linear-regression.py

class LinearRegression:
  
  def predict(self, X):
    return np.dot(X, self._W)
  
  def _gradient_descent_step(self, X, targets, lr):

    predictions = self.predict(X)
    
    error = predictions - targets
    gradient = np.dot(X.T,  error) / len(X)

    self._W -= lr * gradient
      
  def fit(self, X, y, n_iter=100000, lr=0.01):

    self._W = np.zeros(X.shape[1])

    for i in range(n_iter):        
        self._gradient_descent_step(x, y, lr)       
        
    return self