+++ /dev/null
-from __future__ import print_function
-
-from sklearn.datasets import make_blobs
-from sklearn.cluster import KMeans
-from sklearn.metrics import silhouette_samples, silhouette_score
-
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-import numpy as np
-
-print(__doc__)
-
-# Generating the sample data from make_blobs
-# This particular setting has one distinct cluster and 3 clusters placed close
-# together.
-'''X, y = make_blobs(n_samples=500,
- n_features=2,
- centers=4,
- cluster_std=1,
- center_box=(-10.0, 10.0),
- shuffle=True,
- random_state=1) # For reproducibility'''
-
-X = np.array([[132, 192], [117, 960], [117, 962], [1343, 0], [117, 1116], [117, 1117], [117, 1118], [117, 1119], [1015, 0], [117, 966]])
-
-range_n_clusters = [2, 3, 4, 5, 6]
-
-for n_clusters in range_n_clusters:
- # Create a subplot with 1 row and 2 columns
- fig, (ax1, ax2) = plt.subplots(1, 2)
- fig.set_size_inches(18, 7)
-
- # The 1st subplot is the silhouette plot
- # The silhouette coefficient can range from -1, 1 but in this example all
- # lie within [-0.1, 1]
- ax1.set_xlim([-0.1, 1])
- # The (n_clusters+1)*10 is for inserting blank space between silhouette
- # plots of individual clusters, to demarcate them clearly.
- ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
-
- # Initialize the clusterer with n_clusters value and a random generator
- # seed of 10 for reproducibility.
- clusterer = KMeans(n_clusters=n_clusters, random_state=10)
- cluster_labels = clusterer.fit_predict(X)
-
- # The silhouette_score gives the average value for all the samples.
- # This gives a perspective into the density and separation of the formed
- # clusters
- silhouette_avg = silhouette_score(X, cluster_labels)
- print("For n_clusters =", n_clusters,
- "The average silhouette_score is :", silhouette_avg)
-
- # Compute the silhouette scores for each sample
- sample_silhouette_values = silhouette_samples(X, cluster_labels)
-
- y_lower = 10
- for i in range(n_clusters):
- # Aggregate the silhouette scores for samples belonging to
- # cluster i, and sort them
- ith_cluster_silhouette_values = \
- sample_silhouette_values[cluster_labels == i]
-
- ith_cluster_silhouette_values.sort()
-
- size_cluster_i = ith_cluster_silhouette_values.shape[0]
- y_upper = y_lower + size_cluster_i
-
- color = cm.nipy_spectral(float(i) / n_clusters)
- ax1.fill_betweenx(np.arange(y_lower, y_upper),
- 0, ith_cluster_silhouette_values,
- facecolor=color, edgecolor=color, alpha=0.7)
-
- # Label the silhouette plots with their cluster numbers at the middle
- ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
-
- # Compute the new y_lower for next plot
- y_lower = y_upper + 10 # 10 for the 0 samples
-
- ax1.set_title("The silhouette plot for the various clusters.")
- ax1.set_xlabel("The silhouette coefficient values")
- ax1.set_ylabel("Cluster label")
-
- # The vertical line for average silhouette score of all the values
- ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
-
- ax1.set_yticks([]) # Clear the yaxis labels / ticks
- ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
-
- # 2nd Plot showing the actual clusters formed
- colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
- ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
- c=colors, edgecolor='k')
-
- # Labeling the clusters
- centers = clusterer.cluster_centers_
- # Draw white circles at cluster centers
- ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
- c="white", alpha=1, s=200, edgecolor='k')
-
- for i, c in enumerate(centers):
- ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
- s=50, edgecolor='k')
-
- 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()
-
projects / pingpong.git / blobdiff