def test_rff_feature_map_grad_single_equals_feature_map_derivative_d(): D = 2 m = 3 omega = np.random.randn(D, m) u = np.random.uniform(0, 2 * np.pi, m) x = np.random.randn(D) grad = rff_feature_map_grad_single(x, omega, u) grad_manual = np.zeros((D, m)) for d in range(D): grad_manual[d, :] = rff_feature_map_grad_d(x, omega, u, d) assert_allclose(grad_manual, grad)
def test_rff_feature_map_grad_single_equals_feature_map_derivative_d(): D = 2 m = 3 omega = np.random.randn(D, m) u = np.random.uniform(0, 2 * np.pi, m) x = np.random.randn(D) grad = rff_feature_map_grad_single(x, omega, u) grad_manual = np.zeros((D, m)) for d in range(D): grad_manual[d, :] = rff_feature_map_grad_d(x, omega, u, d) assert_allclose(grad_manual, grad)
def grad(self, x): if self.theta is None: raise RuntimeError("Model not fitted yet.") grad = rff_feature_map_grad_single(x, self.omega, self.u) return np.dot(grad, self.theta)
def grad(self, x): if self.theta is None: raise RuntimeError("Model not fitted yet.") grad = rff_feature_map_grad_single(x, self.omega, self.u) return np.dot(grad, self.theta)