fin separation front/back

backend/kmeans_strategy.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+def perform_kmeans_clustering(data, data_name, n_clusters, n_init, max_iter):
+    x = data[data_name].to_numpy()
+    kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
+    y_kmeans = kmeans.fit_predict(x)
+
+    fig = plt.figure()
+    if len(data_name) == 2:
+        ax = fig.add_subplot(projection='rectilinear')
+        plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
+        centers = kmeans.cluster_centers_
+        plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
+    else:
+        ax = fig.add_subplot(projection='3d')
+        ax.scatter(x[:, 0], x[:, 1], x[:, 2], c=y_kmeans, s=50, cmap="viridis")
+        centers = kmeans.cluster_centers_
+        ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
+    return fig