Example is working

GPy/examples/poisson.py

@@ -3,46 +3,45 @@
 
 
 """
-Simple Gaussian Processes classification
+Gaussian Processes + Expectation Propagation - Poisson Likelihood
 """
 import pylab as pb
 import numpy as np
 import GPy
-pb.ion()
 
-pb.close('all')
 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
-"""
+def  toy_1d(seed=default_seed):
+    """
+    Simple 1D classification example
+    :param seed : seed value for data generation (default is 4).
+    :type seed: int
+    """
 
-X = np.arange(0,100,5)[:,None]
-F = np.round(np.sin(X/18.) + .1*X) + np.arange(5,25)[:,None]
-E = np.random.randint(-5,5,20)[:,None]
-Y = F + E
-pb.figure()
-likelihood = GPy.inference.likelihoods.poisson(Y,scale=1.)
+    X = np.arange(0,100,5)[:,None]
+    F = np.round(np.sin(X/18.) + .1*X) + np.arange(5,25)[:,None]
+    E = np.random.randint(-5,5,20)[:,None]
+    Y = F + E
 
-m = GPy.models.GP(X,likelihood=likelihood)
-#m = GPy.models.GP(X,Y=likelihood.Y)
+    kernel = GPy.kern.rbf(1)
+    distribution = GPy.likelihoods.likelihood_functions.Poisson()
+    likelihood = GPy.likelihoods.EP(Y,distribution)
 
-m.constrain_positive('var')
-m.constrain_positive('len')
-m.tie_param('lengthscale')
-if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
-    m.approximate_likelihood()
-print m.checkgrad()
-# Optimize and plot
-m.optimize()
-#m.em(plot_all=False) # EM algorithm
-m.plot(samples=4)
+    m = GPy.models.GP(X,likelihood,kernel)
+    m.ensure_default_constraints()
 
-print(m)
+    # Approximate likelihood
+    m.update_likelihood_approximation()
+
+    # Optimize and plot
+    m.optimize()
+    #m.EPEM FIXME
+    print m
+
+    # Plot
+    pb.subplot(211)
+    m.plot_f() #GP plot
+    pb.subplot(212)
+    m.plot() #Output plot
+
+    return m