model_analyzer.py 文件源码

def precompute_marginals(self):
        sys.stderr.write('Precomputing marginals...\n')
        self._pdfs = [None] * self._num_instances
        # precomputing all possible marginals
        for i in xrange(self._num_instances):
            mean = self._corrected_means[i]
            cov = self._corrected_covs[i]
            self._pdfs[i] = [None] * (2 ** mean.shape[0])
            for marginal_pattern in itertools.product([False, True], repeat=mean.shape[0]):
                marginal_length = marginal_pattern.count(True)
                if marginal_length == 0:
                    continue
                m = np.array(marginal_pattern)
                marginal_mean = mean[m]
                mm = m[:, np.newaxis]
                marginal_cov = cov[np.dot(mm, mm.transpose())].reshape((marginal_length, marginal_length))
                self._pdfs[i][hash_bool_array(m)] = multivariate_normal(mean=marginal_mean, cov=marginal_cov)