本文共 2630 字，大约阅读时间需要 8 分钟。

自己参考Bobo老师写得代码：

主要分为四个文件： knn.py中实现KNN算法、model_selection.py封装了样本数据的一些工具方法，比如切分为训练集和测试集；

metrics用来对模型进行评估、client用来调用算法进行运行

# -*- encoding: utf-8 -*-"""实现KNN的分类算法"""import numpy as npfrom math import sqrtfrom collections import Counterfrom metrics import accuracy_scoreclass KnnClassifier(object):    """    K-近邻算法,（K Nearest Neighbour），简称KNN    """    def __init__(self, k):        """        K表示        :param k: 表示参考的个数        """        self.k = k    def fit(self, X_train, y_train):        """        利用输入的样本集进行训练KNN算法        :param X_train: X 训练样本集        :param y_train: y        :return:        """        self.X_train = X_train        self.y_train = y_train        return self    def predict(self, x_test):        """        对于输入的测试样本x进行预测        :param x_test: 这个一个行向量        :return:        """        assert x_test.shape[1] == self.X_train.shape[1], u'预测样本和训练样本的特征值不相等'        # step1 用欧几里得算法计算x与周边的距离        pridect_list = []        for one_x in x_test:            distances = [sqrt(np.sum((x - one_x) ** 2)) for x in self.X_train]            sorted_index = np.argsort(distances)            fit_y = self.y_train[sorted_index[:self.k]]            target_label = Counter(fit_y).most_common()[0][0]            pridect_list.append(target_label)        return np.asarray(pridect_list, dtype='int32')    def scores(self, y_pridect, y_test):        return accuracy_score(y_pridect, y_test)    def __repr__(self):        return 'knn(k=%s)' % self.k

Metrics文件：

# -*- encoding: utf-8 -*-"""这个文件主要是计算一些指标比如准确度，用来评估算法的好还"""import numpy as npdef accuracy_score(y_test, y_pridect):    """    用来计算准确度    :param y_test: 样本的标记测试集和    :param y_pridect: 预测集    :return:    """    assert y_pridect.shape[0] == y_test.shape[0], u'测试集和预测集的数据个数不相等'    cnt = np.sum(y_test==y_pridect)    return cnt / len(y_pridect)

model_selection.py文件：

# -*- encoding: utf-8 -*-"""这个文件主要是计算一些指标比如准确度，用来评估算法的好还"""import numpy as npdef accuracy_score(y_test, y_pridect):    """    用来计算准确度    :param y_test: 样本的标记测试集和    :param y_pridect: 预测集    :return:    """    assert y_pridect.shape[0] == y_test.shape[0], u'测试集和预测集的数据个数不相等'    cnt = np.sum(y_test==y_pridect)    return cnt / len(y_pridect)

client文件进行测试：

from knn import KnnClassifierfrom sklearn import datasetsfrom model_selection import train_test_splitfrom metrics import accuracy_scoreimport numpy as npif __name__ == '__main__':    knn = KnnClassifier(3)    iris = datasets.load_iris()    x_train, y_train, x_test, y_test = train_test_split(iris.data, iris.target, 0.7)    classifier = knn.fit(x_train, y_train)    y_pridect = classifier.predict(x_test)    print(classifier.scores(y_pridect, y_test))