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