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[psi2] implement RBF cpu
This commit is contained in:
Zhenwen Dai
parent
001db6b089
commit
a2203179f6
5 changed files with 92 additions and 75 deletions
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@ -107,5 +107,3 @@ class SparseGP(GP):
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psi2 = kern.psi2(self.Z, Xnew)
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var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
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return mu, var
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@ -7,7 +7,7 @@ The package for the psi statistics computation
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import numpy as np
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from . import PSICOMP
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from GPy.util.caching import Cache_this,Cacher
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from GPy.util.caching import Cache_this
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class PSICOMP_SSRBF(PSICOMP):
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@ -8,8 +8,7 @@ from ...util.misc import param_to_array
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from stationary import Stationary
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from GPy.util.caching import Cache_this
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from ...core.parameterization import variational
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from psi_comp import ssrbf_psi_comp
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from psi_comp import ssrbf_psi_gpucomp
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from psi_comp import ssrbf_psi_comp,ssrbf_psi_gpucomp,rbf_psi_comp
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from ...util.config import *
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class RBF(Stationary):
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@ -26,6 +25,7 @@ class RBF(Stationary):
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super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
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self.weave_options = {}
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self.group_spike_prob = False
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self.psicomp = rbf_psi_comp.PSICOMP_RBF()
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def set_for_SpikeAndSlab(self):
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if self.useGPU:
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@ -50,7 +50,8 @@ class RBF(Stationary):
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else:
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[0]
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else:
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return self.Kdiag(variational_posterior.mean)
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# return self.Kdiag(variational_posterior.mean)
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[0]
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def psi1(self, Z, variational_posterior):
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if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
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@ -59,8 +60,7 @@ class RBF(Stationary):
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else:
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[1]
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else:
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_, _, _, psi1 = self._psi1computations(Z, variational_posterior)
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return psi1
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[1]
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def psi2(self, Z, variational_posterior):
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if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
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@ -69,8 +69,7 @@ class RBF(Stationary):
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else:
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[2]
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else:
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_, _, _, _, psi2 = self._psi2computations(Z, variational_posterior)
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return psi2
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return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior)[2]
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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# Spike-and-Slab GPLVM
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@ -83,32 +82,37 @@ class RBF(Stationary):
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self.lengthscale.gradient = dL_dlengscale
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elif isinstance(variational_posterior, variational.NormalPosterior):
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l2 = self.lengthscale**2
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if l2.size != self.input_dim:
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l2 = l2*np.ones(self.input_dim)
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dL_dvar, dL_dlengscale, _, _, _ = self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
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self.variance.gradient = dL_dvar
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self.lengthscale.gradient = dL_dlengscale
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#contributions from psi0:
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self.variance.gradient = np.sum(dL_dpsi0)
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self.lengthscale.gradient = 0.
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#from psi1
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denom, _, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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d_length = psi1[:,:,None] * ((dist_sq - 1.)/(self.lengthscale*denom) +1./self.lengthscale)
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dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
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if self.ARD:
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self.lengthscale.gradient += dpsi1_dlength.sum(0).sum(0)
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else:
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self.lengthscale.gradient += dpsi1_dlength.sum()
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self.variance.gradient += np.sum(dL_dpsi1 * psi1) / self.variance
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#from psi2
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S = variational_posterior.variance
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_, Zdist_sq, _, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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if not self.ARD:
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self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2).sum()
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else:
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self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2)
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self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance
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# l2 = self.lengthscale**2
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# if l2.size != self.input_dim:
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# l2 = l2*np.ones(self.input_dim)
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# #contributions from psi0:
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# self.variance.gradient = np.sum(dL_dpsi0)
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# self.lengthscale.gradient = 0.
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#
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# # #from psi1
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# denom, _, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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# d_length = psi1[:,:,None] * ((dist_sq - 1.)/(self.lengthscale*denom) +1./self.lengthscale)
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# dpsi1_dlength = d_length * dL_dpsi1[:, :, None]
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# print dpsi1_dlength.sum(0).sum(0)
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# if self.ARD:
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# self.lengthscale.gradient += dpsi1_dlength.sum(0).sum(0)
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# else:
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# self.lengthscale.gradient += dpsi1_dlength.sum()
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# self.variance.gradient += np.sum(dL_dpsi1 * psi1) / self.variance
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# #from psi2
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# S = variational_posterior.variance
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# _, Zdist_sq, _, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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# if not self.ARD:
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# self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2).sum()
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# else:
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# self.lengthscale.gradient += self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2)
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# # print self._weave_psi2_lengthscale_grads(dL_dpsi2, psi2, Zdist_sq, S, mudist_sq, l2)
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#
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# self.variance.gradient += 2.*np.sum(dL_dpsi2 * psi2)/self.variance
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else:
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raise ValueError, "unknown distriubtion received for psi-statistics"
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@ -123,21 +127,24 @@ class RBF(Stationary):
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return dL_dZ
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elif isinstance(variational_posterior, variational.NormalPosterior):
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l2 = self.lengthscale **2
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#psi1
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denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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grad = np.einsum('ij,ij,ijk,ijk->jk', dL_dpsi1, psi1, dist, -1./(denom*l2))
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#psi2
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Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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term1 = Zdist / l2 # M, M, Q
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S = variational_posterior.variance
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term2 = mudist / (2.*S[:,None,None,:] + l2) # N, M, M, Q
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grad += 2.*np.einsum('ijk,ijk,ijkl->kl', dL_dpsi2, psi2, term1[None,:,:,:] + term2)
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return grad
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_, _, dL_dZ, _, _ = self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
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return dL_dZ
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#
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# l2 = self.lengthscale **2
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#
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# #psi1
