be downloaded using the
-provided script in the `tools` directory. You will have to execute
-`download_data_set.py` from the `tools/` directory and it will download and
-extract all the necessary dataset in order for examples to work perfectly:
+provided script in the `scripts` directory. You will have to execute
+`download_data_set.py` from the `scripts/` directory and it will download and
+extract all the necessary datasets inside the `data/` directory in order for
+examples to work perfectly:
 
 ```sh
-cd tools/
+cd scripts/
 ./download_data_set.py
 ```

appveyor.yml

@@ -8,14 +8,10 @@ environment:
      #  Architecture: x86_amd64
      #  VCPREFIX:'C:\Program Files (x86)\Microsoft Visual Studio 14.0'
      #  VCVARS: 'VC\vcvarsall.bat'
-     #  BOOST_ROOT: C:\Libraries\boost_1_69_0
-     #  BOOST_LIBRARYDIR: C:\Libraries\boost_1_69_0\lib64-msvc-14.0
      - APPVEYOR_BUILD_WORKER_IMAGE: Visual Studio 2017
        VSVER: Visual Studio 15 2017 Win64
        VCPREFIX: 'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community'
        VCVARS: 'VC/Auxiliary/Build/vcvars64.bat'
-       BOOST_ROOT: C:\Libraries\boost_1_69_0
-       BOOST_LIBRARYDIR: C:\Libraries\boost_1_69_0\lib64-msvc-14.0
 
 configuration: Release
 
@@ -36,8 +32,6 @@ build_script:
     cmake -G "%VSVER%"
     -DDEBUG=OFF -DPROFILE=OFF
     -DUSE_OPENMP=OFF
-    -DBOOST_ROOT=%BOOST_ROOT%
-    -DBOOST_LIBRARYDIR=%BOOST_LIBRARYDIR%
     -DBUILD_CLI_EXECUTABLES=OFF
     -DBUILD_JULIA_BINDINGS=OFF
     -DBUILD_PYTHON_BINDINGS=OFF
@@ -55,8 +49,6 @@ build_script:
 #  - >
 #    cmake -G "%VSVER%"
 #    -DDEBUG=OFF -DPROFILE=OFF
-#    -DBOOST_ROOT=%BOOST_ROOT%
-#    -DBOOST_LIBRARYDIR=%BOOST_LIBRARYDIR%
 #    -DMLPACK_LIBRARY:FILEPATH=%MLPACK_LIB%
 #    -DMLPACK_INCLUDE_DIR:PATH=%MLPACK_INCLUDE%
 #    -DBUILD_PYTHON_BINDINGS=OFF -DCMAKE_BUILD_TYPE=Release