[link|unlink_parameter] renaming add_parameter to link_parameter

GPy/likelihoods/gamma.py

@@ -25,7 +25,7 @@ class Gamma(Likelihood):
         super(Gamma, self).__init__(gp_link, 'Gamma')
 
         self.beta = Param('beta', beta)
-        self.add_parameter(self.beta)
+        self.link_parameter(self.beta)
         self.beta.fix()#TODO: gradients!
 
     def pdf_link(self, link_f, y, Y_metadata=None):

GPy/likelihoods/gaussian.py

@@ -40,7 +40,7 @@ class Gaussian(Likelihood):
         super(Gaussian, self).__init__(gp_link, name=name)
 
         self.variance = Param('variance', variance, Logexp())
-        self.add_parameter(self.variance)
+        self.link_parameter(self.variance)
 
         if isinstance(gp_link, link_functions.Identity):
             self.log_concave = True

GPy/likelihoods/mixed_noise.py

@@ -14,7 +14,7 @@ class MixedNoise(Likelihood):
         #NOTE at the moment this likelihood only works for using a list of gaussians
         super(Likelihood, self).__init__(name=name)
 
-        self.add_parameters(*likelihoods_list)
+        self.link_parameters(*likelihoods_list)
         self.likelihoods_list = likelihoods_list
         self.log_concave = False
 

GPy/likelihoods/student_t.py

@@ -29,8 +29,8 @@ class StudentT(Likelihood):
         # sigma2 is not a noise parameter, it is a squared scale.
         self.sigma2 = Param('t_scale2', float(sigma2), Logexp())
         self.v = Param('deg_free', float(deg_free))
-        self.add_parameter(self.sigma2)
-        self.add_parameter(self.v)
+        self.link_parameter(self.sigma2)
+        self.link_parameter(self.v)
         self.v.constrain_fixed()
 
         self.log_concave = False