refactoring files added

GPy/models/gplvm.py

@@ -0,0 +1,67 @@
+### 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 .. import kern
+from ..core import model
+from ..util.linalg import pdinv, PCA
+from ..core import GP
+from ..likelihoods import Gaussian
+from .. import util
+from GPy.util import plot_latent
+
+
+class GPLVM(GP):
+    """
+    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'
+
+    """
+    def __init__(self, Y, input_dim, init='PCA', X = None, kernel=None, normalize_Y=False):
+        if X is None:
+            X = self.initialise_latent(init, input_dim, Y)
+        if kernel is None:
+            kernel = kern.rbf(input_dim, ARD=input_dim>1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
+        likelihood = Gaussian(Y, normalize=normalize_Y)
+        GP.__init__(self, X, likelihood, kernel, normalize_X=False)
+        self._set_params(self._get_params())
+
+    def initialise_latent(self, init, input_dim, Y):
+        if init == 'PCA':
+            return PCA(Y, input_dim)[0]
+        else:
+            return np.random.randn(Y.shape[0], input_dim)
+
+    def _get_param_names(self):
+        return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.N)],[]) + GP._get_param_names(self)
+
+    def _get_params(self):
+        return np.hstack((self.X.flatten(), GP._get_params(self)))
+
+    def _set_params(self,x):
+        self.X = x[:self.N*self.input_dim].reshape(self.N,self.input_dim).copy()
+        GP._set_params(self, x[self.X.size:])
+
+    def _log_likelihood_gradients(self):
+        dL_dX = 2.*self.kern.dK_dX(self.dL_dK,self.X)
+
+        return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
+
+    def plot(self):
+        assert self.likelihood.Y.shape[1]==2
+        pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
+        Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
+        mu, var, upper, lower = self.predict(Xnew)
+        pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
+
+    def plot_latent(self, *args, **kwargs):
+        return util.plot_latent.plot_latent(self, *args, **kwargs)