copula.py 文件源码

def _logcdf(self, samples):
        lower = np.full(2, -np.inf)
        upper = norm.ppf(samples)
        limit_flags = np.zeros(2)
        if upper.shape[0] > 0:

            def func1d(upper1d):
                '''
                Calculates the multivariate normal cumulative distribution
                function of a single sample.
                '''
                return mvn.mvndst(lower, upper1d, limit_flags, self.theta)[1]

            vals = np.apply_along_axis(func1d, -1, upper)
        else:
            vals = np.empty((0, ))
        old_settings = np.seterr(divide='ignore')
        vals = np.log(vals)
        np.seterr(**old_settings)
        vals[np.any(samples == 0.0, axis=1)] = -np.inf
        vals[samples[:, 0] == 1.0] = np.log(samples[samples[:, 0] == 1.0, 1])
        vals[samples[:, 1] == 1.0] = np.log(samples[samples[:, 1] == 1.0, 0])
        return vals