7c8e935f8de396e0c42e0d6d14600bcf70408b09,testing/test_coregion.py,TestEquivalence,test_predicts,#TestEquivalence#,116

Before Change

                            atol=1.0e-2)

    def test_predicts(self):
        with self.session.graph.as_default(), self.session.as_default():
            X_augumented0 = np.hstack([self.Xtest, np.zeros((self.Xtest.shape[0], 1))])
            X_augumented1 = np.hstack([self.Xtest, np.ones((self.Xtest.shape[0], 1))])
            Ytest = [np.sin(x) + 0.9 * np.cos(x*1.6) for x in self.Xtest]
            Y_augumented0 = np.hstack([Ytest, np.zeros((self.Xtest.shape[0], 1))])
            Y_augumented1 = np.hstack([Ytest, np.ones((self.Xtest.shape[0], 1))])

            // check predict_f
            pred_f0 = self.vgp0.predict_f(self.Xtest)
            pred_fc0 = self.cvgp.predict_f(X_augumented0)
            self.assertTrue(np.allclose(pred_f0, pred_fc0, atol=1.0e-2))
            pred_f1 = self.vgp1.predict_f(self.Xtest)
            pred_fc1 = self.cvgp.predict_f(X_augumented1)
            self.assertTrue(np.allclose(pred_f1, pred_fc1, atol=1.0e-2))

            // check predict y
            pred_y0 = self.vgp0.predict_y(self.Xtest)
            pred_yc0 = self.cvgp.predict_y(
                np.hstack([self.Xtest, np.zeros((self.Xtest.shape[0], 1))]))

            // predict_y returns results for all the likelihodds in multi_likelihood
            assert_allclose(pred_y0[0], pred_yc0[0][:, :np.array(Ytest).shape[1]], atol=1.0e-2)
            assert_allclose(pred_y0[1], pred_yc0[1][:, :np.array(Ytest).shape[1]], atol=1.0e-2)
            pred_y1 = self.vgp1.predict_y(self.Xtest)
            pred_yc1 = self.cvgp.predict_y(
                np.hstack([self.Xtest, np.ones((self.Xtest.shape[0], 1))]))

            // predict_y returns results for all the likelihodds in multi_likelihood
            assert_allclose(pred_y1[0], pred_yc1[0][:, np.array(Ytest).shape[1]:], atol=1.0e-2)
            assert_allclose(pred_y1[1], pred_yc1[1][:, np.array(Ytest).shape[1]:], atol=1.0e-2)

            // check predict_density
            pred_ydensity0 = self.vgp0.predict_density(self.Xtest, Ytest)
            pred_ydensity_c0 = self.cvgp.predict_density(X_augumented0, Y_augumented0)
            self.assertTrue(np.allclose(pred_ydensity0, pred_ydensity_c0, atol=1e-2))
            pred_ydensity1 = self.vgp1.predict_density(self.Xtest, Ytest)
            pred_ydensity_c1 = self.cvgp.predict_density(X_augumented1, Y_augumented1)
            assert_allclose(pred_ydensity1, pred_ydensity_c1, atol=1e-2)

            // just check predict_f_samples(self) works
            self.cvgp.predict_f_samples(X_augumented0, 1)
            self.cvgp.predict_f_samples(X_augumented1, 1)

            // check predict_f_full_cov
            self.vgp0.predict_f_full_cov(self.Xtest)
            self.cvgp.predict_f_full_cov(X_augumented0)
            self.vgp1.predict_f_full_cov(self.Xtest)
            self.cvgp.predict_f_full_cov(X_augumented1)


if __name__ == "__main__":
    unittest.main()

After Change

                            atol=1.0e-2)

    def test_predicts(self):
        with self.test_context():
            X_augumented0 = np.hstack([self.Xtest, np.zeros((self.Xtest.shape[0], 1))])
            X_augumented1 = np.hstack([self.Xtest, np.ones((self.Xtest.shape[0], 1))])
            Ytest = [np.sin(x) + 0.9 * np.cos(x*1.6) for x in self.Xtest]

Instances