def estimate_moments()

in svinfer/summary_statistics/summary_statistics.py [0:0]

• 9 lines of code
• 3 McCabe index (conditional complexity)

    def estimate_moments(self, x_moments):
        """
        Estimate the 1st, 2nd, 3rd, 4th (raw) moments for underlying the data,
        given the moments of the corresponding noisy data.
        :param moments: a list of numeric values
        :return: a list of numeric values
        """
        # the 0th, 1st, 2nd, 3rd, 4th moments of a normal distribution
        # with variance self.s2
        noise_moments = [1, 0, self.s2, 0, 3 * self.s2 ** 2]
        a = np.zeros([4, 4])
        for i in range(4):
            for j in range(i + 1):
                a[i, j] = special.comb(i + 1, j + 1) * noise_moments[i - j]
        b = np.array(x_moments) - np.array(noise_moments[1:])
        z_moments = linalg.solve(a, b)
        return z_moments