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| import numpy as np | |
| from tqdm import tqdm | |
| class Perceptron: | |
| 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): | |
| n_features = X.shape[1] | |
| self.weights = np.random.randint(n_features, size=(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) | |
| 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 | |