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some tidying in gp.py
This commit is contained in:
James Hensman
parent
bddb22f4af
commit
683f45366b
2 changed files with 22 additions and 167 deletions
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@ -27,12 +27,6 @@ class GP(GPBase):
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GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
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self._set_params(self._get_params())
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def getstate(self):
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return GPBase.getstate(self)
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def setstate(self, state):
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GPBase.setstate(self, state)
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self._set_params(self._get_params())
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def _set_params(self, p):
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self.kern._set_params_transformed(p[:self.kern.num_params_transformed()])
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@ -101,12 +95,7 @@ class GP(GPBase):
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Note, we use the chain rule: dL_dtheta = dL_dK * d_K_dtheta
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"""
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#return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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if not isinstance(self.likelihood,EP):
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tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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else:
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tmp = np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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return tmp
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return np.hstack((self.kern.dK_dtheta(dL_dK=self.dL_dK, X=self.X), self.likelihood._gradients(partial=np.diag(self.dL_dK))))
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def _raw_predict(self, _Xnew, which_parts='all', full_cov=False, stop=False):
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"""
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@ -193,3 +182,11 @@ class GP(GPBase):
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"""
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Xnew = self._add_output_index(Xnew, output)
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return self.predict(Xnew, which_parts=which_parts, full_cov=full_cov, likelihood_args=likelihood_args)
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def getstate(self):
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return GPBase.getstate(self)
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def setstate(self, state):
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GPBase.setstate(self, state)
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self._set_params(self._get_params())
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@ -52,23 +52,6 @@ class SparseGP(GPBase):
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self._const_jitter = None
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def getstate(self):
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"""
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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"""
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return GPBase.getstate(self) + [self.Z,
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self.num_inducing,
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self.has_uncertain_inputs,
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self.X_variance]
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def setstate(self, state):
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self.X_variance = state.pop()
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self.has_uncertain_inputs = state.pop()
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self.num_inducing = state.pop()
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self.Z = state.pop()
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GPBase.setstate(self, state)
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def _compute_kernel_matrices(self):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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@ -87,7 +70,6 @@ class SparseGP(GPBase):
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# factor Kmm
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self._Lm = jitchol(self.Kmm + self._const_jitter)
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# TODO: no white kernel needed anymore, all noise in likelihood --------
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# The rather complex computations of self._A
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if self.has_uncertain_inputs:
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@ -421,145 +403,21 @@ class SparseGP(GPBase):
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
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def getstate(self):
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"""
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For a specific output, predict the function at the new point(s) Xnew.
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param output: output to predict
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:type output: integer in {0,..., num_outputs-1}
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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:type full_cov: bool
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:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
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:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
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:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
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.. Note:: For multiple output models only
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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"""
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return GPBase.getstate(self) + [self.Z,
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self.num_inducing,
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self.has_uncertain_inputs,
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self.X_variance]
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assert hasattr(self,'multioutput')
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index = np.ones_like(Xnew)*output
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Xnew = np.hstack((Xnew,index))
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# normalize X values
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Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
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mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
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# now push through likelihood
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
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return mean, var, _025pm, _975pm
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def _raw_predict_single_output(self, _Xnew, output=0, X_variance_new=None, which_parts='all', full_cov=False,stop=False):
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"""
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Internal helper function for making predictions for a specific output,
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does not account for normalization or likelihood
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---------
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param output: output to predict
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:type output: integer in {0,..., num_outputs-1}
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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.. Note:: For multiple output models only
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"""
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Bi, _ = dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
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symmetrify(Bi)
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Kmmi_LmiBLmi = backsub_both_sides(self._Lm, np.eye(self.num_inducing) - Bi)
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if self.Cpsi1V is None:
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psi1V = np.dot(self.psi1.T,self.likelihood.V)
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tmp, _ = dtrtrs(self._Lm, np.asfortranarray(psi1V), lower=1, trans=0)
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tmp, _ = dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, _ = dtrtrs(self._Lm, tmp, lower=1, trans=1)
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assert hasattr(self,'multioutput')
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index = np.ones_like(_Xnew)*output
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_Xnew = np.hstack((_Xnew,index))
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if X_variance_new is None:
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Kx = self.kern.K(self.Z, _Xnew, which_parts=which_parts)
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mu = np.dot(Kx.T, self.Cpsi1V)
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if full_cov:
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Kxx = self.kern.K(_Xnew, which_parts=which_parts)
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var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
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else:
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Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
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var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
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else:
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Kx = self.kern.psi1(self.Z, _Xnew, X_variance_new)
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mu = np.dot(Kx, self.Cpsi1V)
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if full_cov:
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raise NotImplementedError, "TODO"
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else:
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Kxx = self.kern.psi0(self.Z, _Xnew, X_variance_new)
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psi2 = self.kern.psi2(self.Z, _Xnew, X_variance_new)
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var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
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return mu, var[:, None]
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def setstate(self, state):
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self.X_variance = state.pop()
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self.has_uncertain_inputs = state.pop()
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self.num_inducing = state.pop()
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self.Z = state.pop()
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GPBase.setstate(self, state)
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def plot_single_output_f(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_single_output_f(self, output=output, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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if self.X.shape[1] == 2:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:1]
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ax.plot(Zu[:,0], np.zeros_like(Zu[:,0]) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:2]
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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def plot_single_output(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_single_output(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax, output=output)
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if self.X.shape[1] == 2:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:1]
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 3:
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:1]
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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