From 2e9f63b7b4e4e069790f320a8dd915e6d94d6eb5 Mon Sep 17 00:00:00 2001
From: Zhenwen Dai <z.dai@sheffield.ac.uk>
Date: Mon, 13 Oct 2014 16:20:44 +0100
Subject: [PATCH] update hmc for tie framework

---
 GPy/core/parameterization/parameter_core.py | 15 ++++-----------
 GPy/inference/optimization/hmc.py           |  8 ++++----
 2 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 0b463f89..f890a0aa 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -954,21 +954,14 @@ class Parameterizable(OptimizationHandlable):
         return self._param_array_
 
     @property
-    def unfixed_param_array(self):
+    def exposed_param_array(self):
         """
-        Array representing the parameters of this class.
-        There is only one copy of all parameters in memory, two during optimization.
-
-        !WARNING!: setting the parameter array MUST always be done in memory:
-        m.param_array[:] = m_copy.param_array
         """
         if self.__dict__.get('_param_array_', None) is None:
             self._param_array_ = np.empty(self.size, dtype=np.float64)
-                    
-        if self.constraints[__fixed__].size !=0:
-            fixes = np.ones(self.size).astype(bool)
-            fixes[self.constraints[__fixed__]] = FIXED
-            return self._param_array_[fixes]
+        
+        if self._has_hidden():
+            return self._param_array_[self._exposed_]
         else:
             return self._param_array_
 
diff --git a/GPy/inference/optimization/hmc.py b/GPy/inference/optimization/hmc.py
index 8c65cdf0..b273624b 100644
--- a/GPy/inference/optimization/hmc.py
+++ b/GPy/inference/optimization/hmc.py
@@ -20,7 +20,7 @@ class HMC:
             self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
             H_old = self._computeH()
             theta_old = self.model.optimizer_array.copy()
-            params[i] = self.model.unfixed_param_array
+            params[i] = self.model.exposed_param_array
             #Matropolis
             self._update(hmc_iters)
             H_new = self._computeH()
@@ -30,7 +30,7 @@ class HMC:
             else:
                 k = np.exp(H_old-H_new)
             if np.random.rand()<k:
-                params[i] = self.model.unfixed_param_array
+                params[i] = self.model.exposed_param_array
             else:
                 self.model.optimizer_array = theta_old
         return params
@@ -64,7 +64,7 @@ class HMC_shortcut:
             stepsize = np.exp(np.random.rand()*(self.stepsize_range[1]-self.stepsize_range[0])+self.stepsize_range[0])
             self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
             H_old = self._computeH()
-            params[i] = self.model.unfixed_param_array
+            params[i] = self.model.exposed_param_array
             theta_old = self.model.optimizer_array.copy()
             #Matropolis
             self._update(hmc_iters, stepsize)
@@ -75,7 +75,7 @@ class HMC_shortcut:
             else:
                 k = np.exp(H_old-H_new)
             if np.random.rand()<k:
-                params[i] = self.model.unfixed_param_array
+                params[i] = self.model.exposed_param_array
             else:
                 self.model.optimizer_array = theta_old
         return params