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# denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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# grad = np.einsum('ij,ij,ijk,ijk->jk', dL_dpsi1, psi1, dist, -1./(denom*l2))
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#
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# #psi2
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# Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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# term1 = Zdist / l2 # M, M, Q
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# S = variational_posterior.variance
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# term2 = mudist / (2.*S[:,None,None,:] + l2) # N, M, M, Q
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#
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# grad += 2.*np.einsum('ijk,ijk,ijkl->kl', dL_dpsi2, psi2, term1[None,:,:,:] + term2)
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#
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# return grad
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else:
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raise ValueError, "unknown distriubtion received for psi-statistics"
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@ -151,24 +158,27 @@ class RBF(Stationary):
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return dL_dmu, dL_dS, dL_dgamma
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elif isinstance(variational_posterior, variational.NormalPosterior):
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l2 = self.lengthscale **2
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#psi1
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denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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tmp = psi1[:, :, None] / l2 / denom
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grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1)
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grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1)
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#psi2
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_, _, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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S = variational_posterior.variance
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tmp = psi2[:, :, :, None] / (2.*S[:,None,None,:] + l2)
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grad_mu += -2.*np.einsum('ijk,ijkl,ijkl->il', dL_dpsi2, tmp , mudist)
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grad_S += np.einsum('ijk,ijkl,ijkl->il', dL_dpsi2 , tmp , (2.*mudist_sq - 1))
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_, _, _, dL_dmu, dL_dS = self.psicomp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
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# l2 = self.lengthscale **2
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# #psi1
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# denom, dist, dist_sq, psi1 = self._psi1computations(Z, variational_posterior)
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# tmp = psi1[:, :, None] / l2 / denom
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# grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1)
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# grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1)
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# #psi2
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# _, _, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
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# S = variational_posterior.variance
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# tmp = psi2[:, :, :, None] / (2.*S[:,None,None,:] + l2)
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# grad_mu += -2.*np.einsum('jk,ijkl,ijkl->il', dL_dpsi2, tmp , mudist)
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# grad_S += np.einsum('jk,ijkl,ijkl->il', dL_dpsi2 , tmp , (2.*mudist_sq - 1))
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return dL_dmu, dL_dS
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else:
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raise ValueError, "unknown distriubtion received for psi-statistics"
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return grad_mu, grad_S
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#return grad_mu, grad_S
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#---------------------------------------#
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# Precomputations #
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@ -8,7 +8,7 @@ from ..likelihoods import Gaussian
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from ..inference.optimization import SCG
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from ..util import linalg
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from ..core.parameterization.variational import NormalPosterior, NormalPrior, VariationalPosterior
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from ..inference.latent_function_inference.var_dtc_parallel import update_gradients
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from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
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from ..inference.latent_function_inference.var_dtc_gpu import VarDTC_GPU
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class BayesianGPLVM(SparseGP):
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@ -67,7 +67,7 @@ class BayesianGPLVM(SparseGP):
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X.mean.gradient, X.variance.gradient = X_grad
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def parameters_changed(self):
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if isinstance(self.inference_method, VarDTC_GPU):
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if isinstance(self.inference_method, VarDTC_GPU) or isinstance(self.inference_method, VarDTC_minibatch):
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update_gradients(self)
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return
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@ -89,7 +89,7 @@ class vector_show(matplotlib_show):
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class lvm(matplotlib_show):
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1], disable_drag=False):
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"""Visualize a latent variable model
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:param model: the latent variable model to visualize.
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@ -108,12 +108,14 @@ class lvm(matplotlib_show):
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if isinstance(latent_axes,mpl.axes.Axes):
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self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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if not disable_drag:
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self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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else:
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self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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if not disable_drag:
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self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
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self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
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@ -125,6 +127,7 @@ class lvm(matplotlib_show):
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self.move_on = False
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self.latent_index = latent_index
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self.latent_dim = model.input_dim
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self.disable_drag = disable_drag
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# The red cross which shows current latent point.
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self.latent_values = vals
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@ -149,8 +152,13 @@ class lvm(matplotlib_show):
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def on_click(self, event):
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print 'click!'
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if event.inaxes!=self.latent_axes: return
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self.move_on = not self.move_on
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self.called = True
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if self.disable_drag:
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self.move_on = True
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self.called = True
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self.on_move(event)
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else:
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self.move_on = not self.move_on
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self.called = True
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def on_move(self, event):
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if event.inaxes!=self.latent_axes: return
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@ -400,7 +408,7 @@ class mocap_data_show(matplotlib_show):
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def __init__(self, vals, axes=None, connect=None):
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if axes==None:
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fig = plt.figure()
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axes = fig.add_subplot(111, projection='3d',aspect='equal')
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axes = fig.add_subplot(111, projection='3d', aspect='equal')
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matplotlib_show.__init__(self, vals, axes)
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self.connect = connect
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@ -438,6 +446,7 @@ class mocap_data_show(matplotlib_show):
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self.process_values()
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self.initialize_axes_modify()
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self.draw_vertices()
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self.initialize_axes()
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self.finalize_axes_modify()
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self.draw_edges()
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self.axes.figure.canvas.draw()
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@ -460,10 +469,10 @@ class mocap_data_show(matplotlib_show):
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self.axes.set_xlim(self.x_lim)
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self.axes.set_ylim(self.y_lim)
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self.axes.set_zlim(self.z_lim)
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self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1.5, 1.5])
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self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1., 1.])
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#self.axes.set_aspect('equal')
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self.axes.autoscale(enable=False)
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# self.axes.set_aspect('equal')
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# self.axes.autoscale(enable=False)
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def finalize_axes_modify(self):
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self.axes.set_xlim(self.x_lim)
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