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some tidy up
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Zhenwen Dai
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703dbcabe2
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c6d0e7f1d1
3 changed files with 0 additions and 143 deletions
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@ -3,14 +3,11 @@
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import numpy as np
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from scipy import weave
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from kern import Kern
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from ...util.linalg import tdot
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from ...util.misc import param_to_array
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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from ...util.caching import Cache_this
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from ...core.parameterization import variational
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from ...util.config import *
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from .psi_comp import PSICOMP_Linear
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@ -1,139 +0,0 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from kern import Kern
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import numpy as np
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from ...util.linalg import tdot
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from ...util.config import *
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from stationary import Stationary
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from psi_comp import ssrbf_psi_comp
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class SSRBF(Stationary):
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"""
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Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel
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for Spike-and-Slab GPLVM
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.. math::
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k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg) \ \ \ \ \ \\text{ where } r^2 = \sum_{i=1}^d \\frac{ (x_i-x^\prime_i)^2}{\ell_i^2}
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where \ell_i is the lengthscale, \sigma^2 the variance and d the dimensionality of the input.
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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 variance: the variance of the kernel
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:type variance: float
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:param lengthscale: the vector of lengthscale of the kernel
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:type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
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:param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
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:type ARD: Boolean
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:rtype: kernel object
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.. Note: this object implements both the ARD and 'spherical' version of the function
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"""
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def __init__(self, input_dim, variance=1., lengthscale=None, ARD=True, active_dims=None, name='SSRBF'):
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assert ARD==True, "Not Implemented!"
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super(SSRBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
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def K_of_r(self, r):
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return self.variance * np.exp(-0.5 * r**2)
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def dK_dr(self, r):
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return -r*self.K_of_r(r)
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def parameters_changed(self):
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pass
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def Kdiag(self, X):
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ret = np.empty(X.shape[0])
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ret[:] = self.variance
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return ret
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#---------------------------------------#
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# PSI statistics #
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#---------------------------------------#
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def psi0(self, Z, variational_posterior):
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ret = np.empty(variational_posterior.mean.shape[0])
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ret[:] = self.variance
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return ret
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def psi1(self, Z, variational_posterior):
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_psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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return _psi1
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def psi2(self, Z, variational_posterior):
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_psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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return _psi2
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def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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_, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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#contributions from psi0:
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self.variance.gradient = np.sum(dL_dpsi0)
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#from psi1
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self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
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self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
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#from psi2
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self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
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self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
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def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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#psi1
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grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0)
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#psi2
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grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1)
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return grad
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def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
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ndata = variational_posterior.mean.shape[0]
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_, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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_, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
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#psi1
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grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1)
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grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1)
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grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1)
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#psi2
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grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1)
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grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1)
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grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1)
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return grad_mu, grad_S, grad_gamma
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#---------------------------------------#
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# Precomputations #
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#---------------------------------------#
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#@cache_this(1)
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def _K_computations(self, X, X2):
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"""
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K(X,X2) - X is NxQ
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Q -> input dimension (self.input_dim)
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"""
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if X2 is None:
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self._X2 = None
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X = X / self.lengthscale
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Xsquare = np.sum(np.square(X), axis=1)
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self._K_dist2 = -2.*tdot(X) + (Xsquare[:, None] + Xsquare[None, :])
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else:
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self._X2 = X2.copy()
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X = X / self.lengthscale
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X2 = X2 / self.lengthscale
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self._K_dist2 = -2.*np.dot(X, X2.T) + (np.sum(np.square(X), axis=1)[:, None] + np.sum(np.square(X2), axis=1)[None, :])
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self._K_dvar = np.exp(-0.5 * self._K_dist2)
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@ -6,7 +6,6 @@ from kern import Kern
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import numpy as np
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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import numpy as np
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class Static(Kern):
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def __init__(self, input_dim, variance, active_dims, name):
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