Removed predicted proba.

README.md

@@ -22,12 +22,8 @@ onn_network.partial_fit(np.asarray([[0.8, 0.5]]), np.asarray([1]))
 #Predict classes
 predictions = onn_network.predict(np.asarray([[0.1, 0.2], [0.8, 0.5]]))
 
-Predictions -- array([1, 1])
+Predictions -- array([1, 0])
 
-#Predict classes probabilities
-predictions = onn_network.predict_proba(np.asarray([[0.1, 0.2], [0.8, 0.5]]))
-
-Predictions -- array([[0.5048331 , 0.50083154],[0.49516693, 0.49916846]], dtype=float32)
 ```
 
 ## New features
@@ -37,7 +33,6 @@ Predictions -- array([[0.5048331 , 0.50083154],[0.49516693, 0.49916846]], dtype=
 
 ## Contributors
 - [Alison de Andrade Carrera](https://github.com/alison-carrera)
-- [Fábio Silva Vilas Boas](https://github.com/fabiosvb)
 
 ## References
 - [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://arxiv.org/abs/1711.03705)

onn/OnlineNeuralNetwork.py

@@ -127,22 +127,19 @@ class ONN():
         if len(data.shape) != 1:
             raise Exception("Wrong dimension for this Y data. It should have only one dimensions.")
 
-    def partial_fit(self, X_data, Y_data, show_loss=True):
+    def partial_fit_(self, X_data, Y_data, show_loss=True):
         self.validate_input_X(X_data)
         self.validate_input_Y(Y_data)
         self.update_weights(X_data, Y_data, show_loss)
 
-    def predict_proba(self, X_data):
-        self.validate_input_X(X_data)
-        result = torch.softmax(
-            torch.sum(torch.mul(
-                self.alpha.view(self.max_num_hidden_layers, 1).repeat(1, len(X_data)).view(
-                    self.max_num_hidden_layers, len(X_data), 1), self.forward(X_data)), 0), 0)
-        result[torch.isnan(result)] = 0
-        return result.data.cpu().numpy()
+    def partial_fit(self, X_data, Y_data, show_loss=True):
+        self.partial_fit_(X_data, Y_data, show_loss)
 
-    def predict(self, X_data):
+    def predict_(self, X_data):
         self.validate_input_X(X_data)
         return torch.argmax(torch.sum(torch.mul(
             self.alpha.view(self.max_num_hidden_layers, 1).repeat(1, len(X_data)).view(
                 self.max_num_hidden_layers, len(X_data), 1), self.forward(X_data)), 0), dim=1).cpu().numpy()
+
+    def predict(self, X_data):
+        return self.predict_(X_data)

setup.py

@@ -6,7 +6,7 @@ with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
     long_description = f.read()
 
 setup(name='onn',
-      version='0.0.5',
+      version='0.0.6',
       description='Online Neural Network',
       url='https://github.com/alison-carrera/onn',
       author='Alison Carrera',