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KMeans_世界观热点

2023-04-21 06:00:33 来源：博客园

(资料图片仅供参考)

from sklearn.datasets import make_blobsimport matplotlib.pyplot as plt

x, y = make_blobs(n_samples=500, n_features=2, centers=4, random_state=1)

color = ["red", "pink", "orange", "gray"]fig, ax1 = plt.subplots(1)for i in range(4):    ax1.scatter(x[y == i, 0], x[y == i, 1], marker="o", s=8, c=color[i])plt.show()

from sklearn.cluster import KMeansn_clusters = 3cluster = KMeans(n_clusters=n_clusters, n_init="auto", random_state=1).fit(x)

# 聚类预测结果y_predict = cluster.labels_y_predict

array([2, 2, 0, 1, 0, 1, 0, 0, 0, 0, 2, 2, 0, 1, 0, 2, 0, 2, 1, 0, 0, 0,       0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0,       0, 2, 0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,       2, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,       0, 0, 1, 2, 0, 0, 1, 2, 2, 0, 2, 1, 1, 2, 1, 0, 1, 0, 0, 1, 1, 0,       0, 2, 1, 0, 1, 0, 1, 0, 1, 0, 0, 2, 2, 0, 0, 0, 1, 2, 2, 0, 1, 0,       0, 0, 0, 2, 1, 0, 1, 1, 0, 2, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 1, 2,       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 2, 0, 0, 2, 1, 0,       0, 0, 0, 2, 0, 0, 1, 2, 2, 0, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 0, 2,       2, 1, 2, 0, 1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1,       0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 2, 0, 2, 0, 1, 1, 0, 2, 1, 2, 0, 0,       2, 2, 2, 2, 0, 0, 2, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,       1, 0, 2, 2, 0, 0, 0, 0, 1, 1, 0, 1, 0, 2, 1, 2, 1, 2, 2, 1, 2, 1,       1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 2, 0, 2,       2, 0, 1, 2, 0, 0, 1, 1, 0, 2, 1, 1, 0, 2, 1, 1, 0, 0, 1, 0, 0, 2,       2, 1, 0, 2, 0, 1, 1, 0, 0, 0, 2, 0, 1, 1, 0, 1, 1, 1, 1, 2, 2, 0,       1, 0, 0, 2, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 2, 1, 2, 2, 2, 2, 2,       2, 0, 2, 1, 2, 1, 1, 0, 1, 0, 0, 0, 2, 1, 0, 1, 0, 2, 0, 0, 2, 0,       0, 1, 1, 2, 0, 0, 1, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0, 2, 0, 1, 2, 1,       0, 0, 1, 0, 0, 1, 2, 0, 1, 1, 0, 0, 0, 0, 2, 1, 2, 0, 1, 2, 2, 2,       0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 0,       1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 1, 0, 2, 1, 2, 1, 2, 0, 1, 1,       2, 0, 0, 2, 0, 0, 0, 2, 0, 1, 0, 0, 2, 2, 2, 0], dtype=int32)

# 质心的位置centroid = cluster.cluster_centers_centroid

array([[-8.0807047 , -3.50729701],       [-1.54234022,  4.43517599],       [-7.11207261, -8.09458846]])

color = ["red", "pink", "orange", "gray"]fig, ax1 = plt.subplots(1)for i in range(n_clusters):    ax1.scatter(x[y_predict == i, 0], x[y_predict == i, 1], marker="o", s=8, c=color[i])ax1.scatter(centroid[:, 0], centroid[:, 1], marker="x", s=100, c="black")plt.show()

cluster.inertia_

1903.5607664611762

n_clusters = 4cluster = KMeans(n_clusters=n_clusters, n_init="auto", random_state=1).fit(x)cluster.inertia_

908.3855684760615

n_clusters = 100cluster = KMeans(n_clusters=n_clusters, n_init="auto", random_state=1).fit(x)cluster.inertia_

34.70849858088455

# 轮廓系数from sklearn.metrics import silhouette_scorefrom sklearn.metrics import silhouette_samples

silhouette_score(x, y_predict)

0.5882004012129721

silhouette_score(x, cluster.labels_)

