35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn.cluster import KMeans
|
|
from sklearn.datasets import make_blobs
|
|
plt.figure(figsize=(12, 12))
|
|
n_samples = 1500
|
|
random_state = 170
|
|
X, y = make_blobs(n_samples=n_samples, random_state=random_state)
|
|
# Incorrect number of clusters
|
|
y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X)
|
|
plt.subplot(221)
|
|
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
|
|
plt.title("Incorrect Number of Blobs")
|
|
# Anisotropicly distributed data
|
|
transformation = [[ 0.60834549, -0.63667341], [-0.40887718, 0.85253229]]
|
|
X_aniso = np.dot(X, transformation)
|
|
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso)
|
|
plt.subplot(222)
|
|
plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred)
|
|
plt.title("Anisotropicly Distributed Blobs")
|
|
# Different variance
|
|
X_varied, y_varied = make_blobs(n_samples=n_samples,
|
|
cluster_std=[1.0, 2.5, 0.5],
|
|
random_state=random_state)
|
|
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)
|
|
plt.subplot(223)
|
|
plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)
|
|
plt.title("Unequal Variance")
|
|
# Unevenly sized blobs
|
|
X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))
|
|
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_filtered)
|
|
plt.subplot(224)
|
|
plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)
|
|
plt.title("Unevenly Sized Blobs")
|
|
plt.show() |