import  matplotlib.pyplot as plt

import numpy as np

%config ZMQInteractiveShell.ast_node_interactivity='all'

def draw(X_train,y_train,X_new):

    # 正负实例点初始化

    X_po=np.zeros(X_train.shape[1])

    X_ne=np.zeros(X_train.shape[1])

    # 区分正、负实例点

    for i in range(y_train.shape[0]):

        if y_train[i]==1:

            X_po=np.vstack((X_po,X_train[i]))

        else:

            X_ne=np.vstack((X_ne,X_train[i]))

    # 实例点绘图

    plt.plot(X_po[1:,0],X_po[1:,1],"g*",label="")

    plt.plot(X_ne[1:, 0], X_ne[1:, 1], "rx", label="-1")

    plt.plot(X_new[:, 0], X_new[:, 1], "bo", label="test_points")

    # 测试点坐标值标注

    for xy in zip(X_new[:, 0], X_new[:, 1]):

        plt.annotate("test{}".format(xy),xy)

    # 设置坐标轴

    plt.axis([0,10,0,10])

    plt.xlabel("x1")

    plt.ylabel("x2")

    # 显示图例

    plt.legend()

    # 显示图像

    plt.show()

import numpy as np

from collections import Counter

class KNN:

    def __init__(self, X_train, y_train, k):

        # 所需要的参数初始化

        self.k = k

        self.X_train = X_train

        self.y_train = y_train

    def predict(self, X_new):

        # 计算欧式距离

        # np.linalg.norm() 表示求范数，ord = 2 表示求2阶范数

        # 得到的结果形式为：[(d0, 1), (d1, -1), ...]，其中d0和d1表示距离，1和-1表示标签

        dist_list = [(np.linalg.norm(X_new - self.X_train[i], ord = 2), self.y_train[i]) for i in range(self.X_train.shape[0])]

        # 对所有距离进行排序

        dist_list.sort(key = lambda x : x[0])

        # 取前k个最小距离对应的类型（也就是y值）

        y_list = [dist_list[i][-1] for i in range(self.k)]  # [-1, 1, 1 ,-1,...]

        # 对上述k个点的分类进行统计

        y_count = Counter(y_list).most_common()  # [(-1, 3), (1, 2)]

        return y_count[0][0]

def main():

    # 训练数据集

    X_train = np.array([

        [5,4],

        [9,6],

        [4,7],

        [2,3],

        [8,1],

        [7,2]

    ])

    # 标签

    y_train = np.array([1,1,1,-1,-1,-1])

    # 测试数据

    X_new = np.array([[5, 3]])

    # 绘图

    draw(X_train, y_train, X_new)

    # k取不同值对分类结果的影响

    for k in range(1,6,2):  # 表示1-5每隔2个取一个数

        # 构建KNN实例

        clf = KNN(X_train, y_train, k=k)

        # 对测试数据进行分类预测

        y_predict = clf.predict(X_new)

        print('k = {}，被分类为：{}'.format(k, y_predict))

if __name__ == '__main__':

    main()