K-Means 算法原理链接.

使用时，实例化类后，只需关注 fit(), predict()，传入数据类型为np.array，形状为 N x M。

class MyKMeans:
    labels_ = []  # fit 后每类数据的标签
    cluster_centers_ = None  # N x M, 聚类中心个数
    __cluster_centers_dict = dict()

    def __init__(self, n_clusters=3, n_init=3, max_iter=300):
        """
        :param n_clusters: 聚类中心个数
        :param n_init: 随机初始化几次聚类中心, 结果从最优中选择
        :param max_iter: 迭代次数
        """
        self.n_clusters = n_clusters
        self.n_init = n_init
        self.max_iter = max_iter
        for i in range(n_init):
            self.__cluster_centers_dict[str(i)] = None

    # 拟合
    def fit(self, X):
        # X.shape : N x M
        for i in tqdm(range(self.n_init)):
            self.__init_cluster_centers(X)
            sum_dis = 0
            for j in range(self.max_iter):
                sum_dis, _ = self.__distance(X)
                # print(_.shape)
                self.__change_cluster_centers(X)
            self.__cluster_centers_dict[str(i)] = [sum_dis, self.cluster_centers_, self.labels_]
        # print(list(self.__cluster_centers_dict.items())[0][1][0])
        self.cluster_centers_, self.labels_ = \
            sorted(list(self.__cluster_centers_dict.items()), key=lambda item: item[1][0])[0][1][1:]

    # 预测
    def predict(self, x):
        # x.shape : N x M
        _, dis = self.__distance(x, is_fit=False)
        # print(dis.shape)
        return np.argmin(dis, axis=1)

    # 算距离
    def __distance(self, X, is_fit=True):
        distances = np.sum(np.power(X - self.cluster_centers_[:, np.newaxis, ...], 2), axis=2).T
        if is_fit:
            self.labels_ = np.argmin(distances, axis=1)
        return np.sum(distances), distances

    # 修改聚类中心
    def __change_cluster_centers(self, X):
        new_cc = np.zeros(self.cluster_centers_.shape)
        sum_l = np.zeros(self.cluster_centers_.shape[0])
        for i in range(len(self.labels_)):
            # print(new_cc.shape, X.shape, new_cc[self.labels_[i]].shape, X[i].shape, new_cc[self.labels_[i]], X[i])
            new_cc[self.labels_[i]] = new_cc[self.labels_[i]] + X[i]
            sum_l[self.labels_[i]] += 1
        sum_l = sum_l[:, np.newaxis]
        sum_l = np.repeat(sum_l, repeats=new_cc.shape[1], axis=1)
        self.cluster_centers_ = new_cc / sum_l

    # 随机初始化聚类中心
    def __init_cluster_centers(self, X):
        indexs = random.sample(range(0, X.shape[0]), self.n_clusters)
        self.cluster_centers_ = np.array([X[i] for i in indexs])