非监督学习之聚类

我就是我 2024-04-18 10:46 90阅读 0赞

# 1.概念 #

# 2.代码 #

kMeans.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sklearn.datasets as ds
    import matplotlib.colors
    from sklearn.cluster import KMeans
    
    
    def expand(a, b):
        d = (b - a) * 0.1
        return a-d, b+d
    
    
    if __name__ == "__main__":
        N = 400
        centers = 4
        data, y = ds.make_blobs(N, n_features=2, centers=centers, random_state=2)
        data2, y2 = ds.make_blobs(N, n_features=2, centers=centers, cluster_std=(1,2.5,0.5,2), random_state=2)
        data3 = np.vstack((data[y == 0][:], data[y == 1][:50], data[y == 2][:20], data[y == 3][:5]))
        y3 = np.array([0] * 100 + [1] * 50 + [2] * 20 + [3] * 5)
    
        cls = KMeans(n_clusters=4, init='k-means++')
        y_hat = cls.fit_predict(data)
        y2_hat = cls.fit_predict(data2)
        y3_hat = cls.fit_predict(data3)
    
        m = np.array(((1, 1), (1, 3)))
        data_r = data.dot(m)
        y_r_hat = cls.fit_predict(data_r)
    
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
        cm = matplotlib.colors.ListedColormap(list('rgbm'))
    
        plt.figure(figsize=(9, 10), facecolor='w')
        plt.subplot(421)
        plt.title(u'原始数据')
        plt.scatter(data[:, 0], data[:, 1], c=y, s=30, cmap=cm, edgecolors='none')
        x1_min, x2_min = np.min(data, axis=0)
        x1_max, x2_max = np.max(data, axis=0)
        x1_min, x1_max = expand(x1_min, x1_max)
        x2_min, x2_max = expand(x2_min, x2_max)
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(422)
        plt.title(u'KMeans++聚类')
        plt.scatter(data[:, 0], data[:, 1], c=y_hat, s=30, cmap=cm, edgecolors='none')
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(423)
        plt.title(u'旋转后数据')
        plt.scatter(data_r[:, 0], data_r[:, 1], c=y, s=30, cmap=cm, edgecolors='none')
        x1_min, x2_min = np.min(data_r, axis=0)
        x1_max, x2_max = np.max(data_r, axis=0)
        x1_min, x1_max = expand(x1_min, x1_max)
        x2_min, x2_max = expand(x2_min, x2_max)
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(424)
        plt.title(u'旋转后KMeans++聚类')
        plt.scatter(data_r[:, 0], data_r[:, 1], c=y_r_hat, s=30, cmap=cm, edgecolors='none')
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(425)
        plt.title(u'方差不相等数据')
        plt.scatter(data2[:, 0], data2[:, 1], c=y2, s=30, cmap=cm, edgecolors='none')
        x1_min, x2_min = np.min(data2, axis=0)
        x1_max, x2_max = np.max(data2, axis=0)
        x1_min, x1_max = expand(x1_min, x1_max)
        x2_min, x2_max = expand(x2_min, x2_max)
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(426)
        plt.title(u'方差不相等KMeans++聚类')
        plt.scatter(data2[:, 0], data2[:, 1], c=y2_hat, s=30, cmap=cm, edgecolors='none')
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(427)
        plt.title(u'数量不相等数据')
        plt.scatter(data3[:, 0], data3[:, 1], s=30, c=y3, cmap=cm, edgecolors='none')
        x1_min, x2_min = np.min(data3, axis=0)
        x1_max, x2_max = np.max(data3, axis=0)
        x1_min, x1_max = expand(x1_min, x1_max)
        x2_min, x2_max = expand(x2_min, x2_max)
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.subplot(428)
        plt.title(u'数量不相等KMeans++聚类')
        plt.scatter(data3[:, 0], data3[:, 1], c=y3_hat, s=30, cmap=cm, edgecolors='none')
        plt.xlim((x1_min, x1_max))
        plt.ylim((x2_min, x2_max))
        plt.grid(True)
    
