EM 算法

# !/usr/bin/python# -*- coding:utf-8 -*-import numpy as npimport pandas as pdfrom sklearn.mixture import GaussianMixtureimport matplotlib as mplimport matplotlib.colorsimport matplotlib.pyplot as pltfrom sklearn.metrics.pairwise import pairwise_distances_argminmpl.rcParams<span style="font.sans-serif">] = ['SimHei'</span>mpl.rcParams['axes.unicode_minus'] = Falseiris_feature = '花萼长度', '花萼宽度', '花瓣长度', '花瓣宽度'def expand(a, b, rate=0.05):    d = (b - a) * rate    return a-d, b+dif __name__ == '__main__':    path = 'iris.data'    data = pd.read_csv(path, header=None)    x_prime = data[np.arange(4)]    y = pd.Categorical(data[4]).codes    n_components = 3    feature_pairs = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]    plt.figure(figsize=(8, 6), facecolor='w')    for k, pair in enumerate(feature_pairs, start=1):        x = x_prime[pair]        m = np.array([np.mean(x[y == i], axis=0) for i in range(3)])  # 均值的实际值        print('实际均值 = \n', m)        gmm = GaussianMixture(n_components=n_components, covariance_type='full', random_state=0)        gmm.fit(x)        print('预测均值 = \n', gmm.means_)        print('预测方差 = \n', gmm.covariances_)        y_hat = gmm.predict(x)        order = pairwise_distances_argmin(m, gmm.means_, axis=1, metric='euclidean')        print('顺序：\t', order)        n_sample = y.size        n_types = 3        change = np.empty((n_types, n_sample), dtype=np.bool)        for i in range(n_types):            change[i] = y_hat == order[i]        for i in range(n_types):            y_hat[change[i]] = i        acc = '准确率：%.2f%%' % (100*np.mean(y_hat == y))        print(acc)        cm_light = mpl.colors.ListedColormap(<span style="color:#FF8080">, '#77E0A0', '#A0A0FF'</span>)        cm_dark = mpl.colors.ListedColormap(<span style="r">, 'g', '#6060FF'</span>)        x1_min, x2_min = x.min()        x1_max, x2_max = x.max()        x1_min, x1_max = expand(x1_min, x1_max)        x2_min, x2_max = expand(x2_min, x2_max)        x1, x2 = np.mgrid[x1_min:x1_max:200j, x2_min:x2_max:200j]
        grid_test = np.stack((x1.flat, x2.flat), axis=1)        grid_hat = gmm.predict(grid_test)        change = np.empty((n_types, grid_hat.size), dtype=np.bool)        for i in range(n_types):            change[i] = grid_hat == order[i]        for i in range(n_types):            grid_hat[change[i]] = i        grid_hat = grid_hat.reshape(x1.shape)        plt.subplot(2, 3, k)        plt.pcolormesh(x1, x2, grid_hat, cmap=cm_light)        plt.scatter(x[pair[0]], x[pair[1]], s=20, c=y, marker='o', cmap=cm_dark, edgecolors='k')        xx = 0.95 * x1_min + 0.05 * x1_max        yy = 0.1 * x2_min + 0.9 * x2_max        plt.text(xx, yy, acc, fontsize=10)        plt.xlim((x1_min, x1_max))        plt.ylim((x2_min, x2_max))        plt.xlabel(iris_feature[pair[0]], fontsize=11)        plt.ylabel(iris_feature[pair[1]], fontsize=11)        plt.grid(b=True, ls=':', color='#606060')    plt.suptitle('EM算法无监督分类鸢尾花数据', fontsize=14)    plt.tight_layout(1, rect=(0, 0, 1, 0.95))    plt.show()