meta_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
        ----------
        Quing Wang, Sanjeev R. Kulkarni, and Sergio Verdu. Universal
        estimation of information measures for analog sources. Foundations
        And Trends In Communications And Information Theory, 5:265-353,
        2009.

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

        """

        num_of_samples, dim = y.shape  # number of samples, dimension

        # estimate the mean and the covariance of y:
        m = mean(y, axis=0)
        c = cov(y, rowvar=False)  # 'rowvar=False': 1 row = 1 observation

        # entropy of N(m,c):
        if dim == 1: 
            det_c = c  # det(): 'expected square matrix' exception
            # multivariate_normal(): 'cov must be 2 dimensional and square'
            # exception:
            c = array([[c]])

        else:
            det_c = det(c)

        h_normal = 1/2 * log((2*pi*exp(1))**dim * det_c)

        # generate samples from N(m,c):
        y_normal = multivariate_normal(m, c, num_of_samples)

        h = h_normal - self.kl_co.estimation(y, y_normal)

        return h