model_kmeans.py

import matplotlib.pyplot as plt
# import seaborn as sns; sns.set()
import numpy as np

# Generate some data
from sklearn.datasets.samples_generator import make_blobs
X, y_true = make_blobs(n_samples=400, centers=4,
                       cluster_std=0.60, random_state=0)
X = X[:, ::-1] # flip axes for better plotting

# Plot the data with K Means Labels

# from sklearn.cluster import KMeans
# kmeans = KMeans(4, random_state=0)
# labels = kmeans.fit(X).predict(X)
# plt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');
# plt.show()

from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist

def plot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None):
    labels = kmeans.fit_predict(X)

    # plot the input data
    ax = ax or plt.gca()
    ax.axis('equal')
    ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)

    # plot the representation of the KMeans model
    centers = kmeans.cluster_centers_
    radii = [cdist(X[labels == i], [center]).max()
             for i, center in enumerate(centers)]
    for c, r in zip(centers, radii):
        ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))
    plt.show()

kmeans = KMeans(n_clusters=4, random_state=0)
plot_kmeans(kmeans, X)