def test_one_pt(self): surrogate = ResponseSurface() x = array([[0.]]) y = array([[1.]]) surrogate.train(x, y) assert_rel_error(self, surrogate.betas, array([[1.], [0.], [0.]]), 1e-9)
def test_one_pt(self): surrogate = ResponseSurface() x = array([[0.0]]) y = array([[1.0]]) surrogate.train(x, y) assert_rel_error(self, surrogate.betas, array([[1.0], [0.0], [0.0]]), 1e-9)
def test_vector_derivs(self): surrogate = ResponseSurface() x = array([[a, b] for a, b in itertools.product(linspace(0, 1, 10), repeat=2)]) y = array([[a + b, a - b] for a, b in x]) surrogate.train(x, y) jac = surrogate.jacobian(array([[0.5, 0.5]])) assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)
def test_1d_training(self): x = array([[0.0], [2.0], [3.0]]) y = array([[branin_1d(case)] for case in x]) surrogate = ResponseSurface() surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_rel_error(self, mu, y0, 1e-9)
def test_vector_derivs(self): surrogate = ResponseSurface() x = array([[a, b] for a, b in itertools.product(linspace(0, 1, 10), repeat=2)]) y = array([[a + b, a - b] for a, b in x]) surrogate.train(x, y) jac = surrogate.linearize(array([[0.5, 0.5]])) assert_rel_error(self, jac, array([[1, 1], [1, -1]]), 1e-5)
def test_scalar_derivs(self): surrogate = ResponseSurface() x = array([[0.], [1.], [2.], [3.]]) y = x.copy() surrogate.train(x, y) jac = surrogate.linearize(array([[0.]])) assert_rel_error(self, jac[0][0], 1., 1e-3)
def test_1d_ill_conditioned(self): # Test for least squares solver utilization when ill-conditioned x = array([[case] for case in linspace(0.0, 1.0, 40)]) y = sin(x) surrogate = ResponseSurface() surrogate.train(x, y) new_x = array([0.5]) mu = surrogate.predict(new_x) assert_rel_error(self, mu, sin(0.5), 1e-3)
def test_scalar_derivs(self): surrogate = ResponseSurface() x = array([[0.0], [1.0], [2.0], [3.0]]) y = x.copy() surrogate.train(x, y) jac = surrogate.jacobian(array([[0.0]])) assert_rel_error(self, jac[0][0], 1.0, 1e-3)
def test_1d_ill_conditioned(self): # Test for least squares solver utilization when ill-conditioned x = array([[case] for case in linspace(0., 1., 40)]) y = sin(x) surrogate = ResponseSurface() surrogate.train(x, y) new_x = array([0.5]) mu = surrogate.predict(new_x) assert_rel_error(self, mu, sin(0.5), 1e-3)
def test_1d_predictor(self): x = array([[0.0], [2.0], [3.0], [4.0], [6.0]]) y = array([[branin_1d(case)] for case in x]) surrogate = ResponseSurface() surrogate.train(x, y) new_x = array([pi]) mu = surrogate.predict(new_x) assert_rel_error(self, mu, 1.73114, 1e-4)
def test_vector_output(self): surrogate = ResponseSurface() x = array([[0.], [2.], [4.]]) y = array([[0., 0.], [1., 1.], [2., 0.]]) surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_near_equal(mu, y0, 1e-9)
def test_vector_input(self): surrogate = ResponseSurface() x = array([[0., 0., 0.], [1., 1., 1.]]) y = array([[0.], [3.]]) surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_rel_error(self, mu, y0, 1e-9)
def test_vector_input(self): surrogate = ResponseSurface() x = array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]) y = array([[0.0], [3.0]]) surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_rel_error(self, mu, y0, 1e-9)
def test_2d(self): x = array([[-2.0, 0.0], [-0.5, 1.5], [1.0, 1.0], [0.0, 0.25], [0.25, 0.0], [0.66, 0.33]]) y = array([[branin(case)] for case in x]) surrogate = ResponseSurface() surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_rel_error(self, mu, y0, 1e-9) mu = surrogate.predict(array([0.5, 0.5])) assert_rel_error(self, mu, branin([0.5, 0.5]), 1e-1)
def test_2d(self): x = array([[-2., 0.], [-0.5, 1.5], [1., 1.], [0., .25], [.25, 0.], [.66, .33]]) y = array([[branin(case)] for case in x]) surrogate = ResponseSurface() surrogate.train(x, y) for x0, y0 in zip(x, y): mu = surrogate.predict(x0) assert_rel_error(self, mu, y0, 1e-9) mu = surrogate.predict(array([.5, .5])) assert_rel_error(self, mu, branin([.5, .5]), 1e-1)