In [2]:

    

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

digits_train=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra',header=None)
digits_test=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tes',header=None)

X_train=digits_train[np.arange(64)]
y_train=digits_train[64]
X_test=digits_test[np.arange(64)]
y_test=digits_test[64]

from sklearn.cluster import KMeans
kmeans=KMeans(n_clusters=10)
kmeans.fit(X_train)
y_pred=kmeans.predict(X_test)


    

In [3]:

    

from sklearn import metrics
# 使用ARI（Adjusted Rand Index）进行聚类性能评估
print metrics.adjusted_rand_score(y_test, y_pred)


    

    




0.659942175683

In [6]:

    

# 若评估的数据没有所属类别，则使用轮廓系数（Silhouette Coefficient）
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt

# 分割出3*2=6个子图，并在1号子图作图
plt.subplot(3,2,1)

# 初始化原始数据点。
x1 = np.array([1, 2, 3, 1, 5, 6, 5, 5, 6, 7, 8, 9, 7, 9])
x2 = np.array([1, 3, 2, 2, 8, 6, 7, 6, 7, 1, 2, 1, 1, 3])
X = np.array(zip(x1, x2)).reshape(len(x1), 2)


# 在1号子图做出原始数据点阵的分布。
plt.xlim([0, 10])
plt.ylim([0, 10])
plt.title('Instances')
plt.scatter(x1, x2)

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'b']
markers = ['o', 's', 'D', 'v', '^', 'p', '*', '+']

clusters = [2, 3, 4, 5, 8]
subplot_counter = 1
sc_scores = []
for t in clusters:
    subplot_counter += 1
    plt.subplot(3, 2, subplot_counter)
    kmeans_model = KMeans(n_clusters=t).fit(X)
    for i, l in enumerate(kmeans_model.labels_):
        plt.plot(x1[i], x2[i], color=colors[l], marker=markers[l], ls='None')
    plt.xlim([0, 10])
    plt.ylim([0, 10])
    sc_score = silhouette_score(X, kmeans_model.labels_, metric='euclidean')
    sc_scores.append(sc_score)

# 绘制轮廓系数与不同类簇数量的直观显示图。
    plt.title('K = %s, silhouette coefficient= %0.03f' %(t, sc_score))
    
# 绘制轮廓系数与不同类簇数量的关系曲线。
plt.figure()
plt.plot(clusters, sc_scores, '*-')
plt.xlabel('Number of Clusters')
plt.ylabel('Silhouette Coefficient Score')

plt.show()


    

In [7]:

    

# 导入必要的工具包。
import numpy as np
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt

# 使用均匀分布函数随机三个簇，每个簇周围10个数据样本。
cluster1 = np.random.uniform(0.5, 1.5, (2, 10))
cluster2 = np.random.uniform(5.5, 6.5, (2, 10))
cluster3 = np.random.uniform(3.0, 4.0, (2, 10))

# 绘制30个数据样本的分布图像。
X = np.hstack((cluster1, cluster2, cluster3)).T
plt.scatter(X[:,0], X[:, 1])
plt.xlabel('x1')
plt.ylabel('x2')
plt.show()


# 测试9种不同聚类中心数量下，每种情况的聚类质量，并作图。
K = range(1, 10)
meandistortions = []

for k in K:
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    meandistortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1))/X.shape[0])
    
plt.plot(K, meandistortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average Dispersion')
plt.title('Selecting k with the Elbow Method')
plt.show()


    

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