0.3626791469009942

silhouette_samples(x, y_predict)

array([ 0.62982017,  0.5034877 ,  0.56148795,  0.84881844,  0.56034142,        0.78740319,  0.39254042,  0.4424015 ,  0.48582704,  0.41586457,        0.62497924,  0.75540751,  0.50080674,  0.8452256 ,  0.54730432,        0.60232423,  0.54574988,  0.68789747,  0.86605921,  0.25389678,        0.49316173,  0.47993065,  0.2222642 ,  0.8096265 ,  0.54091189,        0.30638567,  0.88557311,  0.84050532,  0.52855895,  0.49260117,        0.65291019,  0.85602282,  0.47734375,  0.60418857,  0.44210292,        0.6835351 ,  0.44776257,  0.423086  ,  0.6350923 ,  0.4060121 ,        0.54540657,  0.5628461 ,  0.78366733,  0.37063114,  0.35132112,        0.74493029,  0.53691616,  0.36724842,  0.87717083,  0.79594363,        0.84641859,  0.38341344,  0.42043012,  0.4024608 ,  0.64639537,        0.46244151,  0.31853572,  0.10047008,  0.37909034,  0.56424494,        0.86153448,  0.82630007,  0.53288582,  0.35699772,  0.86994617,        0.52259763,  0.71296285,  0.5269434 ,  0.42375504,  0.3173951 ,        0.67512993,  0.47574584,  0.44493897,  0.70152025,  0.37911024,        0.44338293,  0.75528756,  0.23339973,  0.48832955,  0.36920643,        0.84872127,  0.87346766,  0.53069113,  0.85553096,  0.85764386,        0.47306874,  0.02036611,  0.83126042,  0.38759022,  0.49233068,        0.74566044,  0.60466216,  0.56741342,  0.43416703,  0.83602352,        0.72477786,  0.65632253,  0.53058775,  0.60023269,  0.77641023,        0.84703763,  0.70993659,  0.7801523 ,  0.46161604,  0.84373446,        0.39295281,  0.46052385,  0.88273449,  0.87440032,  0.48304623,        0.53380475,  0.75891465,  0.85876382,  0.38558097,  0.85795763,        0.39785899,  0.85219954,  0.53642823,  0.86038619,  0.43699704,        0.38829633,  0.54291415,  0.69030671,  0.43887074,  0.51384962,        0.51912781,  0.83667847,  0.76248539,  0.69612144,  0.51530997,        0.86167552,  0.55346107,  0.56205672,  0.49273512,  0.38805592,        0.57038854,  0.68677314,  0.20332654,  0.75659329,  0.82280178,        0.51078711,  0.56655943,  0.39855324,  0.87777997,  0.81846156,        0.85011915,  0.53745726,  0.48476499,  0.57083761,  0.62520973,        0.48791422,  0.57163867,  0.80710385,  0.75753237,  0.80107683,        0.50370862,  0.49411065,  0.56270422,  0.46054445,  0.46870708,        0.53443711,  0.52806612,  0.54696216,  0.38036632,  0.8439417 ,        0.43517732,  0.74914748,  0.64728736,  0.41663216,  0.8823285 ,        0.65599758,  0.56449485,  0.51988053,  0.62928512,  0.88015404,        0.56872777,  0.39189978,  0.49345531,  0.46686063,  0.59723997,        0.44721036,  0.30721342,  0.75113026,  0.50932716,  0.73578982,       -0.11420488,  0.41858652,  0.75882296,  0.7275962 , -0.04073665,        0.80153593,  0.87004395,  0.68206941,  0.43331808,  0.46482802,        0.84659276,  0.50866477,  0.68601103,  0.74449975,  0.83022338,        0.73707965,  0.27681202,  0.66098479,  0.28977719,  0.51863521,        0.63445046,  0.40559979,  0.14818081,  0.76068525,  0.23252498,        0.53021521,  0.47737535,  0.20930573,  0.73655361,  0.40050939,        0.38201296,  0.53131423,  0.8300432 ,  0.57416668,  0.83002234,        0.43809863,  0.72601129,  0.30355831,  0.36933954,  0.48245049,        0.50126688,  0.50360422,  0.87011861,  0.56950365,  0.83076761,        0.71764725,  0.53645163,  0.7001754 ,  0.50522187,  0.87888555,        0.77936165,  0.10535855,  0.73083257,  0.87808798,  0.66433392,        0.46478475,  0.37703473,  0.73374533,  0.74890043,  0.73918627,        0.63932594,  0.09590229,  0.56398421,  