# coding=utf-8

'''
@author: wepon, http://2hwp.com
Reference:
            Book: <<Machine Learning in Action>>
            Software: sklearn.cluster.KMeans
'''
import numpy as np


class KMeans(object):
    """
    - 参数
        n_clusters:
            聚类个数，即k
        initCent:
            质心初始化方式，可选"random"或指定一个具体的array,默认random，即随机初始化
        max_iter:
            最大迭代次数
    """

    def __init__(self, n_clusters=5, initCent='random', max_iter=300):
        if hasattr(initCent, '__array__'):
            n_clusters = initCent.shape[0]
            self.centroids = np.asarray(initCent, dtype=np.float)
        else:
            self.centroids = None

        self.n_clusters = n_clusters
        self.max_iter = max_iter
        self.initCent = initCent
        self.clusterAssment = None
        self.labels = None
        self.sse = None

        # 计算两点的欧式距离

    def _distEclud(self, vecA, vecB):
        return np.linalg.norm(vecA - vecB)

    # 随机选取k个质心,必须在数据集的边界内
    def _randCent(self, X, k):
        n = X.shape[1]  # 特征维数
        centroids = np.empty((k, n))  # k*n的矩阵，用于存储质心
        for j in range(n):  # 产生k个质心，一维一维地随机初始化
            minJ = min(X[:, j])
            rangeJ = float(max(X[:, j]) - minJ)
            centroids[:, j] = (minJ + rangeJ * np.random.rand(k, 1)).flatten()
        return centroids

    def fit(self, X):
        # 类型检查
        if not isinstance(X, np.ndarray):
            try:
                X = np.asarray(X)
            except:
                raise TypeError("numpy.ndarray required for X")

        m = X.shape[0]  # m代表样本数量
        self.clusterAssment = np.empty((m, 2))  # m*2的矩阵，第一列存储样本点所属的族的索引值，
        # 第二列存储该点与所属族的质心的平方误差
        if self.initCent == 'random':
            self.centroids = self._randCent(X, self.n_clusters)

        clusterChanged = True
        for _ in range(self.max_iter):
            clusterChanged = False
            for i in range(m):  # 将每个样本点分配到离它最近的质心所属的族
                minDist = np.inf;
                minIndex = -1
                for j in range(self.n_clusters):
                    distJI = self._distEclud(self.centroids[j, :], X[i, :])
                    if distJI < minDist:
                        minDist = distJI;
                        minIndex = j
                if self.clusterAssment[i, 0] != minIndex:
                    clusterChanged = True
                    self.clusterAssment[i, :] = minIndex, minDist ** 2

            if not clusterChanged:  # 若所有样本点所属的族都不改变,则已收敛，结束迭代
                break
            for i in range(self.n_clusters):  # 更新质心，即将每个族中的点的均值作为质心
                ptsInClust = X[np.nonzero(self.clusterAssment[:, 0] == i)[0]]  # 取出属于第i个族的所有点
                self.centroids[i, :] = np.mean(ptsInClust, axis=0)

        self.labels = self.clusterAssment[:, 0]
        self.sse = sum(self.clusterAssment[:, 1])

    def predict(self, X):  # 根据聚类结果，预测新输入数据所属的族
        # 类型检查
        if not isinstance(X, np.ndarray):
            try:
                X = np.asarray(X)
            except:
                raise TypeError("numpy.ndarray required for X")

        m = X.shape[0]  # m代表样本数量
        preds = np.empty((m,))
        for i in range(m):  # 将每个样本点分配到离它最近的质心所属的族
            minDist = np.inf
            for j in range(self.n_clusters):
                distJI = self._distEclud(self.centroids[j, :], X[i, :])
                if distJI < minDist:
                    minDist = distJI
                    preds[i] = j
        return preds


class biKMeans(object):
    def __init__(self, n_clusters=5):
        self.n_clusters = n_clusters
        self.centroids = None
        self.clusterAssment = None
        self.labels = None
        self.sse = None

    # 计算两点的欧式距离
    def _distEclud(self, vecA, vecB):
        return np.linalg.norm(vecA - vecB)

    def fit(self, X):
        m = X.shape[0]
        self.clusterAssment = np.zeros((m, 2))
        centroid0 = np.mean(X, axis=0).tolist()
        centList = [centroid0]
        for j in range(m):  # 计算每个样本点与质心之间初始的平方误差
            self.clusterAssment[j, 1] = self._distEclud(np.asarray(centroid0), X[j, :]) ** 2

        while (len(centList) < self.n_clusters):
            lowestSSE = np.inf
            for i in range(len(centList)):  # 尝试划分每一族,选取使得误差最小的那个族进行划分
                ptsInCurrCluster = X[np.nonzero(self.clusterAssment[:, 0] == i)[0], :]
                clf = KMeans(n_clusters=2)
                clf.fit(ptsInCurrCluster)
                centroidMat, splitClustAss = clf.centroids, clf.clusterAssment  # 划分该族后，所得到的质心、分配结果及误差矩阵
                sseSplit = sum(splitClustAss[:, 1])
                sseNotSplit = sum(self.clusterAssment[np.nonzero(self.clusterAssment[:, 0] != i)[0], 1])
                if (sseSplit + sseNotSplit) < lowestSSE:
                    bestCentToSplit = i
                    bestNewCents = centroidMat
                    bestClustAss = splitClustAss.copy()
                    lowestSSE = sseSplit + sseNotSplit
            # 该族被划分成两个子族后,其中一个子族的索引变为原族的索引，另一个子族的索引变为len(centList),然后存入centList
            bestClustAss[np.nonzero(bestClustAss[:, 0] == 1)[0], 0] = len(centList)
            bestClustAss[np.nonzero(bestClustAss[:, 0] == 0)[0], 0] = bestCentToSplit
            centList[bestCentToSplit] = bestNewCents[0, :].tolist()
            centList.append(bestNewCents[1, :].tolist())
            self.clusterAssment[np.nonzero(self.clusterAssment[:, 0] == bestCentToSplit)[0], :] = bestClustAss

        self.labels = self.clusterAssment[:, 0]
        self.sse = sum(self.clusterAssment[:, 1])
        self.centroids = np.asarray(centList)

    def predict(self, X):  # 根据聚类结果，预测新输入数据所属的族
        # 类型检查
        if not isinstance(X, np.ndarray):
            try:
                X = np.asarray(X)
            except:
                raise TypeError("numpy.ndarray required for X")

        m = X.shape[0]  # m代表样本数量
        preds = np.empty((m,))
        for i in range(m):  # 将每个样本点分配到离它最近的质心所属的族
            minDist = np.inf
            for j in range(self.n_clusters):
                distJI = self._distEclud(self.centroids[j, :], X[i, :])
                if distJI < minDist:
                    minDist = distJI
                    preds[i] = j
        return preds