mrd touches

GPy/models/mrd.py

@@ -0,0 +1,139 @@
+'''
+Created on 10 Apr 2013
+
+@author: Max Zwiessele
+'''
+from GPy.core import model
+from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
+import numpy
+from GPy.models.sparse_GP import sparse_GP
+import itertools
+
+
+class MRD(model):
+    """
+    Do MRD on given Datasets in Ylist.
+    All Ys in Ylist are in [N x Dn], where Dn can be different per Yn,
+    N must be shared across datasets though.
+
+    :param Ylist...: observed datasets
+    :type Ylist: [np.ndarray]
+    :param names: names for different gplvm models
+    :type names: [str]
+    :param Q: latent dimensionality
+    :type Q: int
+    :param init: initialisation method for the latent space
+    :type init: 'PCA'|'random'
+    :param X:
+        Initial latent space
+    :param X_variance:
+        Initial latent space variance
+    :param init: [PCA|random]
+        initialization method to use
+    :param M:
+        number of inducing inputs to use
+    :param Z:
+        initial inducing inputs
+    :param kernel:
+        kernel to use
+    """
+    def __init__(self, *Ylist, **kwargs):
+        self._debug = False
+        if kwargs.has_key("_debug"):
+            self._debug = kwargs['_debug']
+            del kwargs['_debug']
+        if kwargs.has_key("names"):
+            self.names = kwargs['names']
+            del kwargs['names']
+        else:
+            self.names = ["{}".format(i + 1) for i in range(len(Ylist))]
+        if kwargs.has_key('kernel'):
+            kernel = kwargs['kernel']
+            k = lambda: kernel.copy()
+            del kwargs['kernel']
+        else:
+            k = lambda: None
+        self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), **kwargs) for Y in Ylist]
+        self.gref = self.bgplvms[0]
+        nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
+        self.nparams = nparams.cumsum()
+        self.Q = self.gref.Q
+        self.N = self.gref.N
+        self.NQ = self.N * self.Q
+        self.M = self.gref.M
+        self.MQ = self.M * self.Q
+
+        model.__init__(self)  # @UndefinedVariable
+
+    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([['X_variance_%i_%i' % (n, q) for q in range(self.Q)] for n in range(self.N)], [])
+        n1 = self.gref._get_param_names()
+        n1var = n1[:self.NQ * 2 + self.MQ]
+        map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
+                                         itertools.izip(ns,
+                                                        itertools.repeat(name)))
+        return list(itertools.chain(n1var, *(map_names(\
+                sparse_GP._get_param_names(g)[self.MQ:], n) \
+                for g, n in zip(self.bgplvms, self.names))))
+
+    def _get_params(self):
+        """
+        return parameter list containing private and shared parameters as follows:
+        
+        =================================================================
+        | mu | S | Z || theta1 | theta2 | .. | thetaN |
+        =================================================================
+        """
+        X = self.gref.X.flatten()
+        X_var = self.gref.X_variance.flatten()
+        Z = self.gref.Z.flatten()
+        thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
+        params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
+        return params
+
+    def _set_var_params(self, g, X, X_var, Z):
+        g.X = X
+        g.X_variance = X_var
+        g.Z = Z
+
+    def _set_kern_params(self, g, p):
+        g.kern._set_params(p[:g.kern.Nparam])
+        g.likelihood._set_params(p[g.kern.Nparam:])
+
+    def _set_params(self, x):
+        start = 0; end = self.NQ
+        X = x[start:end].reshape(self.N, self.Q)
+        start = end; end += start
+        X_var = x[start:end].reshape(self.N, self.Q)
+        start = end; end += self.MQ
+        Z = x[start:end].reshape(self.M, self.Q)
+        thetas = x[end:]
+
+        # set params for all others:
+        for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]):
+            self._set_var_params(g, X, X_var, Z)
+            self._set_kern_params(g, thetas[s:e])
+            g._compute_kernel_matrices()
+            g._computations()
+
+
+    def log_likelihood(self):
+        ll = self.gref.KL_divergence()
+        for g in self.bgplvms:
+            ll += sparse_GP.log_likelihood(g)
+        return ll
+
+    def _log_likelihood_gradients(self):
+        dldmus = self.gref.dL_dmuS().flatten()
+        dldzt1 = sparse_GP._log_likelihood_gradients(self.gref)
+        return numpy.hstack((dldmus,
+                             dldzt1,
+                numpy.hstack([numpy.hstack([g.dL_dtheta(),
+                                            g.likelihood._gradients(\
+                                                partial=g.partial_for_likelihood)]) \
+                              for g in self.bgplvms[1:]])))
+
+    def plot_scales(self):
+        pass
+