0.65471361,  0.32850826,        0.50686886,  0.82252268,  0.8784639 ,  0.50307722,  0.55480534,        0.87909816,  0.47641098,  0.31311959,  0.52686075,  0.88545307,        0.20448704,  0.80778118,  0.44642434,  0.40574811,  0.88056023,        0.4973487 ,  0.69311101,  0.72625355,  0.48589387,  0.4978385 ,        0.55313636,  0.50253656,  0.87260952,  0.86131163,  0.40383223,        0.86877735,  0.47545049,  0.55504965,  0.88434796,  0.70495153,        0.88081422,  0.73413228,  0.74319485,  0.86247661,  0.68152552,        0.87029291,  0.81761732,  0.55085702,  0.49102505,  0.55389601,        0.124766  ,  0.4404892 ,  0.53977082,  0.57674226,  0.52475521,        0.71693971,  0.59037229,  0.27134864,  0.55075649,  0.5305809 ,        0.45997724,  0.52098416,  0.69242901,  0.42370109,  0.55411474,        0.56138849,  0.53447704,  0.69329183,  0.54368936,  0.32886853,        0.86126399,  0.71469113,  0.49146367,  0.50494774,  0.82158862,        0.86861319,  0.54403438,  0.73940315,  0.81462808,  0.84352203,        0.48207009,  0.7354327 ,  0.78085872,  0.87875202,  0.04033208,        0.50804578,  0.80938918,  0.51061604,  0.38053425,  0.64455589,        0.67957545,  0.87709406,  0.54770971,  0.49617626,  0.06631062,        0.82052164,  0.85247897,  0.4986702 ,  0.41583248,  0.53794955,        0.73049329,  0.28601778,  0.87874615,  0.86432778,  0.53085921,        0.81504707,  0.80902757,  0.73654387,  0.79629133,  0.69825831,        0.71042076,  0.37753505,  0.87392688,  0.36052199,  0.53293388,        0.65652301,  0.8590337 ,  0.37778142,  0.88171647,  0.55744616,        0.72988524,  0.47205379,  0.25321102,  0.36665898,  0.87510459,        0.54567292,  0.4377203 ,  0.69836179,  0.88279947,  0.73712769,        0.7571288 ,  0.64200399,  0.71414246,  0.66105524,  0.64924985,       -0.03393189,  0.67879166,  0.87717775,  0.70483203,  0.81570721,        0.88445546,  0.42536337,  0.84352976,  0.19940384,  0.33446675,       -0.05200008,  0.63729057,  0.86077417,  0.29232998,  0.85936207,        0.01230106,  0.74072871,  0.54572786,  0.4226642 ,  0.75803727,        0.41490286,  0.47701084,  0.81796862,  0.80656788,  0.63246787,        0.43149716,  0.47554846,  0.67481449,  0.29491288,  0.47884262,        0.73531065,  0.74909774,  0.53905722,  0.60853703,  0.41799506,        0.26889856,  0.65941878,  0.57469934,  0.74695893,  0.53566443,        0.87031783,  0.55546256,  0.74959292,  0.52013136,  0.48602131,        0.84252024,  0.5553399 ,  0.32396765,  0.83121787,  0.6507822 ,        0.40589711,  0.81861161,  0.85537229,  0.51500612,  0.46370284,        0.35233694,  0.41423309,  0.66647621,  0.87838551,  0.55564776,        0.52172866,  0.80216634,  0.74626963,  0.70305507,  0.727976  ,        0.4315848 ,  0.71546113, -0.14042082,  0.70475791,  0.54510442,        0.49963818,  0.50497552,  0.5260391 ,  0.7371355 ,  0.39249758,        0.47181954,  0.51361169,  0.4902578 ,  0.42402416,  0.54710266,        0.42517899,  0.54612333,  0.40920498,  0.73864644,  0.5056526 ,        0.87463183,  0.41531738,  0.88324604,  0.4574416 ,  0.50326717,        0.56519891,  0.86397315,  0.84031419,  0.81795975,  0.55956891,        0.43032946,  0.28423933,  0.75002919,  0.53694244,  0.86418082,        0.50509088,  0.75702551,  0.85123063,  0.47073065,  0.85904201,        0.69214588,  0.32746785,  0.87507056,  0.77556871,  0.47820639,        0.37692453,  0.23345891,  0.46482472,  0.36325517,  0.17966353,        0.31925836,  0.67652463,  0.35889712,  0.87965911,  0.3907438 ,        0.5748237 ,  0.74655924,  0.57403918,  0.69733646,  0.52992071])

