TwoStepCondTestObject.py 文件源码

def compute_gradient_totalcverr_wrt_sqsigma(self,matrix_results,lambda_val,sigmasq_z):
        # 0: K_tst_tr; 1: K_tr_tr; 2: D_tst_tr; 3: D_tr_tr
        num_sample_cv = self.num_samples
        ttl_num_folds = np.shape(matrix_results)[1]
        gradient_cverr_per_fold = np.zeros(ttl_num_folds)
        for jj in range(ttl_num_folds):
            uu = np.shape(matrix_results[3][jj])[0]
            log_M_tr_tst = matrix_results[2][jj].T*float(-1/2)*sigmasq_z**(-1)
            M_tr_tst = exp(log_M_tr_tst)
            log_M_tr_tr = matrix_results[3][jj]*float(-1/2)*sigmasq_z**(-1)
            M_tr_tr = exp(log_M_tr_tr)
            lower_ZZ = cholesky(M_tr_tr+ lambda_val*eye(uu), lower=True)
            ZZ = cho_solve((lower_ZZ,True),eye(uu))
            term_1 = matrix_results[0][jj].dot(ZZ.dot((M_tr_tr*sigmasq_z**(-1)*(-log_M_tr_tr)).dot(ZZ.dot(M_tr_tst))))
            term_2 = -matrix_results[0][jj].dot(ZZ.dot(M_tr_tst*(-log_M_tr_tst*sigmasq_z**(-1))))
            term_3 = (sigmasq_z**(-1)*(M_tr_tst.T)*(-log_M_tr_tst.T)).dot(ZZ.dot(matrix_results[1][jj].dot(ZZ.dot(M_tr_tst))))
            term_4 = -(M_tr_tst.T).dot(ZZ.dot((M_tr_tr*sigmasq_z**(-1)*(-log_M_tr_tr)).dot(ZZ.dot(matrix_results[1][jj].dot(
                                                                                    ZZ.dot(M_tr_tst))))))
            term_5 = -(M_tr_tst.T).dot(ZZ.dot(matrix_results[1][jj].dot(ZZ.dot((M_tr_tr*sigmasq_z**(-1)*(-log_M_tr_tr)).dot(
                                                                                    ZZ.dot(M_tr_tst))))))
            term_6 = (M_tr_tst.T).dot(ZZ.dot(matrix_results[1][jj].dot(ZZ.dot(M_tr_tst*sigmasq_z**(-1)*(-log_M_tr_tst)))))
            gradient_cverr_per_fold[jj] = trace(2*term_1 + 2*term_2 + term_3 + term_4 + term_5 + term_6)
        return sum(gradient_cverr_per_fold)/float(num_sample_cv)