Recipe 578914 revision 1

def fit(X, Y):

    def mean(Xs):
        return sum(Xs) / len(Xs)
    m_X = mean(X)
    m_Y = mean(Y)

    def std(Xs, m):
        normalizer = len(Xs) - 1
        return math.sqrt(sum((pow(x - m, 2) for x in Xs)) / normalizer)
    # assert np.round(Series(X).std(), 6) == np.round(std(X, m_X), 6)

    def pearson_r(Xs, Ys):

        sum_xy = 0
        sum_sq_v_x = 0
        sum_sq_v_y = 0

        for (x, y) in zip(Xs, Ys):
            var_x = x - m_X
            var_y = y - m_Y
            sum_xy += var_x * var_y
            sum_sq_v_x += pow(var_x, 2)
            sum_sq_v_y += pow(var_y, 2)
        return sum_xy / math.sqrt(sum_sq_v_x * sum_sq_v_y)
    # assert np.round(Series(X).corr(Series(Y)), 6) == np.round(pearson_r(X, Y), 6)

    r = pearson_r(X, Y)

    b = r * (std(Y, m_Y) / std(X, m_X))
    A = m_Y - b * m_X

    def line(x):
        return b * x + A
    return line

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• revision 1 (9 years ago)