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mucho changes to linear.py
This commit is contained in:
James Hensman
parent
e9957ed896
commit
ec6e85b1c1
4 changed files with 32 additions and 32 deletions
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@ -70,7 +70,7 @@ class SparseGP(GP):
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#gradients wrt Z
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self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z)
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self.Z.gradient += self.kern.gradients_Z_expectations(
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self.grad_dict['dL_dpsi1'], self.grad_dict['dL_dpis2'], Z=self.Z, variational_posterior=self.X)
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self.grad_dict['dL_dpsi1'], self.grad_dict['dL_dpsi2'], Z=self.Z, variational_posterior=self.X)
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else:
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#gradients wrt kernel
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target = np.zeros(self.kern.size)
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@ -274,6 +274,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
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_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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#k = kern.RBF(Q, ARD=True, lengthscale=10.)
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m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
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if optimize:
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@ -281,7 +282,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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m.q.plot("BGPLVM Latent Space 1D")
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m.X.plot("BGPLVM Latent Space 1D")
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m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
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return m
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@ -302,7 +303,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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m = BayesianGPLVM(Y.copy(), Q, init="random", num_inducing=num_inducing, kernel=k)
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m.inference_method = VarDTCMissingData()
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m.Y[inan] = _np.nan
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m.q.variance *= .1
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m.X.variance *= .1
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m.parameters_changed()
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m.Yreal = Y
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@ -311,7 +312,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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m.optimize('bfgs', messages=verbose, max_iters=max_iters,
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gtol=.05)
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if plot:
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m.q.plot("BGPLVM Latent Space 1D")
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m.X.plot("BGPLVM Latent Space 1D")
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m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
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return m
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@ -22,22 +22,25 @@ class Linear(Kern):
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:param input_dim: the number of input dimensions
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:type input_dim: int
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:param variances: the vector of variances :math:`\sigma^2_i`
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:type variances: array or list of the appropriate size (or float if there is only one variance parameter)
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:param ARD: Auto Relevance Determination. If equal to "False", the kernel has only one variance parameter \sigma^2, otherwise there is one variance parameter per dimension.
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:type variances: array or list of the appropriate size (or float if there
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is only one variance parameter)
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:param ARD: Auto Relevance Determination. If False, the kernel has only one
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variance parameter \sigma^2, otherwise there is one variance
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parameter per dimension.
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:type ARD: Boolean
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:rtype: kernel object
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"""
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def __init__(self, input_dim, variances=None, ARD=False, name='linear'):
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super(Linear, self).__init__(input_dim, name)
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self.ARD = ARD
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if ARD == False:
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if not ARD:
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if variances is not None:
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variances = np.asarray(variances)
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assert variances.size == 1, "Only one variance needed for non-ARD kernel"
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else:
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variances = np.ones(1)
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self._Xcache, self._X2cache = np.empty(shape=(2,))
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else:
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if variances is not None:
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variances = np.asarray(variances)
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@ -103,7 +106,6 @@ class Linear(Kern):
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#---------------------------------------#
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# PSI statistics #
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# variational #
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#---------------------------------------#
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def psi0(self, Z, variational_posterior):
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@ -118,18 +120,16 @@ class Linear(Kern):
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return np.dot(ZAinner, ZA.T)
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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mu, S = variational_posterior.mean, variational_posterior.variance
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#psi1
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self.update_gradients_full(dL_dpsi1, variational_posterior.mean, Z)
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# psi0:
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tmp = dL_dpsi0[:, None] * self._mu2S(variational_posterior)
