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from sklearn.cluster import MeanShift
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs, make_moons
# Create dataset for clustering
X, y = make_blobs(n_samples=500, cluster_std=1, centers=4 )
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.matmul(X, transformation)
# Train Mean Shift model on blobs dataset and visualize the results
blobs_clustering = MeanShift(bandwidth=2).fit(X_aniso)
fig, axes = plt.subplots(1, 2)
axes[0].scatter(X[:, 0], X[:, 1], c=blobs_clustering.labels_, s=50, cmap='tab20b')
axes[0].set_title('Clustered anizo blobs data')
axes[1].scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='tab20b')
axes[1].set_title('Real anizo blobs data')

              
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from sklearn.datasets import make_moons
import matplotlib.pyplot as plt
from sklearn.cluster import MeanShift

# Create moons dataset for clustering
X, y = make_moons(n_samples=500)
# Fit Mean Shift model on moons dataset and visualize the results
moons_clustering = MeanShift(bandwidth=0.7).fit(X)
fig,axes = plt.subplots(1,2)
axes[0].scatter(X[:, 0], X[:, 1], c=moons_clustering.labels_, s=50, cmap='tab20b')
axes[0].set_title('Clustered moons data')
axes[1].scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='tab20b')
axes[1].set_title('Real moons data')