From 7c8de7fff83e00d5e30e8f1a58569b1637f820ca Mon Sep 17 00:00:00 2001
From: mzwiessele <ibinbei@gmail.com>
Date: Mon, 7 Jul 2014 16:00:58 -0700
Subject: [PATCH] [whitespaces]

---
 GPy/core/parameterization/parameter_core.py        | 2 --
 GPy/core/parameterization/priors.py                | 8 ++++----
 GPy/examples/dimensionality_reduction.py           | 2 +-
 GPy/inference/latent_function_inference/var_dtc.py | 1 -
 4 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/GPy/core/parameterization/parameter_core.py b/GPy/core/parameterization/parameter_core.py
index 8c23e475..e359409e 100644
--- a/GPy/core/parameterization/parameter_core.py
+++ b/GPy/core/parameterization/parameter_core.py
@@ -751,8 +751,6 @@ class OptimizationHandlable(Indexable):
         Transform the gradients by multiplying the gradient factor for each
         constraint to it.
         """
-        if self.has_parent():
-            return g
         [np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
         if self._has_fixes(): return g[self._fixes_]
         return g
diff --git a/GPy/core/parameterization/priors.py b/GPy/core/parameterization/priors.py
index 29adc923..ddc4d02f 100644
--- a/GPy/core/parameterization/priors.py
+++ b/GPy/core/parameterization/priors.py
@@ -76,11 +76,11 @@ class Uniform(Prior):
         o = super(Prior, cls).__new__(cls, lower, upper)
         cls._instances.append(weakref.ref(o))
         return cls._instances[-1]()
-    
+
     def __init__(self, lower, upper):
         self.lower = float(lower)
         self.upper = float(upper)
-    
+
     def __str__(self):
         return "[" + str(np.round(self.lower)) + ', ' + str(np.round(self.upper)) + ']'
 
@@ -93,7 +93,7 @@ class Uniform(Prior):
 
     def rvs(self, n):
         return np.random.uniform(self.lower, self.upper, size=n)
-    
+
 class LogGaussian(Prior):
     """
     Implementation of the univariate *log*-Gaussian probability function, coupled with random variables.
@@ -246,7 +246,7 @@ class Gamma(Prior):
         """
         Creates an instance of a Gamma Prior  by specifying the Expected value(s)
         and Variance(s) of the distribution.
-    
+
         :param E: expected value
         :param V: variance
         """
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 7893a13e..1932691c 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -296,7 +296,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
     from GPy.models import BayesianGPLVM
     from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
 
-    D1, D2, D3, N, num_inducing, Q = 1000, 5, 8, 400, 3, 4
+    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
     _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
     Y = Ylist[0]
     k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py
index 3370fdf1..dfe1c344 100644
--- a/GPy/inference/latent_function_inference/var_dtc.py
+++ b/GPy/inference/latent_function_inference/var_dtc.py
@@ -302,7 +302,6 @@ class VarDTCMissingData(LatentFunctionInference):
             #if ((i+1.)/size) >= next_ten:
             #    logger.info('preparing traces {:> 6.1%}'.format((i+1.)/size))
             #    next_ten += .1
-
             #y = y[v]
             if het_noise: beta = beta_all[i]
             else: beta = beta_all