Delete BackPropogation1.py

BackPropogation1.py

@@ -1,53 +0,0 @@
-import numpy as np
-from tqdm import tqdm
-
-
-class BackPropogation:
-    def __init__(self,learning_rate=0.01, epochs=100,activation_function='step'):
-        self.bias = 0
-        self.learning_rate = learning_rate
-        self.max_epochs = epochs
-        self.activation_function = activation_function
-
-
-    def activate(self, x):
-        if self.activation_function == 'step':
-            return 1 if x >= 0 else 0
-        elif self.activation_function == 'sigmoid':
-            return 1 if (1 / (1 + np.exp(-x)))>=0.5 else 0
-        elif self.activation_function == 'relu':
-            return 1 if max(0,x)>=0.5 else 0
-
-    def fit(self, X, y):
-        error_sum=0
-        n_features = X.shape[1]
-        self.weights = np.zeros((n_features))
-        for epoch in tqdm(range(self.max_epochs)):
-            for i in range(len(X)):
-                inputs = X[i]
-                target = y[i]
-                weighted_sum = np.dot(inputs, self.weights) + self.bias
-                prediction = self.activate(weighted_sum)
-                
-                # Calculating loss and updating weights.
-                error = target - prediction
-                self.weights += self.learning_rate * error * inputs
-                self.bias += self.learning_rate * error
-                
-            print(f"Updated Weights after epoch {epoch} with {self.weights}")
-        print("Training Completed")
-
-    def predict(self, X):
-        predictions = []
-        for i in range(len(X)):
-            inputs = X[i]
-            weighted_sum = np.dot(inputs, self.weights) + self.bias
-            prediction = self.activate(weighted_sum)
-            predictions.append(prediction)
-        return predictions
-
-
-    
-    
-    
-