base_h.py 文件源码

def estimation(self, y):
        """ Estimate Shannon entropy.

        Parameters
        ----------
        y : (number of samples, dimension)-ndarray
            One row of y corresponds to one sample.

        Returns
        -------
        h : float
            Estimated Shannon entropy.

        References
        ----------
        M. N. Goria, Nikolai N. Leonenko, V. V. Mergel, and P. L. Novi 
        Inverardi. A new class of random vector entropy estimators and its 
        applications in testing statistical hypotheses. Journal of 
        Nonparametric Statistics, 17: 277-297, 2005. (S={k})

        Harshinder Singh, Neeraj Misra, Vladimir Hnizdo, Adam Fedorowicz
        and Eugene Demchuk. Nearest neighbor estimates of entropy.
        American Journal of Mathematical and Management Sciences, 23,
        301-321, 2003. (S={k})

        L. F. Kozachenko and Nikolai N. Leonenko. A statistical estimate
        for the entropy of a random vector. Problems of Information
        Transmission, 23:9-16, 1987. (S={1})

        Examples
        --------
        h = co.estimation(y)

        """

        num_of_samples, dim = y.shape
        distances_yy = knn_distances(y, y, True, self.knn_method, self.k,
                                     self.eps, 2)[0]
        v = volume_of_the_unit_ball(dim)
        distances_yy[:, self.k - 1][distances_yy[:, self.k-1] == 0] = 1e-6
        h = log(num_of_samples - 1) - psi(self.k) + log(v) + \
            dim * sum(log(distances_yy[:, self.k-1])) / num_of_samples

        return h