models

GPy/models/sparse_GPLVM.py

@@ -0,0 +1,57 @@
+import numpy as np
+import pylab as pb
+import sys, pdb
+# from .. import kern
+# from ..core import model
+# from ..util.linalg import pdinv, PCA
+from GPLVM import GPLVM
+from sparse_GP_regression import sparse_GP_regression
+
+class sparse_GPLVM(sparse_GP_regression, GPLVM):
+    """
+    Sparse 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, init='PCA', **kwargs):
+        X = self.initialise_latent(init, Q, Y)
+        sparse_GP_regression.__init__(self, X, Y, **kwargs)
+
+    def get_param_names(self):
+        return (sum([['X_%i_%i'%(n,q) for n in range(self.N)] for q in range(self.Q)],[])
+                + sparse_GP_regression.get_param_names(self))
+
+    def get_param(self):
+        return np.hstack((self.X.flatten(), sparse_GP_regression.get_param(self)))
+
+    def set_param(self,x):
+        self.X = x[:self.X.size].reshape(self.N,self.Q).copy()
+        sparse_GP_regression.set_param(self, x[self.X.size:])
+
+    def log_likelihood(self):
+        return sparse_GP_regression.log_likelihood(self)
+
+    def dL_dX(self):
+        dpsi0_dX = self.kern.dKdiag_dX(self.X)
+        dpsi1_dX = self.kern.dK_dX(self.X,self.Z)
+        dpsi2_dX = self.psi1[:,None,:,None]*dpsi1_dX[None,:,:,:]
+
+        dL_dX = ((self.dL_dpsi0 * dpsi0_dX).sum(0)
+                 + (self.dL_dpsi1[:,:,None]*dpsi1_dX).sum(0)
+                 + 2.0*(self.dL_dpsi2[:, :, None,None] * dpsi2_dX).sum(0).sum(0))
+
+        return dL_dX
+
+    def log_likelihood_gradients(self):
+        return np.hstack((self.dL_dX().flatten(), sparse_GP_regression.log_likelihood_gradients(self)))
+
+    def plot(self):
+        GPLVM.plot(self)
+        mu, var = sparse_GP_regression.predict(self, self.Z+np.random.randn(*self.Z.shape)*0.0001)
+        pb.plot(mu[:, 0] , mu[:, 1], 'ko')