from sklearn.metrics import calinski_harabasz_scorecalinski_harabasz_score(x, y_predict)

1809.991966958033

from time import timenow = time()calinski_harabasz_score(x, y_predict)time() - now

0.0034482479095458984

now = time()silhouette_score(x, y_predict)time() - now

0.008353948593139648

import datetimedatetime.datetime.fromtimestamp(time()).strftime(r"%Y-%m-%d %H:%M:%S")

"2023-04-21 00:14:24"

from sklearn.cluster import KMeansfrom sklearn.metrics import silhouette_samples, silhouette_scoreimport matplotlib.pyplot as pltimport matplotlib.cm as cmimport numpy as npfor n_clusters in [2, 3, 4, 5, 6, 7]:    n_clusters = n_clusters    # 设置画布和子画布    fig, (ax1, ax2) = plt.subplots(1, 2)    # 设置画布尺寸    fig.set_size_inches(18, 7)    # 设置子ax1的X轴刻度    ax1.set_xlim([-0.1, 1])    # 设置子ax2的Y轴刻度 0 ——（500 + （2 + 1）* 10）    ax1.set_ylim([0, x.shape[0] + (n_clusters + 1) * 10])    # 实例化KMeans    clusterer = KMeans(n_clusters=n_clusters, n_init="auto", random_state=10).fit(x)    # 每个样本点对应的标签    cluster_labels = clusterer.labels_    # 计算轮廓系数的均值    silhouette_avg = silhouette_score(x, cluster_labels)    print(        "For n_clusters =",        n_clusters,        "The average silhouette_score is :",        silhouette_avg,    )    # 计算数据集中每个样本自己的轮廓系数    sample_silhouette_values = silhouette_samples(x, cluster_labels)    # 为了不让图形紧贴X轴    y_lower = 10    for i in range(n_clusters):        # 取出每个样本对应标签 i 的数组，并进行排序        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]        ith_cluster_silhouette_values.sort()        # 取出每个样本对应标签 i 的数组的 总记录数        size_cluster_i = ith_cluster_silhouette_values.shape[0]        # 10 + 每个样本对应标签 i 的数组的 总记录数        y_upper = y_lower + size_cluster_i        # 随机颜色        color = cm.nipy_spectral(float(i) / n_clusters)        ax1.fill_betweenx(            np.arange(y_lower, y_upper),  # X轴            ith_cluster_silhouette_values,  # Y轴            facecolor=color,            alpha=0.7,  # 透明度        )        # Y轴上的标签        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))        # 更新下一个簇的位置        y_lower = y_upper + 10        # 设置标签        ax1.set_title("The silhouette plot for the various clusters.")        ax1.set_xlabel("The silhouette coefficient values")        ax1.set_ylabel("Cluster label")        # 画出平均线        ax1.axvline(x=silhouette_avg, color="red", linestyle="--")        # 清空Y轴坐标        ax1.set_yticks([])        ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])        colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)        # 画第二个画布的散点图        ax2.scatter(x[:, 0], x[:, 1], marker="o", s=8, c=colors)        # 画出质心        centers = clusterer.cluster_centers_        ax2.scatter(centers[:, 0], centers[:, 1], marker="x", c="red", alpha=1, s=200)        ax2.set_title("The visualization of the clustered data.")        ax2.set_xlabel("Feature space for the 1st feature")        ax2.set_ylabel("Feature space for the 2nd feature")        plt.suptitle(            (                "Silhouette analysis for KMeans clustering on sample data "                "with n_clusters = %d" % n_clusters            ),            fontsize=14,            fontweight="bold",        )plt.show()

For n_clusters = 2 The average silhouette_score is : 0.7049787496083262For n_clusters = 3 The average silhouette_score is : 0.5882004012129721For n_clusters = 4 The average silhouette_score is : 0.6505186632729437For n_clusters = 5 The average silhouette_score is : 0.5662344175321901For n_clusters = 6 The average silhouette_score is : 0.4358297989156284For n_clusters = 7 The average silhouette_score is : 0.3685767770971513

