Fixing GP_EP

GPy/examples/classification.py

@@ -76,11 +76,10 @@ def toy_linear_1d_classification(model_type='Full', inducing=4, seed=default_see
 
     # create simple GP model
     if model_type=='Full':
-        m = GPy.models.simple_GP_EP(data['X'],likelihood)
+        m = GPy.models.GP_EP(data['X'],likelihood)
     else:
         # create sparse GP EP model
         m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
-            
 
     m.constrain_positive('var')
     m.constrain_positive('len')

GPy/examples/ep_fix.py

@@ -0,0 +1,39 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+"""
+Simple Gaussian Processes classification
+"""
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+
+default_seed=10000
+
+model_type='Full'
+inducing=4
+seed=default_seed
+"""Simple 1D classification example.
+:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+:param seed : seed value for data generation (default is 4).
+:type seed: int
+:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
+:type inducing: int
+"""
+data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
+likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
+
+m = GPy.models.GP_EP2(data['X'],likelihood)
+
+#m.constrain_positive('var')
+#m.constrain_positive('len')
+#m.tie_param('lengthscale')
+m.approximate_likelihood()
+# Optimize and plot
+#m.optimize()
+#m.em(plot_all=False) # EM algorithm
+m.plot()
+
+print(m)