def test_bias_kern(self):
N, num_inducing, input_dim, D = 10, 3, 2, 4
X = np.random.rand(N, input_dim)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,input_dim).T
Y -= Y.mean(axis=0)
k = GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
m = BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
m.ensure_default_constraints()
m.randomize()
self.assertTrue(m.checkgrad())
