206f681e3a08d2f6079484de1ba3ea7abaf1dadc,mlxtend/regression/linear_regression.py,LinearRegression,_normal_equation,#LinearRegression#,126

Before Change

        // intercept
        y_pred = np.dot(X, w[1:])
        w[0] = np.mean(y) - np.mean(y_pred)
        return w

    def _shuffle(self, X, y):

After Change

    def _normal_equation(self, X, y):
        Solve linear regression analytically
        Xb = np.hstack((np.ones((X.shape[0], 1)), X))
        w = np.zeros(X.shape[1])
        z = np.linalg.inv(np.dot(Xb.T, Xb))
        w = np.dot(z, np.dot(Xb.T, y))
        return w

Instances