def test_backward_pass(): npr.seed(1) N = 10 D = 3 alpha = Hyperparameter( initial_value = 2*np.ones(D), prior = priors.Lognormal(1.5), name = 'alpha' ) beta = Hyperparameter( initial_value = 0.5*np.ones(D), prior = priors.Lognormal(1.5), name = 'beta' ) bw = KumarWarp(D, alpha=alpha, beta=beta) data = 0.5*np.ones(D) v = npr.randn(D) bw.forward_pass(data) assert np.all(bw.backward_pass(v) == 0.5773502691896257*v)
def test_forward_pass(): npr.seed(1) N = 10 D = 3 alpha = Hyperparameter(initial_value=2 * np.ones(D), prior=priors.Lognormal(1.5), name='alpha') beta = Hyperparameter(initial_value=0.5 * np.ones(D), prior=priors.Lognormal(1.5), name='beta') bw = KumarWarp(D, alpha=alpha, beta=beta) data = 0.5 * np.ones(D) assert np.all(bw.forward_pass(data) == 0.1339745962155614)
def test_forward_pass(): npr.seed(1) N = 10 D = 3 alpha = Hyperparameter( initial_value = 2*np.ones(D), prior = priors.Lognormal(1.5), name = 'alpha' ) beta = Hyperparameter( initial_value = 0.5*np.ones(D), prior = priors.Lognormal(1.5), name = 'beta' ) bw = KumarWarp(D, alpha=alpha, beta=beta) data = 0.5*np.ones(D) assert np.all(bw.forward_pass(data) == 0.1339745962155614)