import numpy as np


class LinearRegression:
    def __init__(self, learning_rate, num_iterations):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None

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

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

        # Gradient Descent
        for _ in range(self.num_iterations):
            predictions = np.dot(X, self.weights) + self.bias

            dw = (1 / num_samples) * np.dot(X.T, (predictions - y))
            db = (1 / num_samples) * np.sum(predictions - y)

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

    def predict(self, X):
        predictions = np.dot(X, self.weights) + self.bias
        return predictions