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Plot_s_dbw.py
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import numpy as np | |
import S_Dbw as sdbw | |
from sklearn.cluster import KMeans | |
from sklearn.datasets.samples_generator import make_blobs | |
from sklearn.metrics.pairwise import pairwise_distances_argmin | |
np.random.seed(0) | |
S_Dbw_result = [] | |
batch_size = 45 | |
centers = [[1, 1], [-1, -1], [1, -1]] | |
cluster_std=[0.7,0.3,1.2] | |
n_clusters = len(centers) | |
X1, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[0]) | |
X2, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[1]) | |
X3, _ = make_blobs(n_samples=3000, centers=centers, cluster_std=cluster_std[2]) | |
import matplotlib.pyplot as plt | |
fig = plt.figure(figsize=(9, 3)) | |
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.08, top=0.9) | |
colors = ['#4EACC5', '#FF9C34', '#4E9A06'] | |
for item, X in enumerate(list([X1, X2, X3])): | |
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10) | |
k_means.fit(X) | |
k_means_cluster_centers = k_means.cluster_centers_ | |
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers) | |
KS = sdbw.S_Dbw(X, k_means_labels, k_means_cluster_centers) | |
S_Dbw_result.append(KS.S_Dbw_result()) | |
ax = fig.add_subplot(1,3,item+1) | |
for k, col in zip(range(n_clusters), colors): | |
my_members = k_means_labels == k | |
cluster_center = k_means_cluster_centers[k] | |
ax.plot(X[my_members, 0], X[my_members, 1], 'w', | |
markerfacecolor=col, marker='.') | |
ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, | |
markeredgecolor='k', markersize=6) | |
ax.set_title('S_Dbw: %.3f' %(S_Dbw_result[item])) | |
ax.set_ylim((-4,4)) | |
ax.set_xlim((-4,4)) | |
plt.text(-3.5, 1.8, 'cluster_std: %f' %(cluster_std[item])) | |
plt.savefig('./pic1.png', dpi=150) |
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np.random.seed(0) | |
S_Dbw_result = [] | |
batch_size = 45 | |
centers = [[[1, 1], [-1, -1], [1, -1]], | |
[[0.8, 0.8], [-0.8, -0.8], [0.8, -0.8]], | |
[[1.2, 1.2], [-1.2, -1.2], [1.2, -1.2]]] | |
n_clusters = len(centers) | |
X1, _ = make_blobs(n_samples=3000, centers=centers[0], cluster_std=0.7) | |
X2, _ = make_blobs(n_samples=3000, centers=centers[1], cluster_std=0.7) | |
X3, _ = make_blobs(n_samples=3000, centers=centers[2], cluster_std=0.7) | |
import matplotlib.pyplot as plt | |
fig = plt.figure(figsize=(8, 3)) | |
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.2, top=0.9) | |
colors = ['#4EACC5', '#FF9C34', '#4E9A06'] | |
for item, X in enumerate(list([X1, X2, X3])): | |
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10) | |
k_means.fit(X) | |
k_means_cluster_centers = k_means.cluster_centers_ | |
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers) | |
KS = sdbw.S_Dbw(X, k_means_labels, k_means_cluster_centers) | |
S_Dbw_result.append(KS.S_Dbw_result()) | |
ax = fig.add_subplot(1,3,item+1) | |
for k, col in zip(range(n_clusters), colors): | |
my_members = k_means_labels == k | |
cluster_center = k_means_cluster_centers[k] | |
ax.plot(X[my_members, 0], X[my_members, 1], 'w', | |
markerfacecolor=col, marker='.') | |
ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, | |
markeredgecolor='k', markersize=6) | |
ax.set_title('S_Dbw: %.3f ' %(S_Dbw_result[item])) | |
# ax.set_xticks(()) | |
# ax.set_yticks(()) | |
ax.set_ylim((-4,4)) | |
ax.set_xlim((-4,4)) | |
ax.set_xlabel('centers: \n%s' %(centers[item])) | |
plt.savefig('./pic2.png', dpi=150) |
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