        plt.tight_layout(2)
        plt.suptitle(u'数据分布对KMeans聚类的影响', fontsize=18)
        # https://github.com/matplotlib/matplotlib/issues/829
        plt.subplots_adjust(top=0.92)
        plt.show()

criteria,py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    from sklearn import metrics
    
    
    if __name__ == "__main__":
        y = [0, 0, 0, 1, 1, 1]
        y_hat = [0, 0, 1, 1, 2, 2]
        h = metrics.homogeneity_score(y, y_hat)
        c = metrics.completeness_score(y, y_hat)
        print u'同一性(Homogeneity)：', h
        print u'完整性(Completeness)：', c
        v2 = 2 * c * h / (c + h)
        v = metrics.v_measure_score(y, y_hat)
        print u'V-Measure：', v2, v
    
        print
        y = [0, 0, 0, 1, 1, 1]
        y_hat = [0, 0, 1, 2, 3, 3]
        h = metrics.homogeneity_score(y, y_hat)
        c = metrics.completeness_score(y, y_hat)
        v = metrics.v_measure_score(y, y_hat)
        print u'同一性(Homogeneity)：', h
        print u'完整性(Completeness)：', c
        print u'V-Measure：', v
    
        # 允许不同值
        print
        y = [0, 0, 0, 1, 1, 1]
        y_hat = [1, 1, 1, 0, 0, 0]
        h = metrics.homogeneity_score(y, y_hat)
        c = metrics.completeness_score(y, y_hat)
        v = metrics.v_measure_score(y, y_hat)
        print u'同一性(Homogeneity)：', h
        print u'完整性(Completeness)：', c
        print u'V-Measure：', v
    
        y = [0, 0, 1, 1]
        y_hat = [0, 1, 0, 1]
        ari = metrics.adjusted_rand_score(y, y_hat)
        print ari
    
        y = [0, 0, 0, 1, 1, 1]
        y_hat = [0, 0, 1, 1, 2, 2]
        ari = metrics.adjusted_rand_score(y, y_hat)
        print ari

向量量化  
VetorQuantization.py

# !/usr/bin/python
    # -*- coding: utf-8 -*-
    
    from PIL import Image
    import numpy as np
    from sklearn.cluster import KMeans
    import matplotlib
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    
    
    def restore_image(cb, cluster, shape):
        row, col, dummy = shape
        image = np.empty((row, col, 3))
        index = 0
        for r in range(row):
            for c in range(col):
                image[r, c] = cb[cluster[index]]
                index += 1
        return image
    
    
    def show_scatter(a):
        N = 10
        print '原始数据：\n', a
        density, edges = np.histogramdd(a, bins=[N,N,N], range=[(0,1), (0,1), (0,1)])
        density /= density.max()
        x = y = z = np.arange(N)
        d = np.meshgrid(x, y, z)
    
        fig = plt.figure(1, facecolor='w')
        ax = fig.add_subplot(111, projection='3d')
        ax.scatter(d[1], d[0], d[2], c='r', s=100*density, marker='o', depthshade=True)
        ax.set_xlabel(u'红色分量')
        ax.set_ylabel(u'绿色分量')
        ax.set_zlabel(u'蓝色分量')
        plt.title(u'图像颜色三维频数分布', fontsize=20)
    
        plt.figure(2, facecolor='w')
        den = density[density > 0]
        den = np.sort(den)[::-1]
        t = np.arange(len(den))
        plt.plot(t, den, 'r-', t, den, 'go', lw=2)
        plt.title(u'图像颜色频数分布', fontsize=18)
        plt.grid(True)
    
        plt.show()
    
    
    if __name__ == '__main__':
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
    
        num_vq = 50
        im = Image.open('16.lena.png')     # 16.son.bmp(100)/16.flower2.png(200)/16.son.png(60)/16.lena.png(50)
        image = np.array(im).astype(np.float) / 255
        image = image[:, :, :3]
        image_v = image.reshape((-1, 3))
        model = KMeans(num_vq)
        show_scatter(image_v)
    
