changed the filename from BGPLVM to Bayesian_GPLVM to tidy the namespace a little

GPy/models/Bayesian_GPLVM.py

@@ -0,0 +1,85 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+import pylab as pb
+import sys, pdb
+from GPLVM import GPLVM
+from sparse_GP import sparse_GP
+from GPy.util.linalg import pdinv
+from ..likelihoods import Gaussian
+from .. import kern
+
+class Bayesian_GPLVM(sparse_GP, GPLVM):
+    """
+    Bayesian Gaussian Process Latent Variable Model
+
+    :param Y: observed data
+    :type Y: np.ndarray
+    :param Q: latent dimensionality
+    :type Q: int
+    :param init: initialisation method for the latent space
+    :type init: 'PCA'|'random'
+
+    """
+    def __init__(self, Y, Q, X = None, S = None, init='PCA', M=10, Z=None, kernel=None, **kwargs):
+        if X == None:
+            X = self.initialise_latent(init, Q, Y)
+
+        if S is None:
+            S = np.ones_like(X) * 1e-2#
+
+        if Z is None:
+            Z = np.random.permutation(X.copy())[:M]
+        assert Z.shape[1]==X.shape[1]
+
+        if kernel is None:
+            kernel = kern.rbf(Q) + kern.white(Q)
+
+
+        sparse_GP.__init__(self, X, Gaussian(Y), kernel, Z=Z, X_uncertainty=S, **kwargs)
+
+    def _get_param_names(self):
+        X_names = sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
+        S_names = sum([['S_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[])
+        return (X_names + S_names + sparse_GP._get_param_names(self))
+
+    def _get_params(self):
+        """
+        Horizontally stacks the parameters in order to present them to the optimizer.
+        The resulting 1-D array has this structure:
+
+        ===============================================================
+        |       mu       |        S        |    Z    | theta |  beta  |
+        ===============================================================
+
+        """
+        return np.hstack((self.X.flatten(), self.X_uncertainty.flatten(), sparse_GP._get_params(self)))
+
+    def _set_params(self,x):
+        N, Q = self.N, self.Q
+        self.X = x[:self.X.size].reshape(N,Q).copy()
+        self.X_uncertainty = x[(N*Q):(2*N*Q)].reshape(N,Q).copy()
+        sparse_GP._set_params(self, x[(2*N*Q):])
+
+    def dL_dmuS(self):
+        dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi1_dmuS(self.dL_dpsi1,self.Z,self.X,self.X_uncertainty)
+        dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi0_dmuS(self.dL_dpsi0,self.Z,self.X,self.X_uncertainty)
+        dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.dL_dpsi2,self.Z,self.X,self.X_uncertainty)
+        dL_dmu = dL_dmu_psi0 + dL_dmu_psi1 + dL_dmu_psi2
+        dL_dS = dL_dS_psi0 + dL_dS_psi1 + dL_dS_psi2
+
+        dKL_dS = (1. - (1./self.X_uncertainty))*0.5
+        dKL_dmu = self.X
+        return np.hstack(((dL_dmu - dKL_dmu).flatten(), (dL_dS - dKL_dS).flatten()))
+
+    def KL_divergence(self):
+        var_mean = np.square(self.X).sum()
+        var_S = np.sum(self.X_uncertainty - np.log(self.X_uncertainty))
+        return 0.5*(var_mean + var_S) - 0.5*self.Q*self.N
+
+    def log_likelihood(self):
+        return sparse_GP.log_likelihood(self) - self.KL_divergence()
+
+    def _log_likelihood_gradients(self):
+        return np.hstack((self.dL_dmuS().flatten(), sparse_GP._log_likelihood_gradients(self)))