重要参数 init & random_state & n_init

array([[-6.92324165e+00, -1.06695320e+01],       [-8.63062033e+00, -7.13940564e+00],       [-9.63048069e+00, -2.72044935e+00],       [-2.30647659e+00,  5.30797676e+00],       [-7.57005366e+00, -3.01446491e+00],       [-1.00051011e+00,  2.77905153e+00],       [-4.81826839e+00, -2.77214822e+00],       [-5.33964799e+00, -1.27625764e+00],       [-7.94308840e+00, -3.89993901e+00],       [-5.54924525e+00, -3.41298968e+00],       [-5.14508990e+00, -9.54492198e+00],       [-7.09669936e+00, -8.04074036e+00],       [-5.82641512e+00, -1.96346196e+00],       [-1.83198811e+00,  3.52863145e+00],       [-7.34267235e+00, -3.16546482e+00],       [-7.34072825e+00, -6.92427252e+00],       [-7.94653906e+00, -3.36768655e+00],       [-8.24598536e+00, -8.61315821e+00],       [-1.98197711e+00,  4.02243551e+00],       [-4.35098035e+00, -3.69476678e+00],       [-1.04768696e+01, -3.60318139e+00],       [-1.10195984e+01, -3.15882031e+00],       [-5.17255904e+00, -4.31835971e+00],       [-2.40671820e+00,  6.09894447e+00],       [-6.72149498e+00, -2.88440806e+00],       [-6.58935963e+00, -4.43379548e+00],       [-1.46126019e+00,  4.52549851e+00],       [-9.19003455e-01,  3.45278927e+00],       [-1.04093517e+01, -2.67482046e+00],       [-6.36722809e+00, -3.32666072e+00],       [-6.72766125e+00, -7.14516267e+00],       [-2.27956075e+00,  5.10452190e+00],       [-5.84887560e+00, -3.03970506e+00],       [-6.07993051e+00, -7.08197568e+00],       [-5.26682929e+00, -2.69645055e+00],       [-6.05367512e+00, -9.62979077e+00],       [-1.00822205e+01, -4.25071043e+00],       [-1.18708735e+01, -3.03273343e+00],       [-5.37107307e+00, -7.95635833e+00],       [-9.37590900e+00, -4.55315308e+00],       [-6.63401987e+00, -2.58340356e+00],       [-9.54609655e+00, -2.84917422e+00],       [-1.69825542e+00,  2.79071751e+00],       [-5.60217602e+00, -6.59908490e-01],       [-6.03429022e+00, -4.08821196e+00],       [-6.37230784e+00, -8.63190046e+00],       [-1.02264783e+01, -2.33998717e+00],       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-6.92950589e+00],       [-1.01967107e+01, -2.08687717e+00],       [-7.96356538e+00, -7.83357116e+00],       [-6.77680402e+00, -6.65511992e+00],       [-1.08749940e+01, -4.82113577e+00],       [-1.84048021e+00,  3.80256924e+00],       [-7.98067403e+00, -8.56048015e+00],       [-6.32066246e+00, -3.30751892e+00],       [-6.17979966e+00, -3.00803447e+00],       [-2.17665436e+00,  3.40946304e+00],       [-6.73224718e-01,  4.62002377e+00],       [-8.93892171e+00, -3.51521408e+00],       [-7.48937497e+00, -8.88475909e+00],       [-2.89641328e+00,  5.28232880e+00],       [-8.13399258e-01,  3.54697393e+00],       [-5.77752667e+00, -2.85145276e+00],       [-6.24883850e+00, -8.76563508e+00],       [-3.10367371e+00,  3.90202401e+00],       [-1.05724063e+00,  4.82677207e+00],       [-5.73215048e+00, -5.04695454e+00],       [-9.93696231e+00, -3.74222379e+00],       [-3.03267723e+00,  4.72164926e+00],       [-1.07035530e+01, -2.76066248e+00],       [-5.68475631e+00, -3.76816924e+00],       [-8.62182374e+00, -8.76567023e+00],       [-6.67177294e+00, -9.97714796e+00],       [-1.92577841e+00,  4.43910442e+00],       [-8.16299488e+00, -3.38896569e+00],       [-3.74380343e+00, -8.75345344e+00],       [-5.66601211e+00, -4.97019633e+00],       [-2.88961804e+00,  4.95702736e+00],       [-2.35995841e+00,  4.20309542e+00],       [-6.80491557e+00, -3.49602548e+00],       [-7.10480676e+00, -4.10830531e+00],       [-6.96685539e+00, -3.12876392e+00],       [-6.31354495e+00, -8.01283267e+00],       [-4.47120679e+00, -3.54131043e+00],       [-1.53940095e+00,  5.02369298e+00],       [-1.60875215e+00,  3.76949422e+00],       [-1.01927698e+01, -3.14795512e+00],       [-2.80207810e+00,  4.05714715e+00],       [ 2.45098802e-01,  5.51754657e+00],       [-3.31028117e+00,  3.51593428e+00],       [-2.84187803e+00,  3.74073535e+00],       [-5.75867612e+00, -8.75783107e+00],       [-5.99591056e+00, -8.11285667e+00],       [-4.98360687e+00, -3.20522961e+00],       [-1.86845414e+00,  4.99311306e+00],       [-9.71503679e+00, -4.77944598e+00],       [-6.47373322e+00, -2.78682541e+00],       [-6.99263028e+00, -7.14344077e+00],       [-1.53773863e+00,  5.53597378e+00],       [-1.04464505e+01, -4.62579659e+00],       [-1.09679881e+00,  4.64722696e+00],       [-7.25256877e+00, -2.91682833e+00],       [-1.97451969e-01,  2.34634916e+00],       [-1.00670412e+01, -4.06174061e+00],       [-6.13468589e+00, -4.50793424e+00],       [-1.03725172e+01, -4.70331816e+00],       [-1.88188805e+00,  4.20573180e+00],       [-7.15498484e+00, -3.10778598e+00],       [-6.14254799e+00, -3.65202206e+00],       [-7.42749427e+00, -9.63838456e+00],       [-1.13009458e+00,  4.54419108e+00],       [-6.28485505e+00, -8.78266971e+00],       [-7.33325349e+00, -8.28490373e+00],       [-6.40320111e+00, -7.16687592e+00],       [-7.22187586e+00, -9.48843083e+00],       [-6.09834293e+00, -7.44017905e+00],       [-7.20807793e+00, -7.12024433e+00],       [-9.68744022e+00, -6.04759636e+00],       [-7.87372938e+00, -7.59578865e+00],       [-1.14663009e+00,  4.10839703e+00],       [-5.90344220e+00, -8.18075749e+00],       [-2.76017908e+00,  5.55121358e+00],       [-1.23606555e+00,  4.48382994e+00],       [-9.97584967e+00, -4.42202236e+00],       [-2.10668847e+00,  5.63099757e+00],       [-4.73558876e+00, -4.23748969e+00],       [-1.07233096e+01, -4.82111722e+00],       [-8.26074369e+00, -5.64724782e+00],       [-6.88384344e+00, -7.04605265e+00],       [-2.15777347e+00,  4.09550489e+00],       [-7.85988444e+00, -4.73888254e+00],       [-4.60642026e-01,  4.59164629e+00],       [-5.05685487e+00, -5.02946642e+00],       [-7.66055006e+00, -8.46234942e+00],       [-8.41923982e+00, -3.45834788e+00],       [-1.09947323e+01, -4.06014253e+00],       [-6.71376529e+00, -8.22199857e+00],       [-1.07972600e+01, -4.24494314e+00],       [-8.23746328e+00, -4.01400104e+00],       [-2.93211866e+00,  4.72003759e+00],       [-1.66145139e+00,  3.00986944e+00],       [-7.65734347e+00, -1.04581360e+01],       [-9.98054778e+00, -4.38249083e+00],       [-5.51940374e+00, -2.38780334e+00],       [-1.96967668e+00,  1.97165210e+00],       [-3.88464981e+00, -2.84336261e+00],       [-5.82969906e+00, -2.99067321e+00],       [-6.66700176e+00, -9.14923899e+00],       [-6.62889599e+00, -8.84071550e+00],       [-6.48944961e+00, -2.06753733e+00],       [-7.17134231e+00, -1.09442245e+01],       [-1.13042466e+01, -3.87696807e+00],       [-9.53654840e+00, -5.12933122e+00],       [-6.09866132e+00, -7.42731125e+00],       [-8.78925618e+00, -2.83764674e+00],       [-7.32386504e+00, -7.96393491e+00],       [-1.00330804e+01, -1.84274349e+00],       [-1.03619773e+00,  3.97153319e+00],       [-6.42829877e+00, -6.74397472e+00],       [-2.87930430e+00,  6.85585852e+00],       [-1.05299465e+01, -2.83521515e+00],       [-6.11423078e+00, -3.20893543e+00],       [-1.78245013e+00,  3.47072043e+00],       [-8.95271809e+00, -3.34483385e+00],       [-5.16617901e+00, -3.79170586e+00],       [-1.64215050e+00,  3.28447114e+00],       [-8.33534296e+00, -7.87023257e+00],       [-6.31107706e+00, -3.92118081e+00],       [-1.78002448e+00,  3.17336913e+00],       [-1.68417686e+00,  3.63132825e+00],       [-1.05552072e+01, -3.01417980e+00],       [-5.34354009e+00, -2.13897664e+00],       [-1.15365057e+01, -4.40124373e+00],       [-4.89503758e+00, -2.48633456e+00],       [-5.44396990e+00, -8.95941292e+00],       [-1.58173878e+00,  5.02487013e+00],       [-7.02993859e+00, -6.69931052e+00],       [-6.17074238e+00, -2.56078204e+00],       [-2.22186534e+00,  6.36136794e+00],       [-7.57385446e+00, -8.31971406e+00],       [-7.65822594e+00, -7.64292051e+00],       [-6.89501293e+00, -9.31723608e+00],       [-1.11141825e+01, -3.87242145e+00],       [-7.94152277e-01,  2.10495117e+00],       [-6.42803193e+00, -5.52129397e+00],       [-5.89780702e+00, -8.19289680e+00],       [-6.59169697e+00, -2.44779959e+00],       [-6.45785776e+00, -3.30981436e+00],       [-1.07755713e+01, -2.83750744e+00],       [-1.02341495e+01, -3.22553505e+00],       [-6.26681839e+00, -8.25516014e+00],       [-5.20580980e+00, -3.29853839e+00],       [-5.46045264e+00, -2.30831553e+00],       [-7.04259952e+00, -3.45332351e+00],       [-6.09962804e+00, -3.14226915e+00],       [-5.66006950e+00, -3.43776965e+00],       [-7.08097398e+00, -3.03972377e+00],       [-8.41264712e+00, -6.68248825e+00],       [-7.36513410e+00, -1.38859731e+00],       [-1.04166504e+01, -4.43253346e+00],       [-6.41623854e+00, -8.04588481e+00],       [-5.88919348e+00, -2.37049472e+00],       [-1.42946517e+00,  5.16850105e+00],       [-6.56118069e+00, -3.95967311e+00],       [-1.47299851e+00,  4.81654152e+00],       [-5.88100804e+00, -3.31692615e+00],       [-1.04125594e+01, -3.50140251e+00],       [-8.55209377e+00, -3.15841000e+00],       [-7.90673749e-01,  5.15690151e+00],       [-1.00754365e-01,  4.51589257e+00],       [-1.30901393e+00,  3.09420646e+00],       [-9.54755699e+00, -2.18801345e+00],       [-5.32030011e+00, -2.99303869e+00],       [-9.48229870e+00, -5.06821960e+00],       [-6.74361627e+00, -8.87844303e+00],       [-1.02518924e+01, -2.55350460e+00],       [-1.96576392e+00,  5.23446451e+00],       [-5.88036774e+00, -2.36326290e+00],       [-7.34774574e+00, -8.41955499e+00],       [-7.58703957e-01,  3.72276201e+00],       [-8.41357863e+00, -6.85069257e+00],       [-8.20576492e-01,  5.33759195e+00],       [-7.93489041e+00, -7.78403764e+00],       [-5.69446566e+00, -4.06205304e+00],       [-8.57698874e-01,  4.45305717e+00],       [ 1.50975008e-01,  3.10076295e+00],       [-6.55394441e+00, -6.44256627e+00],       [-1.09316272e+01, -4.48636887e+00],       [-6.50155596e+00, -4.65329331e+00],       [-6.93650519e+00, -6.39281292e+00],       [-1.01336898e+01, -4.75061833e+00],       [-9.89148978e+00, -5.47902886e+00],       [-8.89871617e+00, -4.85498304e+00],       [-8.11394993e+00, -7.83656921e+00],       [-5.29078354e+00, -3.64846688e+00],       [-1.41076074e+00,  4.10984872e+00],       [-9.50537595e+00, -4.63402669e+00],       [-7.82749456e+00, -2.51032104e+00],       [-6.38088086e+00, -8.50663809e+00],       [-8.96014913e+00, -8.06349899e+00],       [-7.66603898e+00, -7.59715459e+00],       [-6.46534407e+00, -2.85544633e+00]])

