some noodling around in the likelihoods

GPy/likelihoods/gaussian.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2012, 2013 Ricardo Andrade
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
@@ -30,7 +30,7 @@ class Gaussian(Likelihood):
             analytical_variance = False
             analytical_mean = False
 
-        super(Gaussian, self).__init__(gp_link, analytical_mean, analytical_variance, name=name)
+        super(Gaussian, self).__init__(gp_link, name=name)
 
         self.variance = Param('variance', variance)
         self.add_parameter(self.variance)
@@ -42,6 +42,10 @@ class Gaussian(Likelihood):
         return np.eye(Y.shape[0]) * self.variance
 
     def _gradients(self, partial):
+        """
+        Return the derivative of the log marginal likelihood wrt self.variance, 
+        given the appropriate partial derivative
+        """
         return np.sum(partial)
 
     def _preprocess_values(self, Y):
@@ -51,7 +55,7 @@ class Gaussian(Likelihood):
         """
         return Y
 
-    def _moments_match_analytical(self, data_i, tau_i, v_i):
+    def _moments_match_ep(self, data_i, tau_i, v_i):
         """
         Moments match of the marginal approximation in EP algorithm
 
@@ -65,11 +69,11 @@ class Gaussian(Likelihood):
         Z_hat = 1./np.sqrt(2.*np.pi*sum_var)*np.exp(-.5*(data_i - v_i/tau_i)**2./sum_var)
         return Z_hat, mu_hat, sigma2_hat
 
-    def _predictive_mean_analytical(self, mu, sigma):
+    def _predictive_mean(self, mu, sigma):
         new_sigma2 = self.predictive_variance(mu, sigma)
         return new_sigma2*(mu/sigma**2 + self.gp_link.transf(mu)/self.variance)
 
-    def _predictive_variance_analytical(self, mu, sigma, predictive_mean=None):
+    def _predictive_variance(self, mu, sigma, predictive_mean=None):
         return 1./(1./self.variance + 1./sigma**2)
 
     def pdf_link(self, link_f, y, extra_data=None):

GPy/likelihoods/likelihood.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2012, 2013 Ricardo Andrade
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
@@ -19,28 +19,17 @@ class Likelihood(Parameterized):
 
     To use this class, inherrit and define missing functionality. 
 
-    The minimum required funciotnality is... TODO
-    """
-    def __init__(self,gp_link,analytical_mean=False,analytical_variance=False, name='likelihood_base'):
-        """
-        What are analytical_mean, analyitical_variance? TODO
+    To enable use with EP, ...
+
+    To enable use with Laplace approximation, ...
+
+    For exact Gaussian inference, define ...
+
     """
+    def __init__(self, gp_link, name):
         super(Likelihood, self).__init__(name) 
         assert isinstance(gp_link,link_functions.GPTransformation), "gp_link is not a valid GPTransformation."
         self.gp_link = gp_link
-        self.analytical_mean = analytical_mean
-        self.analytical_variance = analytical_variance
-        if self.analytical_mean:
-            self.moments_match = self._moments_match_analytical
-            self.predictive_mean = self._predictive_mean_analytical
-        else:
-            self.moments_match = self._moments_match_numerical
-            self.predictive_mean = self._predictive_mean_numerical
-        if self.analytical_variance:
-            self.predictive_variance = self._predictive_variance_analytical
-        else:
-            self.predictive_variance = self._predictive_variance_numerical
-
         self.log_concave = False
 
     def _gradients(self,partial):
@@ -56,12 +45,6 @@ class Likelihood(Parameterized):
         """
         return Y
 
-    def _moments_match_analytical(self,obs,tau,v):
-        """
-        If available, this function computes the moments analytically.
-        """
-        raise NotImplementedError
-
     def log_predictive_density(self, y_test, mu_star, var_star):
         """
         Calculation of the log predictive density
@@ -90,7 +73,7 @@ class Likelihood(Parameterized):
         p_ystar = scaled_p_ystar/np.sqrt(2*np.pi*var_star)
         return np.log(p_ystar)
 
-    def _moments_match_numerical(self,obs,tau,v):
+    def _moments_match_ep(self,obs,tau,v):
         """
         Calculation of moments using quadrature
 
@@ -124,27 +107,7 @@ class Likelihood(Parameterized):
 
         return z, mean, variance
 
-    def _predictive_mean_analytical(self,mu,sigma):
-        """
-        Predictive mean
-        .. math::
-            E(Y^{*}|Y) = E( E(Y^{*}|f^{*}, Y) )
-
-        If available, this function computes the predictive mean analytically.
-        """
-        raise NotImplementedError
-
-    def _predictive_variance_analytical(self,mu,sigma):
-        """
-        Predictive variance
-        .. math::
-            V(Y^{*}| Y) = E( V(Y^{*}|f^{*}, Y) ) + V( E(Y^{*}|f^{*}, Y) )
-
-        If available, this function computes the predictive variance analytically.
-        """
-        raise NotImplementedError
-
-    def _predictive_mean_numerical(self,mu,variance):
+    def _predictive_mean(self,mu,variance):
         """
         Quadrature calculation of the predictive mean: E(Y_star|Y) = E( E(Y_star|f_star, Y) )
 
@@ -159,7 +122,7 @@ class Likelihood(Parameterized):
 
         return mean
 
-    def _predictive_variance_numerical(self,mu,variance,predictive_mean=None):
+    def _predictive_variance(self,mu,variance,predictive_mean=None):
         """
         Numerical approximation to the predictive variance: V(Y_star)
 
@@ -376,6 +339,7 @@ class Likelihood(Parameterized):
         assert dlogpdf_dtheta.shape[1] == len(self._get_param_names())
         assert dlogpdf_df_dtheta.shape[1] == len(self._get_param_names())
         assert d2logpdf_df2_dtheta.shape[1] == len(self._get_param_names())
+
         return dlogpdf_dtheta, dlogpdf_df_dtheta, d2logpdf_df2_dtheta
 
     def predictive_values(self, mu, var, full_cov=False, sampling=False, num_samples=10000):