Kmean分類

項目鏈接：https://github.com/Wchenguang/gglearn/blob/master/KmeansClassifier/講解/KmeansClassifier.ipynb

1. 首先, 隨機確定 K 個初始點作為質心（不必是數據中的點）。
2. 然后將數據集中的每個點分配到一個簇中, 具體來講, 就是為每個點找到距其最近的質心, 并將其分配該質心所對應的簇. 這一步完成之后, 每個簇的質心更新為該簇所有點的平均值.
3. 重復上述過程直到數據集中的所有點都距離它所對應的質心最近時結束。

            
              import numpy as np
import matplotlib.pyplot as plt

class KmeansClassifier:
    def __init__(self, k, distance_method = "o", random_select = True, 
                    plot = False):
        self.k = k
        self.distance_method = distance_method
        self.random_select = random_select
        self.plot = plot
        if(distance_method == "o"):
            self._dist = self._euler_dist
        else:
            self._dist = self._manhattan_distance
            
    def _euler_dist(self, x1, x):
        return np.sqrt(np.multiply(x-x1, x-x1).sum())
    
    def _manhattan_distance(self, x1, x):
        return np.abs(x-x1).sum()
    
    def _get_nearest(self, x, center_list, dist):
        dists = []
        for center in center_list:
            dists.append(dist(x, center))
        return dists.index(min(dists))
    
    def _fit(self, x, y, dist, x_center_index_list, center_list):
        xy_map = np.hstack((x, x_center_index_list, x_center_index_list))
        for row in xy_map:
            row[-1] = self._get_nearest(row[:-2], center_list, dist)
        flag = np.all(xy_map[:, -1] == xy_map[:, -2])
        return flag, xy_map[:, -1].reshape(-1, 1), center_list
    
    def _random_center_list(self, x, k):
        center_list = np.zeros((x.shape[1], k))
        for col in range(x.shape[1]):
            col_max = np.max(x[:, col])
            col_min = np.min(x[:, col])
            center_list[col, :] = col_min + (col_max - col_min) * np.random.rand(1, k)
        return center_list.T
    
    def _updata_center_list(self, x, x_center_index_list, center_list):
        new_center_list = []
        for index in range(len(center_list)):
            part_x = x[np.where(
                    x_center_index_list[:, -1] == index)]
            if(0 != part_x.size):
                new_center_list.append(np.mean(part_x, axis = 0))
            else:
                new_center_list.append(np.zeros(part_x.shape[1]))
        return new_center_list
    
    def _plot(self, x, x_center_index_list, center_list):
        '''
        數據繪制，只能繪制二維
        '''
        center_array = np.array(center_list)
        for index in range(len(center_list)):
            part_x = x[np.where(
                    x_center_index_list[:, -1] == index)]
            plt.scatter(part_x[:, 0], part_x[:, 1])
        plt.scatter(center_array[:, 0], center_array[:, 1], marker = "+")
        plt.show()
    
    def fit(self, x, y, center_list = None):
        if not center_list:
            center_list = self._random_center_list(x, self.k)
        x_center_index_list = np.zeros(x.shape[0]).reshape(-1, 1)
        flag = False
        while(True):
            flag, x_center_index_list, center_list = self._fit(x, 
                                        y, self._dist, x_center_index_list, center_list)
            if(flag):
                break
            center_list = self._updata_center_list(x, x_center_index_list,
                                                      center_list)
            if(self.plot):
                self._plot(x, x_center_index_list, center_list)
        return self

x = np.random.randint(1, 100, (50, 2))
y = np.random.randint(1,4,(10, 1))
kmeans_clf = KmeansClassifier(k = 4, plot=True)
kmeans_clf.fit(x, y)