案例：图片矢量量化

# 导入库import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_sample_imagefrom sklearn.cluster import KMeansfrom sklearn.metrics import pairwise_distances_argminfrom sklearn.utils import shuffle

china = load_sample_image("china.jpg")plt.axis(False)plt.imshow(china)

china.shape

(427, 640, 3)

china.dtype

dtype("uint8")

china

array([[[174, 201, 231],        [174, 201, 231],        [174, 201, 231],        ...,        [250, 251, 255],        [250, 251, 255],        [250, 251, 255]],       [[172, 199, 229],        [173, 200, 230],        [173, 200, 230],        ...,        [251, 252, 255],        [251, 252, 255],        [251, 252, 255]],       [[174, 201, 231],        [174, 201, 231],        [174, 201, 231],        ...,        [252, 253, 255],        [252, 253, 255],        [252, 253, 255]],       ...,       [[ 88,  80,   7],        [147, 138,  69],        [122, 116,  38],        ...,        [ 39,  42,  33],        [  8,  14,   2],        [  6,  12,   0]],       [[122, 112,  41],        [129, 120,  53],        [118, 112,  36],        ...,        [  9,  12,   3],        [  9,  15,   3],        [ 16,  24,   9]],       [[116, 103,  35],        [104,  93,  31],        [108, 102,  28],        ...,        [ 43,  49,  39],        [ 13,  21,   6],        [ 15,  24,   7]]], dtype=uint8)

china[0][0]

array([174, 201, 231], dtype=uint8)

import pandas as pdpd.DataFrame(china.reshape(427 * 640, 3)).drop_duplicates().shape

(96615, 3)

n_clusters = 64china = np.array(china, dtype="float64") / china.max()

w, h, d = original_shape = tuple(china.shape)

assert d == 3, "d 必须为 3"

image_array = np.reshape(china, (427 * 640, 3))image_array.shape

(273280, 3)

image_array_sample = shuffle(image_array, random_state=0)[:1000]kmeans = KMeans(n_clusters=n_clusters, n_init="auto", random_state=0).fit(    image_array_sample)