        N = image_v.shape[0]    # 图像像素总数
        # 选择足够多的样本(如1000个)，计算聚类中心
        idx = np.random.randint(0, N, size=1000)
        image_sample = image_v[idx]
        model.fit(image_sample)
        c = model.predict(image_v)  # 聚类结果
        print '聚类结果：\n', c
        print '聚类中心：\n', model.cluster_centers_
    
        plt.figure(figsize=(15, 8), facecolor='w')
        plt.subplot(121)
        plt.axis('off')
        plt.title(u'原始图片', fontsize=18)
        plt.imshow(image)
        # plt.savefig('1.png')
    
        plt.subplot(122)
        vq_image = restore_image(model.cluster_centers_, c, image.shape)
        plt.axis('off')
        plt.title(u'矢量量化后图片：%d色' % num_vq, fontsize=18)
        plt.imshow(vq_image)
        # plt.savefig('2.png')
    
        plt.tight_layout(1.2)
        plt.show()

AP.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sklearn.datasets as ds
    import matplotlib.colors
    from sklearn.cluster import AffinityPropagation
    from sklearn.metrics import euclidean_distances
    
    
    if __name__ == "__main__":
        N = 400
        centers = [[1, 2], [-1, -1], [1, -1], [-1, 1]]
        data, y = ds.make_blobs(N, n_features=2, centers=centers, cluster_std=[0.5, 0.25, 0.7, 0.5], random_state=0)
        m = euclidean_distances(data, squared=True)
        preference = -np.median(m)
        print 'Preference：', preference
    
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(12, 9), facecolor='w')
        for i, mul in enumerate(np.linspace(1, 4, 9)):
            print mul
            p = mul * preference
            model = AffinityPropagation(affinity='euclidean', preference=p)
            af = model.fit(data)
            center_indices = af.cluster_centers_indices_
            n_clusters = len(center_indices)
            print ('p = %.1f' % mul), p, '聚类簇的个数为：', n_clusters
            y_hat = af.labels_
    
            plt.subplot(3, 3, i+1)
            plt.title(u'Preference：%.2f，簇个数：%d' % (p, n_clusters))
            clrs = []
            for c in np.linspace(16711680, 255, n_clusters):
                clrs.append('#%06x' % c)
            # clrs = plt.cm.Spectral(np.linspace(0, 1, n_clusters))
            for k, clr in enumerate(clrs):
                cur = (y_hat == k)
                plt.scatter(data[cur, 0], data[cur, 1], c=clr, edgecolors='none')
                center = data[center_indices[k]]
                for x in data[cur]:
                    plt.plot([x[0], center[0]], [x[1], center[1]], color=clr, zorder=1)
            plt.scatter(data[center_indices, 0], data[center_indices, 1], s=100, c=clrs, marker='*', edgecolors='k', zorder=2)
            plt.grid(True)
        plt.tight_layout()
        plt.suptitle(u'AP聚类', fontsize=20)
        plt.subplots_adjust(top=0.92)
        plt.show()

MeanShift.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sklearn.datasets as ds
    import matplotlib.colors
    from sklearn.cluster import MeanShift
    from sklearn.metrics import euclidean_distances
    
    
    if __name__ == "__main__":
        N = 1000
        centers = [[1, 2], [-1, -1], [1, -1], [-1, 1]]
        data, y = ds.make_blobs(N, n_features=2, centers=centers, cluster_std=[0.5, 0.25, 0.7, 0.5], random_state=0)
    
