work on likeluhoods and likelihoods tests

GPy/likelihoods/gamma.py

@@ -1,11 +1,12 @@
-# Copyright (c) 2012, 2013 Ricardo Andrade
+# Copyright (c) 2012 - 2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
 import numpy as np
 from scipy import stats,special
 import scipy as sp
-from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
+from ..util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
+from ..core.parameterization import Param
 import link_functions
 from likelihood import Likelihood
 
@@ -18,14 +19,16 @@ class Gamma(Likelihood):
         \\alpha_{i} = \\beta y_{i}
 
     """
-    def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False,beta=1.):
-        self.beta = beta
-        super(Gamma, self).__init__(gp_link,analytical_mean,analytical_variance)
+    def __init__(self,gp_link=None,beta=1.):
+        if gp_link is None:
+            gp_link = link_functions.Log()
+        super(Gamma, self).__init__(gp_link, 'Gamma')
 
-    def _preprocess_values(self,Y):
-        return Y
+        self.beta = Param('beta', beta)
+        self.add_parameter(self.beta)
+        self.beta.fix()#TODO: gradients!
 
-    def pdf_link(self, link_f, y, extra_data=None):
+    def pdf_link(self, link_f, y, Y_metadata=None):
         """
         Likelihood function given link(f)
 
@@ -37,7 +40,7 @@ class Gamma(Likelihood):
         :type link_f: Nx1 array
         :param y: data
         :type y: Nx1 array
-        :param extra_data: extra_data which is not used in poisson distribution
+        :param Y_metadata: Y_metadata which is not used in poisson distribution
         :returns: likelihood evaluated for this point
         :rtype: float
         """
@@ -47,7 +50,7 @@ class Gamma(Likelihood):
         objective = (y**(alpha - 1.) * np.exp(-self.beta*y) * self.beta**alpha)/ special.gamma(alpha)
         return np.exp(np.sum(np.log(objective)))
 
-    def logpdf_link(self, link_f, y, extra_data=None):
+    def logpdf_link(self, link_f, y, Y_metadata=None):
         """
         Log Likelihood Function given link(f)
 
@@ -59,7 +62,7 @@ class Gamma(Likelihood):
         :type link_f: Nx1 array
         :param y: data
         :type y: Nx1 array
-        :param extra_data: extra_data which is not used in poisson distribution
+        :param Y_metadata: Y_metadata which is not used in poisson distribution
         :returns: likelihood evaluated for this point
         :rtype: float
 
@@ -71,7 +74,7 @@ class Gamma(Likelihood):
         log_objective = alpha*np.log(self.beta) - np.log(special.gamma(alpha)) + (alpha - 1)*np.log(y) - self.beta*y
         return np.sum(log_objective)
 
-    def dlogpdf_dlink(self, link_f, y, extra_data=None):
+    def dlogpdf_dlink(self, link_f, y, Y_metadata=None):
         """
         Gradient of the log likelihood function at y, given link(f) w.r.t link(f)
 
@@ -83,7 +86,7 @@ class Gamma(Likelihood):
         :type link_f: Nx1 array
         :param y: data
         :type y: Nx1 array
-        :param extra_data: extra_data which is not used in gamma distribution
+        :param Y_metadata: Y_metadata which is not used in gamma distribution
         :returns: gradient of likelihood evaluated at points
         :rtype: Nx1 array
 
@@ -94,7 +97,7 @@ class Gamma(Likelihood):
         #return -self.gp_link.dtransf_df(gp)*self.beta*np.log(obs) + special.psi(self.gp_link.transf(gp)*self.beta) * self.gp_link.dtransf_df(gp)*self.beta
         return grad
 
-    def d2logpdf_dlink2(self, link_f, y, extra_data=None):
+    def d2logpdf_dlink2(self, link_f, y, Y_metadata=None):
         """
         Hessian at y, given link(f), w.r.t link(f)
         i.e. second derivative logpdf at y given link(f_i) and link(f_j)  w.r.t link(f_i) and link(f_j)
@@ -108,7 +111,7 @@ class Gamma(Likelihood):
         :type link_f: Nx1 array
         :param y: data
         :type y: Nx1 array
-        :param extra_data: extra_data which is not used in gamma distribution
+        :param Y_metadata: Y_metadata which is not used in gamma distribution
         :returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
         :rtype: Nx1 array
 
@@ -122,7 +125,7 @@ class Gamma(Likelihood):
         #return -self.gp_link.d2transf_df2(gp)*self.beta*np.log(obs) + special.polygamma(1,self.gp_link.transf(gp)*self.beta)*(self.gp_link.dtransf_df(gp)*self.beta)**2 + special.psi(self.gp_link.transf(gp)*self.beta)*self.gp_link.d2transf_df2(gp)*self.beta
         return hess
 
-    def d3logpdf_dlink3(self, link_f, y, extra_data=None):
+    def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
         """
         Third order derivative log-likelihood function at y given link(f) w.r.t link(f)
 
@@ -134,22 +137,10 @@ class Gamma(Likelihood):
         :type link_f: Nx1 array
         :param y: data
         :type y: Nx1 array
-        :param extra_data: extra_data which is not used in gamma distribution
+        :param Y_metadata: Y_metadata which is not used in gamma distribution
         :returns: third derivative of likelihood evaluated at points f
         :rtype: Nx1 array
         """
         assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
         d3lik_dlink3 = -special.polygamma(2, self.beta*link_f)*(self.beta**3)
         return d3lik_dlink3
-
-    def _mean(self,gp):
-        """
-        Mass (or density) function
-        """
-        return self.gp_link.transf(gp)
-
-    def _variance(self,gp):
-        """
-        Mass (or density) function
-        """
-        return self.gp_link.transf(gp)/self.beta