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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
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