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10, 9), facecolor='w')
        m = euclidean_distances(data, squared=True)
        bw = np.median(m)
        print bw
        for i, mul in enumerate(np.linspace(0.1, 0.4, 4)):
            band_width = mul * bw
            model = MeanShift(bin_seeding=True, bandwidth=band_width)
            ms = model.fit(data)
            centers = ms.cluster_centers_
            y_hat = ms.labels_
            n_clusters = np.unique(y_hat).size
            print '带宽：', mul, band_width, '聚类簇的个数为：', n_clusters
    
            plt.subplot(2, 2, i+1)
            plt.title(u'带宽：%.2f，聚类簇的个数为：%d' % (band_width, n_clusters))
            clrs = []
            for c in np.linspace(16711680, 255, n_clusters):
                clrs.append('#%06x' % c)
            # clrs = plt.cm.Spectral(np.linspace(0, 1, n_clusters))
            print clrs
            for k, clr in enumerate(clrs):
                cur = (y_hat == k)
                plt.scatter(data[cur, 0], data[cur, 1], c=clr, edgecolors='none')
            plt.scatter(centers[:, 0], centers[:, 1], s=150, c=clrs, marker='*', edgecolors='k')
            plt.grid(True)
        plt.tight_layout(2)
        plt.suptitle(u'MeanShift聚类', fontsize=20)
        plt.subplots_adjust(top=0.92)
        plt.show()

DBSCAN.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sklearn.datasets as ds
    import matplotlib.colors
    from sklearn.cluster import DBSCAN
    from sklearn.preprocessing import StandardScaler
    
    
    def expand(a, b):
        d = (b - a) * 0.1
        return a-d, b+d
    
    
    if __name__ == "__main__":
        N = 1000
        centers = [[1, 2], [-1, -1], [1, -1], [-1, 1]]
        data, y = ds.make_blobs(N, n_features=2, centers=centers, cluster_std=[0.5, 0.25, 0.7, 0.5], random_state=0)
        data = StandardScaler().fit_transform(data)
        # 数据1的参数：(epsilon, min_sample)
        params = ((0.2, 5), (0.2, 10), (0.2, 15), (0.3, 5), (0.3, 10), (0.3, 15))
    
        # # 数据2
        # t = np.arange(0, 2*np.pi, 0.1)
        # data1 = np.vstack((np.cos(t), np.sin(t))).T
        # data2 = np.vstack((2*np.cos(t), 2*np.sin(t))).T
        # data3 = np.vstack((3*np.cos(t), 3*np.sin(t))).T
        # data = np.vstack((data1, data2, data3))
        # # 数据2的参数：(epsilon, min_sample)
        # params = ((0.5, 3), (0.5, 5), (0.5, 10), (1., 3), (1., 10), (1., 20))
    
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
    
        plt.figure(figsize=(12, 8), facecolor='w')
        plt.suptitle(u'DBSCAN聚类', fontsize=20)
    
        for i in range(6):
            eps, min_samples = params[i]
            model = DBSCAN(eps=eps, min_samples=min_samples)
            model.fit(data)
            y_hat = model.labels_
    
            core_indices = np.zeros_like(y_hat, dtype=bool)
            core_indices[model.core_sample_indices_] = True
    
            y_unique = np.unique(y_hat)
            n_clusters = y_unique.size - (1 if -1 in y_hat else 0)
            print y_unique, '聚类簇的个数为：', n_clusters
    
            # clrs = []
            # for c in np.linspace(16711680, 255, y_unique.size):
            #     clrs.append('#%06x' % c)
            plt.subplot(2, 3, i+1)
            clrs = plt.cm.Spectral(np.linspace(0, 0.8, y_unique.size))
            for k, clr in zip(y_unique, clrs):
                cur = (y_hat == k)
                if k == -1:
                    plt.scatter(data[cur, 0], data[cur, 1], s=20, c='k')
                    continue
                plt.scatter(data[cur, 0], data[cur, 1], s=30, c=clr, edgecolors='k')
                plt.scatter(data[cur & core_indices][:, 0], data[cur & core_indices][:, 1], s=60, c=clr, marker='o', edgecolors='k')
            x1_min, x2_min = np.min(data, axis=0)
            x1_max, x2_max = np.max(data, axis=0)
            x1_min, x1_max = expand(x1_min, x1_max)
            x2_min, x2_max = expand(x2_min, x2_max)
            plt.xlim((x1_min, x1_max))
            plt.ylim((x2_min, x2_max))
            plt.grid(True)
            plt.title(ur'$\epsilon$ = %.1f  m = %d，聚类数目：%d' % (eps, min_samples, n_clusters), fontsize=16)
        plt.tight_layout()
        plt.subplots_adjust(top=0.9)
        plt.show()

