ordering.py 文件源码

def softassign(self):
        """
        Run the softassign algorithm until convergence.

        """
        # TODO add possibility of slack

        for i, indices in enumerate(self.element_type_subset_indices):
            M = self.match_matrix[indices]
            old_M = M.copy()
            for it in xrange(self.max_softassign_iterations):
                # normalize across rows (except slack)
                M /= np.sum(M,axis=1)[:,None]
                # normalize across columns (except slack)
                M /= np.sum(M,axis=0)

                max_row_normalization_error = np.max(abs(np.sum(M, axis = 1)-1))
                # break if converged
                if max_row_normalization_error < self.softassign_convergence_threshold:
                    oprint(5, "Softassign algorithm for subset %d converged in iteration %d" % (i, it+1))
                    break

                mean_squared_difference = np.max(abs(old_M-M))
                if mean_squared_difference < self.softassign_convergence_threshold2:
                    oprint(5, "Softassign algorithm for subset %d converged in iteration %d" % (i, it+1))
                    break

                if it == (self.max_softassign_iterations - 1):
                    eprint(3, "WARNING: Softassign algorithm for subset %d did not converge to %.2g (reached %.2g) in %d iterations" % (i, self.softassign_convergence_threshold, max_row_normalization_error, self.max_softassign_iterations))

                np.copyto(old_M, M)

            # M is NOT a view, but a copy
            self.match_matrix[indices] = M