test Gaussian ART

elementary/GaussianART.py

@@ -25,14 +25,16 @@ def category_choice(self, i: np.ndarray, w: np.ndarray, params: dict) -> tuple[f
         n = w[-1]
         sig = np.diag(np.multiply(sigma,sigma))
         dist = mean-i
-        exp_dist_sig_dist = np.exp(-0.5*np.matmul(dist.T, np.matmul((1/sig), dist)))
+        exp_dist_sig_dist = np.exp(-0.5*np.matmul(dist.T, np.matmul(np.linalg.inv(sig), dist)))
         cache = {
             "exp_dist_sig_dist": exp_dist_sig_dist
         }
         p_i_cj = exp_dist_sig_dist/np.sqrt((self.pi2**self.dim_)*np.linalg.det(sig))
         p_cj = n/np.sum(w_[-1] for w_ in self.W)
 
-        return p_i_cj*p_cj, cache
+        activation = p_i_cj*p_cj
+
+        return activation, cache
 
 
     def match_criterion(self, i: np.ndarray, w: np.ndarray, params: dict, cache: Optional[dict] = None) -> float:
@@ -60,4 +62,4 @@ def update(self, i: np.ndarray, w: np.ndarray, params, cache: Optional[dict] = N
 
 
     def new_weight(self, i: np.ndarray, params: dict) -> np.ndarray:
-        return np.concatenate[i, params["sigma_init"], [1]]
+        return np.concatenate([i, params["sigma_init"], [1.]])

examples/test_gaussian_art.py

@@ -0,0 +1,39 @@
+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+import path
+import sys
+
+# directory reach
+directory = path.Path(__file__).abspath()
+
+print(directory.parent)
+# setting path
+sys.path.append(directory.parent.parent)
+
+from elementary.GaussianART import GaussianART
+from common.utils import normalize
+import numpy as np
+
+
+def cluster_blobs():
+    data, target = make_blobs(n_samples=150, centers=3, cluster_std=0.50, random_state=0, shuffle=False)
+    print("Data has shape:", data.shape)
+
+    X = normalize(data)
+    print("Prepared data has shape:", X.shape)
+
+    params = {
+        "rho": 0.15,
+        "sigma_init": np.array([0.5, 0.5]),
+    }
+    cls = GaussianART(params)
+    y = cls.fit_predict(X)
+
+    print(f"{cls.n_clusters} clusters found")
+
+    cls.visualize(X, y)
+    plt.show()
+
+
+if __name__ == "__main__":
+    cluster_blobs()