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if self.ARD: grad = tmp.sum(0)
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else: grad = np.atleast_1d(tmp.sum())
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#psi1
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self.update_gradients_full(dL_dpsi1, mu, Z)
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grad += self.variances.gradient
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if self.ARD: self.variances.gradient += tmp.sum(0)
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else: self.variances.gradient += tmp.sum()
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#psi2
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tmp = dL_dpsi2[:, :, :, None] * (self._ZAinner(variational_posterior, Z)[:, :, None, :] * (2. * Z)[None, None, :, :])
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if self.ARD: grad += tmp.sum(0).sum(0).sum(0)
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else: grad += tmp.sum()
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if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
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else: self.variances.gradient += tmp.sum()
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def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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#psi1
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@ -155,7 +155,7 @@ class Linear(Kern):
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#--------------------------------------------------#
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def _weave_dpsi2_dmuS(self, dL_dpsi2, Z, pv, target_mu, target_S):
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def _weave_dpsi2_dmuS(self, dL_dpsi2, Z, vp, target_mu, target_S):
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# Think N,num_inducing,num_inducing,input_dim
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ZA = Z * self.variances
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AZZA = ZA.T[:, None, :, None] * ZA[None, :, None, :]
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@ -198,16 +198,15 @@ class Linear(Kern):
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weave_options = {'headers' : ['<omp.h>'],
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'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
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'extra_link_args' : ['-lgomp']}
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mu = pv.mean
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mu = vp.mean
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N,num_inducing,input_dim,mu = mu.shape[0],Z.shape[0],mu.shape[1],param_to_array(mu)
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weave.inline(code, support_code=support_code, libraries=['gomp'],
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arg_names=['N','num_inducing','input_dim','mu','AZZA','AZZA_2','target_mu','target_S','dL_dpsi2'],
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type_converters=weave.converters.blitz,**weave_options)
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def _weave_dpsi2_dZ(self, dL_dpsi2, Z, pv, target):
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AZA = self.variances*self._ZAinner(pv, Z)
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def _weave_dpsi2_dZ(self, dL_dpsi2, Z, vp, target):
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AZA = self.variances*self._ZAinner(vp, Z)
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code="""
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int n,m,mm,q;
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#pragma omp parallel for private(n,mm,q)
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@ -229,23 +228,23 @@ class Linear(Kern):
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'extra_compile_args': ['-fopenmp -O3'], #-march=native'],
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'extra_link_args' : ['-lgomp']}
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N,num_inducing,input_dim = pv.mean.shape[0],Z.shape[0],pv.mean.shape[1]
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mu = param_to_array(pv.mean)
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N,num_inducing,input_dim = vp.mean.shape[0],Z.shape[0],vp.mean.shape[1]
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mu = param_to_array(vp.mean)
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weave.inline(code, support_code=support_code, libraries=['gomp'],
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arg_names=['N','num_inducing','input_dim','AZA','target','dL_dpsi2'],
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type_converters=weave.converters.blitz,**weave_options)
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def _mu2S(self, pv):
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return np.square(pv.mean) + pv.variance
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def _mu2S(self, vp):
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return np.square(vp.mean) + vp.variance
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def _ZAinner(self, pv, Z):
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def _ZAinner(self, vp, Z):
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ZA = Z*self.variances
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inner = (pv.mean[:, None, :] * pv.mean[:, :, None])
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diag_indices = np.diag_indices(pv.mean.shape[1], 2)
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inner[:, diag_indices[0], diag_indices[1]] += pv.variance
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inner = (vp.mean[:, None, :] * vp.mean[:, :, None])
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diag_indices = np.diag_indices(vp.mean.shape[1], 2)
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inner[:, diag_indices[0], diag_indices[1]] += vp.variance
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return np.dot(ZA, inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [num_inducing x N x input_dim]!
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return np.dot(ZA, inner).swapaxes(0, 1) # NOTE: self.ZAinner \in [num_inducing x num_data x input_dim]!
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def input_sensitivity(self):
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if self.ARD: return self.variances
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@ -66,7 +66,7 @@ class BayesianGPLVM(SparseGP):
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super(BayesianGPLVM, self).parameters_changed()
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self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
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self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_q_variational(variational_posterior=self.X, Z=self.Z, **self.grad_dict)
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self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, **self.grad_dict)
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# update for the KL divergence
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self.variational_prior.update_gradients_KL(self.X)
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