SC.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sklearn.datasets as ds
    import matplotlib.colors
    from sklearn.cluster import spectral_clustering
    from sklearn.metrics import euclidean_distances
    
    
    def expand(a, b):
        d = (b - a) * 0.1
        return a-d, b+d
    
    
    if __name__ == "__main__":
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
    
        t = np.arange(0, 2*np.pi, 0.1)
        data1 = np.vstack((np.cos(t), np.sin(t))).T
        data2 = np.vstack((2*np.cos(t), 2*np.sin(t))).T
        data3 = np.vstack((3*np.cos(t), 3*np.sin(t))).T
        data = np.vstack((data1, data2, data3))
    
        n_clusters = 3
        m = euclidean_distances(data, squared=True)
        sigma = np.median(m)
    
        plt.figure(figsize=(12, 8), facecolor='w')
        plt.suptitle(u'谱聚类', fontsize=20)
        clrs = plt.cm.Spectral(np.linspace(0, 0.8, n_clusters))
        for i, s in enumerate(np.logspace(-2, 0, 6)):
            print s
            af = np.exp(-m ** 2 / (s ** 2)) + 1e-6
            y_hat = spectral_clustering(af, n_clusters=n_clusters, assign_labels='kmeans', random_state=1)
            plt.subplot(2, 3, i+1)
            for k, clr in enumerate(clrs):
                cur = (y_hat == k)
                plt.scatter(data[cur, 0], data[cur, 1], s=40, c=clr, edgecolors='k')
            x1_min, x2_min = np.min(data, axis=0)
            x1_max, x2_max = np.max(data, axis=0)
            x1_min, x1_max = expand(x1_min, x1_max)
            x2_min, x2_max = expand(x2_min, x2_max)
            plt.xlim((x1_min, x1_max))
            plt.ylim((x2_min, x2_max))
            plt.grid(True)
            plt.title(ur'$\sigma$ = %.2f' % s, fontsize=16)
        plt.tight_layout()
        plt.subplots_adjust(top=0.9)
        plt.show()

SpactralCluaterImage.py

# !/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.colors
    from sklearn.cluster import spectral_clustering
    from sklearn.feature_extraction import image
    from PIL import Image
    import time
    
    
    if __name__ == "__main__":
        matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
        matplotlib.rcParams['axes.unicode_minus'] = False
    
        pic = Image.open('16.Chrome.png')
        pic = pic.convert('L')
        data = np.array(pic).astype(np.float) / 255
    
        plt.figure(figsize=(10, 5), facecolor='w')
        plt.subplot(121)
        plt.imshow(pic, cmap=plt.cm.gray, interpolation='nearest')
        plt.title(u'原始图片', fontsize=18)
        n_clusters = 15
    
        affinity = image.img_to_graph(data)
        beta = 3
        affinity.data = np.exp(-beta * affinity.data / affinity.data.std()) + 10e-5
        # a = affinity.toarray()
        # b = np.diag(a.diagonal())
        # a -= b
        print '开始谱聚类'
        y = spectral_clustering(affinity, n_clusters=n_clusters, assign_labels='kmeans', random_state=1)
        print '谱聚类完成'
        y = y.reshape(data.shape)
        for n in range(n_clusters):
            data[y == n] = n
        plt.subplot(122)
        clrs = []
        for c in np.linspace(16776960, 16711935, n_clusters):
            clrs.append('#%06x' % c)
        cm = matplotlib.colors.ListedColormap(clrs)
        plt.imshow(data, cmap=cm, interpolation='nearest')
        plt.title(u'谱聚类：%d簇' % n_clusters, fontsize=18)
        plt.tight_layout()
        plt.show()