From 71462a1347b3e7075d0025698c04bf5bcc276492 Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Wed, 5 Jun 2013 17:33:37 +0100
Subject: [PATCH] dim reduction adaption

---
 GPy/examples/dimensionality_reduction.py | 13 ++++++-------
 GPy/inference/sgd.py                     | 10 +++++-----
 GPy/models/mrd.py                        |  2 +-
 3 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index ec6d2ca6..8b2b7a78 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -27,7 +27,7 @@ def BGPLVM(seed=default_seed):
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
     m = GPy.models.BayesianGPLVM(Y, Q, kernel=k, num_inducing=num_inducing)
-    m.constrain_positive('(rbf|bias|noise|white|S)')
+    # m.constrain_positive('(rbf|bias|noise|white|S)')
     # m.constrain_fixed('S', 1)
 
     # pb.figure()
@@ -117,10 +117,9 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
         m.optimize('scg', messages=1)
     return m
 
-def BGPLVM_oil(optimize=True, N=100, Q=5, num_inducing=25, max_f_eval=4e3, plot=False, **k):
+def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_f_eval=4e3, plot=False, **k):
     np.random.seed(0)
     data = GPy.util.datasets.oil()
-    from GPy.core.transformations import logexp_clipped
 
     # create simple GP model
     kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
@@ -132,14 +131,14 @@ def BGPLVM_oil(optimize=True, N=100, Q=5, num_inducing=25, max_f_eval=4e3, plot=
     m.data_labels = data['Y'][:N].argmax(axis=1)
 
     # m.constrain('variance|leng', logexp_clipped())
-    m['lengt'] = m.X.var(0).max() / m.X.var(0)
+    m['.*lengt'] = 1. # m.X.var(0).max() / m.X.var(0)
     m['noise'] = Yn.var() / 100.
 
     m.ensure_default_constraints()
 
     # optimize
     if optimize:
-        m.optimize('scg', messages=1, max_f_eval=max_f_eval)
+        m.optimize('scg', messages=1, max_f_eval=max_f_eval, gtol=.05)
 
     if plot:
         y = m.likelihood.Y[0, :]
@@ -266,9 +265,9 @@ def bgplvm_simulation(optimize='scg',
 
     if optimize:
         print "Optimizing model:"
-        m.optimize('scg', max_iters=max_f_eval,
+        m.optimize(optimize, max_iters=max_f_eval,
                    max_f_eval=max_f_eval,
-                   messages=True, gtol=1e-6)
+                   messages=True, gtol=.05)
     if plot:
         m.plot_X_1d("BGPLVM Latent Space 1D")
         m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
diff --git a/GPy/inference/sgd.py b/GPy/inference/sgd.py
index 0002bb22..e443f45a 100644
--- a/GPy/inference/sgd.py
+++ b/GPy/inference/sgd.py
@@ -18,10 +18,10 @@ class opt_SGD(Optimizer):
 
     """
 
-    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, Model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, learning_rate_adaptation=None, actual_iter=None, schedule=None, **kwargs):
+    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, learning_rate_adaptation=None, actual_iter=None, schedule=None, **kwargs):
         self.opt_name = "Stochastic Gradient Descent"
 
-        self.Model = Model
+        self.Model = model
         self.iterations = iterations
         self.momentum = momentum
         self.learning_rate = learning_rate
@@ -42,11 +42,11 @@ class opt_SGD(Optimizer):
                 self.learning_rate_0 = self.learning_rate.mean()
 
         self.schedule = schedule
-        # if len([p for p in self.Model.kern.parts if p.name == 'bias']) == 1:
+        # if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
         #     self.param_traces.append(('bias',[]))
-        # if len([p for p in self.Model.kern.parts if p.name == 'linear']) == 1:
+        # if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
         #     self.param_traces.append(('linear',[]))
-        # if len([p for p in self.Model.kern.parts if p.name == 'rbf']) == 1:
+        # if len([p for p in self.model.kern.parts if p.name == 'rbf']) == 1:
         #     self.param_traces.append(('rbf_var',[]))
 
         self.param_traces = dict(self.param_traces)
diff --git a/GPy/models/mrd.py b/GPy/models/mrd.py
index b078fd27..8ebff315 100644
--- a/GPy/models/mrd.py
+++ b/GPy/models/mrd.py
@@ -78,7 +78,7 @@ class MRD(Model):
         self.NQ = self.num_data * self.input_dim
         self.MQ = self.num_inducing * self.input_dim
 
-        Model.__init__(self) # @UndefinedVariable
+        model.__init__(self) # @UndefinedVariable
         self._set_params(self._get_params())
 
     @property