Quanlse/ErrorMitigation/ZNE/Extrapolation.py
def linearRegression(x, y):
    r"""
    Linear regression method. Fit the given data points to the following linear model:

    :math:`y = \beta_0 + \beta_1     x`

    :param x: a list of independent variables
    :param y: a list of dependent variables
    :return: An array of coefficients: :math:`\beta_0` (intercept) and :math:`\beta_1` (slope)
    """
    x = np.array(x).ravel()
    y = np.array(y).ravel()
    m = len(x)
    xAvg = np.mean(x)
    yAvg = np.mean(y)
    Sxx = np.sum(x ** 2) - m * xAvg ** 2
    Sxy = np.sum(x * y) - m * xAvg * yAvg
    return np.array([yAvg - xAvg * Sxy / Sxx, Sxy / Sxx])
r"""
    Linear regression method. Fit the given data points to the following linear model:

    :math:`y = \beta_0 + \beta_1     x`

    :param x: a list of independent variables
    :param y: a list of dependent variables
    :return: An array of coefficients: :math:`\beta_0` (intercept) and :math:`\beta_1` (slope)
    
r"""
Linear regression method. Fit the given data points to the following linear model.
r " " " Linear regression method . Fit the given data points to the following linear model .
def linearRegression(x, y):
    x = np.array(x).ravel()
    y = np.array(y).ravel()
    m = len(x)
    xAvg = np.mean(x)
    yAvg = np.mean(y)
    Sxx = np.sum(x ** 2) - m * xAvg ** 2
    Sxy = np.sum(x * y) - m * xAvg * yAvg
    return np.array([yAvg - xAvg * Sxy / Sxx, Sxy / Sxx])
def linearRegression ( x , y ) : x = np . array ( x ) . ravel ( ) y = np . array ( y ) . ravel ( ) m = len ( x ) xAvg = np . mean ( x ) yAvg = np . mean ( y ) Sxx = np . sum ( x ** 2 ) - m * xAvg ** 2 Sxy = np . sum ( x * y ) - m * xAvg * yAvg return np . array ( [ yAvg - xAvg * Sxy / Sxx , Sxy / Sxx ] )
r"""
Linear regression method.
r " " " Linear regression method .
r"""
    Linear regression method. Fit the given data points to the following linear model:

    :math:`y = \beta_0 + \beta_1     x`

    :param x: a list of independent variables
    :param y: a list of dependent variables
    :return: An array of coefficients: :math:`\beta_0` (intercept) and :math:`\beta_1` (slope)
    """
An array of coefficients: :math:`\beta_0` (intercept) and :math:`\beta_1` (slope)
An array of coefficients : : math : ` \ beta_0 ` ( intercept ) and : math : ` \ beta_1 ` ( slope )
x
a list of independent variables
a list of independent variables
y
a list of dependent variables
a list of dependent variables
math
None