dtype for labels

elementary/BaseART.py

@@ -106,7 +106,7 @@ def partial_fit(self, X: np.ndarray, match_reset_func: Optional[Callable] = None
 
         if not hasattr(self, 'W'):
             self.W: list[np.ndarray] = []
-            self.labels_ = np.zeros((X.shape[0], ))
+            self.labels_ = np.zeros((X.shape[0], ), dtype=int)
             j = 0
         else:
             j = len(self.labels_)
@@ -122,7 +122,7 @@ def predict(self, X: np.ndarray):
         self.validate_data(X)
         self.check_dimensions(X)
 
-        y = np.zeros((X.shape[0],))
+        y = np.zeros((X.shape[0],), dtype=int)
         for i, x in enumerate(X):
             c = self.step_pred(x)
             y[i] = c

examples/test_smart.py

@@ -54,7 +54,7 @@ def cluster_blobs():
             if j == 0:
                 layer_colors.append(colors[k])
             else:
-                layer_colors.append(colors[cls.layers[j-1].map_a2b(k)])
+                layer_colors.append(colors[cls.map_deep(j-1, k)])
         cls.modules[j].plot_bounding_boxes(ax, layer_colors)
 
     plt.show()

hierarchical/DeepARTMAP.py

@@ -16,6 +16,10 @@ def __init__(self, modules: list[BaseART]):
     def labels_(self):
         return self.layers[0].labels_
 
+    @property
+    def labels_deep_(self):
+        return np.concatenate([layer.labels_ for layer in self.layers]+[self.layers[-1].labels_a])
+
     @property
     def n_modules(self):
         return len(self.modules)
@@ -24,6 +28,14 @@ def n_modules(self):
     def n_layers(self):
         return len(self.layers)
 
+    def map_deep(self, level: int, y_a: Union[np.ndarray, int]) -> Union[np.ndarray, int]:
+        y_b = self.layers[level].map_a2b(y_a)
+        if level > 0:
+            return self.map_deep(level-1, y_b)
+        else:
+            return y_b
+
+
     def validate_data(
             self,
             X: list[np.ndarray],
@@ -53,7 +65,7 @@ def fit(self, X: list[np.ndarray], y: Optional[np.ndarray] = None, max_iter=1):
             self.layers[0] = self.layers[0].fit(X[1], X[0], max_iter=max_iter)
 
         for art_i in range(1, self.n_layers):
-            y_i = self.layers[art_i-1].labels_
+            y_i = self.layers[art_i-1].labels_a
             self.layers[art_i] = self.layers[art_i].fit(X[art_i], y_i, max_iter=max_iter)
 
         return self

supervised/ARTMAP.py

@@ -13,7 +13,7 @@ def map_a2b(self, y_a: Union[np.ndarray, int]) -> np.ndarray:
         if isinstance(y_a, int):
             return self.map[y_a]
         u, inv = np.unique(y_a, return_inverse=True)
-        return np.array([self.map[x] for x in u])[inv].reshape(y_a.shape)
+        return np.array([self.map[x] for x in u], dtype=int)[inv].reshape(y_a.shape)
 
     def validate_data(self, X: np.ndarray, y: np.ndarray):
         raise NotImplementedError
@@ -73,7 +73,7 @@ def fit(self, X: np.ndarray, y: np.ndarray, max_iter=1):
         self.labels_ = y
         # init module A
         self.module_a.W = []
-        self.module_a.labels_ = np.zeros((X.shape[0],))
+        self.module_a.labels_ = np.zeros((X.shape[0],), dtype=int)
 
         for _ in range(max_iter):
             for i, (x, c_b) in enumerate(zip(X, y)):
@@ -86,7 +86,7 @@ def partial_fit(self, X: np.ndarray, y: np.ndarray):
         if not hasattr(self, 'labels_'):
             self.labels_ = y
             self.module_a.W = []
-            self.module_a.labels_ = np.zeros((X.shape[0],))
+            self.module_a.labels_ = np.zeros((X.shape[0],), dtype=int)
             j = 0
         else:
             j = len(self.labels_)
@@ -119,8 +119,8 @@ def step_pred(self, x: np.ndarray) -> tuple[int, int]:
 
     def predict(self, X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
         check_is_fitted(self)
-        y_a = np.zeros((X.shape[0],))
-        y_b = np.zeros((X.shape[0],))
+        y_a = np.zeros((X.shape[0],), dtype=int)
+        y_b = np.zeros((X.shape[0],), dtype=int)
         for i, x in enumerate(X):
             c_a, c_b = self.step_pred(x)
             y_a[i] = c_a

topological/TopoART.py

@@ -136,7 +136,7 @@ def fit(self, X: np.ndarray, match_reset_func: Optional[Callable] = None, max_it
         self.check_dimensions(X)
 
         self.W: list[np.ndarray] = []
-        self.labels_ = np.zeros((X.shape[0], ))
+        self.labels_ = np.zeros((X.shape[0], ), dtype=int)
         for _ in range(max_iter):
             for i, x in enumerate(X):
                 self.step_prune(X)