simple-logistic-regression.py

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, epochs=1000):
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.weights = None
        self.bias = None

    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def fit(self, X, y):
        num_samples, num_features = X.shape

        # Initialize weights and bias
        self.weights = np.zeros(num_features)
        self.bias = 0

        # Gradient Descent
        for _ in range(self.epochs):
            linear_model = np.dot(X, self.weights) + self.bias
            # map the output probabilities using sigmoid between 0 and 1
            y_predicted = self.sigmoid(linear_model)

            # Derrivative of binary cross entropy loss with respect to weight and bias terms
            dw = (1 / num_samples) * np.dot(X.T, (y_predicted - y))
            db = (1 / num_samples) * np.sum(y_predicted - y)

            # Update weights and bias
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

    def predict(self, X):
        linear_model = np.dot(X, self.weights) + self.bias
        y_predicted = self.sigmoid(linear_model)
        # Convert probabilities to binary output by using a step function
        y_predicted_class = [1 if i > 0.5 else 0 for i in y_predicted]
        return np.array(y_predicted_class)


if __name__ == "__main__":
    # Sample data (X) and labels (y) for AND gate
    X = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
    y = np.array([0, 0, 0, 1])  # AND gate

    model = LogisticRegression(learning_rate=0.1, epochs=1000)
    model.fit(X, y)
    predictions = model.predict(X)

    print("Predictions:", predictions)