def b_fit_score(self, x, y):
        """ Compute the RECI fit score

        Args:
            x (numpy.ndarray): Variable 1
            y (numpy.ndarray): Variable 2

        Returns:
            float: RECI fit score

        """
        x = np.reshape(minmax_scale(x), (-1, 1))
        y = np.reshape(minmax_scale(y), (-1, 1))
        poly = PolynomialFeatures(degree=self.degree)
        poly_x = poly.fit_transform(x)

        poly_x[:,1] = 0
        poly_x[:,2] = 0

        regressor = LinearRegression()
        regressor.fit(poly_x, y)

        y_predict = regressor.predict(poly_x)
        error = mean_squared_error(y_predict, y)

        return error
def b_fit_score(self, x, y):
        """ Compute the RECI fit score

        Args:
            x (numpy.ndarray): Variable 1
            y (numpy.ndarray): Variable 2

        Returns:
            float: RECI fit score

        """
        x = np.reshape(minmax_scale(x), (-1, 1))
        y = np.reshape(minmax_scale(y), (-1, 1))
        poly = PolynomialFeatures(degree=self.degree)
        poly_x = poly.fit_transform(x)

        poly_x[:,1] = 0
        poly_x[:,2] = 0

        regressor = LinearRegression()
        regressor.fit(poly_x, y)

        y_predict = regressor.predict(poly_x)
        error = mean_squared_error(y_predict, y)

        return error
def b_fit_score ( self , x , y ) : x = np . reshape ( minmax_scale ( x ) , ( - 1 , 1 ) ) y = np . reshape ( minmax_scale ( y ) , ( - 1 , 1 ) ) poly = PolynomialFeatures ( degree = self . degree ) poly_x = poly . fit_transform ( x ) poly_x [ : , 1 ] = 0 poly_x [ : , 2 ] = 0 regressor = LinearRegression ( ) regressor . fit ( poly_x , y ) y_predict = regressor . predict ( poly_x ) error = mean_squared_error ( y_predict , y ) return error
Compute the RECI fit score

        Args:
            x (numpy.ndarray): Variable 1
            y (numpy.ndarray): Variable 2

        Returns:
            float: RECI fit score
Compute the RECI fit score