4ca74616a886c24dd7946e00f535f323e2e13787,astroML/linear_model/linear_regression.py,LinearRegression,fit,#LinearRegression#Any#Any#Any#,38

Before Change

        X_fit, y_fit = self._process_Xy(self.X_, self.y_, dy)

        self.coef_ = np.linalg.solve(np.dot(X_fit.T, X_fit),
                                     np.dot(X_fit.T, y_fit))
        return self

    def predict(self, X):
        return np.dot(self._process_X(X), self.coef_)

After Change

        return model

    def fit(self, X, y, y_error=1):
        kwds = {}
        if self.kwds is not None:
            kwds.update(self.kwds)
        kwds["fit_intercept"] = False

Instances