apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-12 05:22:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Poisson likelihood implementations needs to be thought carefully

Browse source
This commit is contained in:
Ricardo 2013-06-05 18:23:12 +01:00
parent 903c8bb123
commit dde30c01ff
1 changed files with 0 additions and 47 deletions
Download patch file Download diff file Expand all files Collapse all files

47
GPy/examples/non_Gaussian.py
View file

@ -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