KNN.py

import numpy as np
from math import sqrt
from collections import Counter
from metrics import accuracy_score

class KNNClassifier:

    def __init__(self, k):
        """初始化KNN分类器"""
        assert k >= 1, "K must be valid."
        self.k = k
        self._X_train = None;
        self._y_train = None;

    def fit(self, X_train, y_train):
        """根据训练数据集 X_train 和 y_train 训练KNN分类器"""
        assert X_train.shape[0] == y_train.shape[0], \
            "the size of X_train must be equal to the size of y_train."
        assert self.k <= X_train.shape[0], \
            "the size of X_train must be at least k."

        self._X_train = X_train
        self._y_train = y_train
        return self

    def predict(self, X_predict):
        """给定特定预测数据集X_predict, 返回表示X_predict的结果向量"""
        assert self._X_train is not None and self._y_train is not None, \
            "must fit before predict!"
        assert X_predict.shape[1] == self._X_train.shape[1], \
            "the feature number of X_predict must be equal to X_train"

        y_predict = [self._predict(x) for x in X_predict]

        return np.array(y_predict)

    def _predict(self, x):
        """给定单个带预测数据 x, 返回 x 的预测结果值"""
        assert x.shape[0] == self._X_train.shape[1], \
            "the feature number of x must be equal to X_train"

        distances = [sqrt(np.sum((x_train - x) ** 2)) for x_train in self._X_train]
        nearest = np.argsort(distances)

        topK_y = [self._y_train[i] for i in nearest[:self.k]]
        votes = Counter(topK_y)

        return votes.most_common(1)[0][0]

    def score(self, X_test, y_test):
        '''根据测试数据集 X_test 和 y_test 确定当前模型的准确度'''
        y_predict = self.predict(X_test)
        return accuracy_score(y_test, y_predict)


    def __repr__(self):
        return "KNN(k=%d)" % self.k