Poisson likelihood implementations needs to be thought carefully

GPy/examples/non_Gaussian.py

@@ -1,47 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-"""
-Gaussian Processes + Expectation Propagation - Poisson Likelihood
-"""
-import pylab as pb
-import numpy as np
-import GPy
-
-default_seed=10000
-
-def  toy_poisson_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
-
-    kernel = GPy.kern.rbf(1)
-    distribution = GPy.likelihoods.likelihood_functions.Poisson()
-    likelihood = GPy.likelihoods.EP(Y,distribution)
-
-    m = GPy.models.GP(X,likelihood,kernel)
-    m.ensure_default_constraints()
-
-    # 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