class LinearRegressionScratch:
def __init__(self, learning_rate=0.01, epochs=1000):
self.learning_rate = learning_rate
self.epochs = epochs
self.weights = None
self.bias = None
def predict(self, X):
“””Compute predictions utilizing linear regression speculation perform.”””
return np.dot(X, self.weights) + self.bias
def compute_loss(self, y_true, y_pred):
“””Compute Imply Squared Error (MSE) loss.”””
return np.imply((y_true – y_pred) ** 2)
def gradient_descent(self, X, y_true, y_pred):
“””Carry out one step of gradient descent replace.”””
n = X.form[0]
# Compute gradients
dw = (-2/n) * np.dot(X.T, (y_true – y_pred))
db = (-2/n) * np.sum(y_true – y_pred)
# Replace weights and bias
self.weights -= self.learning_rate * dw
self.bias -= self.learning_rate * db
def match(self, X, y):
“””Prepare the mannequin utilizing Gradient Descent.”””
n_samples, n_features = X.form
self.weights = np.zeros(n_features) # Initialize weights
self.bias = np.zeros(1) # Initialize bias explicitly
# Retailer loss for visualization
self.loss_history = []
for i in vary(self.epochs):
y_pred = self.predict(X)
loss = self.compute_loss(y, y_pred)
self.gradient_descent(X, y, y_pred)
# Retailer loss & print often
self.loss_history.append(loss)
if i % 100 == 0:
print(f”Epoch {i}: Loss = {loss:.4f}”)