# 质心的坐标kmeans.cluster_centers_

array([[0.97323103, 0.97706735, 0.99369139],       [0.32053664, 0.29638803, 0.25180599],       [0.70375817, 0.7504902 , 0.74052288],       [0.06169935, 0.06196078, 0.04235294],       [0.50718954, 0.53594771, 0.40043573],       [0.83529412, 0.86349206, 0.89505135],       [0.40612745, 0.40612745, 0.22377451],       [0.81568627, 0.53803922, 0.35529412],       [0.22527233, 0.16034858, 0.13420479],       [0.50028011, 0.54789916, 0.57478992],       [0.73524384, 0.82021116, 0.91925591],       [0.90313725, 0.90333333, 0.90607843],       [0.26381462, 0.26773619, 0.1144385 ],       [0.72268908, 0.36022409, 0.25210084],       [0.38867102, 0.46230937, 0.42788671],       [0.88687783, 0.91463047, 0.94932127],       [0.97777778, 0.77254902, 0.60261438],       [0.80999367, 0.82530044, 0.84845035],       [0.61497326, 0.67593583, 0.71265597],       [0.1120915 , 0.13888889, 0.13398693],       [0.48714597, 0.49215686, 0.26143791],       [0.33832442, 0.36684492, 0.31764706],       [0.51372549, 0.33333333, 0.19529412],       [0.8127451 , 0.89264706, 0.98071895],       [0.14323063, 0.10718954, 0.07656396],       [0.76068627, 0.85617647, 0.9604902 ],       [0.45065359, 0.32581699, 0.28562092],       [0.16127451, 0.24068627, 0.24215686],       [0.33986928, 0.26339869, 0.09477124],       [0.61699346, 0.59836601, 0.54052288],       [0.20555556, 0.22287582, 0.08137255],       [0.93776091, 0.9368754 , 0.9485136 ],       [0.40392157, 0.16627451, 0.10156863],       [0.89411765, 0.63764706, 0.43529412],       [0.40606061, 0.44278075, 0.12121212],       [0.225     , 0.07034314, 0.06446078],       [0.28683473, 0.44593838, 0.43305322],       [0.59176471, 0.55215686, 0.43137255],       [0.5827451 , 0.55098039, 0.32078431],       [0.20588235, 0.3379085 , 0.33202614],       [0.83071895, 0.79150327, 0.7254902 ],       [0.72679739, 0.56339869, 0.44575163],       [0.03006536, 0.02538126, 0.01372549],       [0.9       , 0.94498911, 0.99368192],       [0.54980392, 0.44627451, 0.43294118],       [0.74871795, 0.79140271, 0.79803922],       [0.3025641 , 0.33182504, 0.18793363],       [0.54836601, 0.63137255, 0.63529412],       [0.69346405, 0.70653595, 0.64901961],       [0.56339869, 0.40130719, 0.30718954],       [0.93368192, 0.96104575, 0.99616558],       [0.05784314, 0.17156863, 0.2127451 ],       [0.11960784, 0.04191176, 0.0370098 ],       [0.26039216, 0.23581699, 0.20156863],       [0.52679739, 0.53431373, 0.49477124],       [0.0799253 , 0.10644258, 0.054155  ],       [0.71540616, 0.43473389, 0.32268908],       [0.40627451, 0.40235294, 0.33960784],       [0.33604827, 0.34690799, 0.12217195],       [0.84684685, 0.91944886, 0.99194489],       [0.46784314, 0.4372549 , 0.37607843],       [0.16265173, 0.16190476, 0.12380952],       [0.43071895, 0.24183007, 0.18627451],       [0.31176471, 0.15392157, 0.13578431]])

# 质心的索引label = kmeans.predict(image_array)label

array([10, 10, 10, ..., 61,  3,  3], dtype=int32)

kmeans.cluster_centers_[1]

array([0.32053664, 0.29638803, 0.25180599])

image_kmeans = image_array.copy()for i in range(w * h):    image_kmeans[i] = kmeans.cluster_centers_[label[i]]

image_kmeans

array([[0.73524384, 0.82021116, 0.91925591],       [0.73524384, 0.82021116, 0.91925591],       [0.73524384, 0.82021116, 0.91925591],       ...,       [0.16265173, 0.16190476, 0.12380952],       [0.06169935, 0.06196078, 0.04235294],       [0.06169935, 0.06196078, 0.04235294]])

image_kmeans = image_kmeans.reshape(w, h, d)image_kmeans.shape

(427, 640, 3)

# 随机取出64个质心centroid_random = shuffle(image_array)[:n_clusters]# 函数pairwise_distances_argmin(x1,x2,axis) #x1和x2分别是序列# 用来计算x2中的每个样本到x1中的每个样本点的距离，并返回和x2相同形状的，x1中对应的最近的样本点的索引labels_random = pairwise_distances_argmin(centroid_random, image_array, axis=0)image_random = image_array.copy()for i in range(w * h):    image_random[i] = centroid_random[labels_random[i]]image_random = image_random.reshape(w, h, d)image_random.shape

(427, 640, 3)

labels_random

array([55, 55, 55, ..., 52, 60, 60])

plt.figure(figsize=(10, 10))plt.axis("off")plt.title("Original image (96,615 colors)")plt.imshow(china)plt.figure(figsize=(10, 10))plt.axis("off")plt.title("Quantized image (64 colors, K-Means)")plt.imshow(image_kmeans)plt.figure(figsize=(10, 10))plt.axis("off")plt.title("Quantized image (64 colors, Random)")plt.imshow(image_random)plt.show()

关键词：