esiivola / gpygradients Goto Github PK

# Kernel
se = GPy.kern.RBF(input_dim=1, lengthscale=.1, variance=0.01)
se_d = GPy.kern.DiffKern(se, 0)

# Likelihoods
bernoulli = GPy.likelihoods.Bernoulli(gp_link=GPy.likelihoods.link_functions.Probit(nu=100, fixed=False))
probit = GPy.likelihoods.Binomial(gp_link=GPy.likelihoods.link_functions.Probit(nu=100, fixed=False))

# Model
m = GPy.core.MultioutputGP(X_list=[X, Xd], Y_list=[y, yd],
                           kernel_list=[se, se_d],
                           likelihood_list=[bernoulli, probit],
                           inference_method=GPy.inference.latent_function_inference.EP(ep_mode="nested"))

Traceback (most recent call last):
  File "<input>", line 14, in <module>
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/parameterized.py", line 54, in __call__
    self.initialize_parameter()
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/parameter_core.py", line 331, in initialize_parameter
    self.trigger_update()
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/updateable.py", line 79, in trigger_update
    self._trigger_params_changed(trigger_parent)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/parameter_core.py", line 128, in _trigger_params_changed
    self.notify_observers(None, None if trigger_parent else -np.inf)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/observable.py", line 91, in notify_observers
    [callble(self, which=which) for _, _, callble in self.observers]
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/observable.py", line 91, in <listcomp>
    [callble(self, which=which) for _, _, callble in self.observers]
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/paramz/core/parameter_core.py", line 498, in _parameters_changed_notification
    self.parameters_changed()
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/core/gp.py", line 193, in parameters_changed
    self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y_normalized, self.mean_function, self.Y_metadata)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/inference/latent_function_inference/expectation_propagation.py", line 189, in inference
    return self._inference(Y, K, ga_approx, cav_params, likelihood, Y_metadata=Y_metadata,  Z_tilde=log_Z_tilde)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/inference/latent_function_inference/expectation_propagation.py", line 297, in _inference
    dL_dthetaL = likelihood.ep_gradients(Y, cav_params.tau, cav_params.v, np.diag(dL_dK), Y_metadata=Y_metadata, quad_mode='gh')
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/likelihoods/mixed_noise.py", line 71, in ep_gradients
    grads[j:s] += self.likelihoods_list[k].ep_gradients(Y[ind==k,:], cav_tau[ind==k], cav_v[ind==k], dL_dKdiag = dL_dKdiag[ind==k], Y_metadata=Y_metadata, quad_mode=quad_mode, boost_grad=boost_grad)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/likelihoods/likelihood.py", line 256, in ep_gradients
    quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten())))
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/likelihoods/likelihood.py", line 317, in integrate_gh
    b = self.dlogpdf_dtheta(X, Y, Y_metadata_i)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/likelihoods/likelihood.py", line 667, in dlogpdf_dtheta
    return self.dlogpdf_link_dtheta(inv_link_f, y, Y_metadata=Y_metadata)
  File "/Users/jwenger/.virtualenvs/mathesis/lib/python3.6/site-packages/GPy/likelihoods/likelihood.py", line 495, in dlogpdf_link_dtheta
    raise NotImplementedError
NotImplementedError