LS

main.py

class Regression:
    
    def __init(self):
        pass
    
    def __loss(self, X, y, coefs):
        l = (y - np.dot(X, coefs))
        return np.dot(l.T, l)
    
    def fit(self, X, y):
        n_elems, n_coefs = X.shape
        X_with_ones = np.hstack([np.ones((n_elems, 1)), X])
        self.coefs = np.ones((n_coefs + 1, 1))
        
        for i in xrange(10):
            print(self.__loss(X_with_ones, y, self.coefs))
            grad = self.__grad(X_with_ones, y, self.coefs)
            self.coefs += 0.01 * grad
        
    def __grad(self, X, y, coefs):
        return 2 * np.dot(X.T, (y - np.dot(X, coefs)))
    
    def predict(self, X):
        n_elems, n_coefs = X.shape
        X_with_ones = np.hstack([np.ones((n_elems, 1)), X])
        return np.dot(X_with_ones, self.coefs)