Added simple back constrained GPLVM model.

GPy/models/bcgplvm.py

@@ -0,0 +1,50 @@
+# ## 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 ..core import GP
+from ..models import GPLVM
+from ..mappings import *
+
+
+class BCGPLVM(GPLVM):
+    """
+    Back constrained Gaussian Process Latent Variable Model
+
+    :param Y: observed data
+    :type Y: np.ndarray
+    :param input_dim: latent dimensionality
+    :type input_dim: int
+    :param init: initialisation method for the latent space
+    :type init: 'PCA'|'random'
+    :param mapping: mapping for back constraint
+    :type mapping: GPy.core.Mapping object
+
+    """
+    def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False, mapping=None):
+        
+        if mapping is None:
+            mapping = Kernel(X=Y, output_dim=input_dim)
+        self.mapping = mapping
+        GPLVM.__init__(self, Y, input_dim, init, X, kernel, normalize_Y)
+        self.X = self.mapping.f(self.likelihood.Y)
+
+    def _get_param_names(self):
+        return self.mapping._get_param_names() + GP._get_param_names(self)
+
+    def _get_params(self):
+        return np.hstack((self.mapping._get_params(), GP._get_params(self)))
+
+    def _set_params(self, x):
+        self.mapping._set_params(x[:self.mapping.num_params])
+        self.X = self.mapping.f(self.likelihood.Y)
+        GP._set_params(self, x[self.mapping.num_params:])
+
+    def _log_likelihood_gradients(self):
+        dL_df = 2.*self.kern.dK_dX(self.dL_dK, self.X)
+        dL_dtheta = self.mapping.df_dtheta(dL_df, self.likelihood.Y)
+        return np.hstack((dL_dtheta.flatten(), GP._log_likelihood_gradients(self)))
+