PsiMarginal.py 文件源码

def __entropy(self, pdf):
        """Calculate shannon entropy of posterior distribution.
        Arguments
        ---------
            pdf :   ndarray (float64)
                    posterior distribution of psychometric curve parameters for each stimuli


        Returns
        -------
        1D numpy array (float64) : Shannon entropy of posterior for each stimuli
        """
        # Marginalize out all nuisance parameters, i.e. all except alpha and sigma
        postDims = np.ndim(pdf)
        if self.marginalize == True:
            while postDims > 3:  # marginalize out second-to-last dimension, last dim is x
                pdf = np.sum(pdf, axis=-2)
                postDims -= 1
        # find expected entropy, suppress divide-by-zero and invalid value warnings
        # as this is handled by the NaN redefinition to 0
        with np.errstate(divide='ignore', invalid='ignore'):
            entropy = np.multiply(pdf, np.log(pdf))
        entropy[np.isnan(entropy)] = 0  # define 0*log(0) to equal 0
        dimSum = tuple(range(postDims - 1))  # dimensions to sum over. also a Chinese dish
        entropy = -(np.sum(entropy, axis=dimSum))
        return entropy