def b_fit_score(self, x, y):
        """ Computes the cds statistic from variable 1 to variable 2

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2

        Returns:
            float: BF fit score
        """
        x = np.reshape(scale(x), (-1, 1))
        y = np.reshape(scale(y), (-1, 1))
        gp = GaussianProcessRegressor().fit(x, y)
        y_predict = gp.predict(x)
        error = mean_squared_error(y_predict, y)

        return error
def b_fit_score(self, x, y):
        """ Computes the cds statistic from variable 1 to variable 2

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2

        Returns:
            float: BF fit score
        """
        x = np.reshape(scale(x), (-1, 1))
        y = np.reshape(scale(y), (-1, 1))
        gp = GaussianProcessRegressor().fit(x, y)
        y_predict = gp.predict(x)
        error = mean_squared_error(y_predict, y)

        return error
def b_fit_score ( self , x , y ) : x = np . reshape ( scale ( x ) , ( - 1 , 1 ) ) y = np . reshape ( scale ( y ) , ( - 1 , 1 ) ) gp = GaussianProcessRegressor ( ) . fit ( x , y ) y_predict = gp . predict ( x ) error = mean_squared_error ( y_predict , y ) return error
Computes the cds statistic from variable 1 to variable 2

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2

        Returns:
            float: BF fit score
Computes the cds statistic from variable 1 to variable 2