cdt/causality/pairwise/IGCI.py
def integral_approx_estimator(x, y):
    """Integral approximation estimator for causal inference.

    :param x: input variable x 1D
    :param y: input variable y 1D
    :return: Return value of the IGCI model >0 if x->y otherwise if return <0
    """
    a, b = (0., 0.)
    x = np.array(x)
    y = np.array(y)
    idx, idy = (np.argsort(x), np.argsort(y))

    for x1, x2, y1, y2 in zip(x[[idx]][:-1], x[[idx]][1:], y[[idx]][:-1], y[[idx]][1:]):
        if x1 != x2 and y1 != y2:
            a = a + np.log(np.abs((y2 - y1) / (x2 - x1)))

    for x1, x2, y1, y2 in zip(x[[idy]][:-1], x[[idy]][1:], y[[idy]][:-1], y[[idy]][1:]):
        if x1 != x2 and y1 != y2:
            b = b + np.log(np.abs((x2 - x1) / (y2 - y1)))

    return (a - b)/len(x)
def integral_approx_estimator(x, y):
    """Integral approximation estimator for causal inference.

    :param x: input variable x 1D
    :param y: input variable y 1D
    :return: Return value of the IGCI model >0 if x->y otherwise if return <0
    """
    a, b = (0., 0.)
    x = np.array(x)
    y = np.array(y)
    idx, idy = (np.argsort(x), np.argsort(y))

    for x1, x2, y1, y2 in zip(x[[idx]][:-1], x[[idx]][1:], y[[idx]][:-1], y[[idx]][1:]):
        if x1 != x2 and y1 != y2:
            a = a + np.log(np.abs((y2 - y1) / (x2 - x1)))

    for x1, x2, y1, y2 in zip(x[[idy]][:-1], x[[idy]][1:], y[[idy]][:-1], y[[idy]][1:]):
        if x1 != x2 and y1 != y2:
            b = b + np.log(np.abs((x2 - x1) / (y2 - y1)))

    return (a - b)/len(x)
def integral_approx_estimator ( x , y ) : a , b = ( 0. , 0. ) x = np . array ( x ) y = np . array ( y ) idx , idy = ( np . argsort ( x ) , np . argsort ( y ) ) for x1 , x2 , y1 , y2 in zip ( x [ [ idx ] ] [ : - 1 ] , x [ [ idx ] ] [ 1 : ] , y [ [ idx ] ] [ : - 1 ] , y [ [ idx ] ] [ 1 : ] ) : if x1 != x2 and y1 != y2 : a = a + np . log ( np . abs ( ( y2 - y1 ) / ( x2 - x1 ) ) ) for x1 , x2 , y1 , y2 in zip ( x [ [ idy ] ] [ : - 1 ] , x [ [ idy ] ] [ 1 : ] , y [ [ idy ] ] [ : - 1 ] , y [ [ idy ] ] [ 1 : ] ) : if x1 != x2 and y1 != y2 : b = b + np . log ( np . abs ( ( x2 - x1 ) / ( y2 - y1 ) ) ) return ( a - b ) / len ( x )
Integral approximation estimator for causal inference.

    :param x: input variable x 1D
    :param y: input variable y 1D
    :return: Return value of the IGCI model >0 if x->y otherwise if return <0
Integral approximation estimator for causal inference .