Fix: distance of kmeans bad-defined

14.Cluster/KMeans.py

@@ -23,7 +23,7 @@ def fit(self, X):
         while (pre_centers != self.centers).any():
             pre_centers = self.centers.copy()
             dis = euc_dis(X[:, None, :], self.centers[None, :, :])
-            cluster = dis.argmax(axis=-1)
+            cluster = dis.argmin(axis=-1)
             for i in range(self.k):
                 self.centers[i] = X[cluster == i].mean(axis=0)
             step += 1
@@ -32,7 +32,7 @@ def fit(self, X):
 
     def predict(self, X):
         dis = euc_dis(X[:, None, :], self.centers[None, :, :])
-        return dis.argmax(axis=-1)
+        return dis.argmin(axis=-1)
 
 if __name__ == "__main__":
     def demonstrate(X, k, desc):
@@ -46,15 +46,17 @@ def demonstrate(X, k, desc):
         plt.show()
 
     # -------------------------- Example 1 ----------------------------------------
-    X = np.array([[0, 0], [0, 1], [1, 0], [2, 2], [2, 1], [1, 2]])
-    # generate grid-shaped test data
+    X = np.array([[0, 0], [0, 1], [1, 0], [2, 2], [2, 1], [1, 2]]).astype(float)
     demonstrate(X, 2, "Example 1")
 
     # -------------------------- Example 2 ----------------------------------------
     X = np.concatenate([
         np.random.normal([0, 0], [.3, .3], [100, 2]),
         np.random.normal([0, 1], [.3, .3], [100, 2]),
         np.random.normal([1, 0], [.3, .3], [100, 2]),
-    ])
-    # generate grid-shaped test data
-    demonstrate(X, 3, "Example 2: it is very sensitive to noise")
+    ]).astype(float)
+    demonstrate(X, 3, "Example 2")
+
+    # -------------------------- Example 3 ----------------------------------------
+    X = np.array([[0, 0], [0, 1], [0, 3]]).astype(float)
+    demonstrate(X, 2, "Example 3: K-Means doesn't always return the best answer. (try to run multiple times!)")