chore: import upstream snapshot with attribution

examples/gaussian_mixture.py

@@ -0,0 +1,46 @@
+import random
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from mla.kmeans import KMeans
+from mla.gaussian_mixture import GaussianMixture
+
+random.seed(1)
+np.random.seed(6)
+
+
+def make_clusters(skew=True, *arg, **kwargs):
+    X, y = datasets.make_blobs(*arg, **kwargs)
+    if skew:
+        nrow = X.shape[1]
+        for i in np.unique(y):
+            X[y == i] = X[y == i].dot(np.random.random((nrow, nrow)) - 0.5)
+    return X, y
+
+
+def KMeans_and_GMM(K):
+    COLOR = "bgrcmyk"
+
+    X, y = make_clusters(skew=True, n_samples=1500, centers=K)
+    _, axes = plt.subplots(1, 3)
+
+    # Ground Truth
+    axes[0].scatter(X[:, 0], X[:, 1], c=[COLOR[int(assignment)] for assignment in y])
+    axes[0].set_title("Ground Truth")
+
+    # KMeans
+    kmeans = KMeans(K=K, init="++")
+    kmeans.fit(X)
+    kmeans.predict()
+    axes[1].set_title("KMeans")
+    kmeans.plot(ax=axes[1], holdon=True)
+
+    # Gaussian Mixture
+    gmm = GaussianMixture(K=K, init="kmeans")
+    gmm.fit(X)
+    axes[2].set_title("Gaussian Mixture")
+    gmm.plot(ax=axes[2])
+
+
+if __name__ == "__main__":
+    KMeans_and_GMM(4)