Lots of tidying in the inference section

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -1,13 +1,53 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-def exact_gaussian_inference(K, likelihood, Y, Y_metadata=None):
+from posterior import Posterior
+from .../util.linalg import pdinv, dpotrs, tdot
+log_2_pi = np.log(2*np.pi)
 
 
-    Wi, LW, LWi, W_logdet = pdinv(K + likelhood.covariance(Y, Y_metadata))
+class exact_gaussian_inference(object):
+    """
+    An object for inference when the likelihood is Gaussian.
+
+    The function self.inference returns a Posterior object, which summarizes
+    the posterior.
+
+    For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.
+
+    """
+    def __init__(self):
+        self._YYTfactor_cache = caching.cache()
+
+     def get_YYTfactor(self, Y):
+        """
+        find a matrix L which satisfies LLT = YYT. 
+
+        Note that L may have fewer columns than Y.
+        """
+        N, D = Y.shape
+        if (N>D):
+            return Y
+        else:
+            #if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
+            raise NotImplementedError, 'TODO' #TODO
+
+    def inference(self, K, likelihood, Y, Y_metadata=None):
+        """
+        Returns a Posterior class containing essential quantities of the posterior
+        """
+        YYT_factor = self.get_YYTfactor(Y)
+
+        Wi, LW, LWi, W_logdet = pdinv(K + likelhood.covariance(Y, Y_metadata))
+
+        alpha, _ = dpotrs(LW, YYT_factor, lower=1)
+
+        dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
+
+        log_marginal =  0.5*(-Y.size * log_2_pi - Y.shape[1] * W_logdet - np.sum(alpha * YYT_factor.T)
+
+        dL_dtheta_lik = likelihood.dL_dtheta(np.diag(dL_dK))
+
+        return Posterior(log_marginal, dL_DK, dL_dtheta_lik, LW, alpha, K):
 
-    alpha, _ = dpotrs(LW, YYT_factor, lower=1)
-    dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
 
-    log_marginal =  (-0.5 * Y.size * np.log(2.*np.pi) -
-        0.5 * Y.shape[1] * W_logdet + np.sum(np.square(alpha))

GPy/inference/latent_function_inference/posterior.py

@@ -1,33 +1,58 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
 import numpy as np
 
 class Posterior(object):
     """
-    An object to represent a Gaussian posterior over latent function values. 
+    An object to represent a Gaussian posterior over latent function values.
-    """
+    this may be computed exactly for Gaussian likelihoods, or approximated for
-    def __init__(self, log_marginal, dL_dmean=None, cov=None, prec=None):
+    non-Gaussian likelihoods. 
-        self._log_marginal = log_marginal
 
-        #TODO: accept the init arguments, make sure we've got enough information to compute everything.
+    The purpose of this clas is to serve as an interface between the inference
+    schemes and the model classes. 
+
+    """
+    def __init__(self, log_marginal, dLM_DK, dLM_dtheta_lik, woodbury_chol, woodbury_mean, K):
+        """
+        log_marginal: log p(Y|X)
+        DLM_dK: d/dK log p(Y|X)
+        dLM_dtheta_lik : d/dtheta log p(Y|X) (where theta are the parameters of the likelihood)
+        woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K
+        woodbury_mean : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
+        K : the proir covariance (required for lazy computation of various quantities)
+        """
+        self.log_marginal = log_marginal
+        self.dLM_DK = dLM_DK
+        self.dLM_dtheta_lik = _dLM_dtheta_lik
+        self._woodbury_chol = woodbury_chol
+        self._woodbury_mean = woodbury_mean
+        self._K = K
+
+        #these are computed lazily below
+        self._mean = None
+        self._covariance = None
+        self._precision = None
 
     @property
     def mean(self):
         if self._mean is None:
-            self._mean = ??
+            self._mean = np.dot(self._K, self._woodbury_mean)
         return self._mean
 
     @property
     def covariance(self):
         if self._covariance is None:
-            self._covariance = ??
+            LiK, _ = dpotrs
+            self._covariance = self._K - tdot(LiK.T)
         return self._covariance
 
     @property
     def precision(self):
         if self._precision is None:
-            self._precision = ??
+            self._precision = np.linalg.inv(self.covariance)
         return self._precision
 
-    @prop