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Ricardo Andrade 2013-03-11 11:41:46 +00:00
parent 1ddc059251
commit addb5da4e4
2 changed files with 82 additions and 53 deletions
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22
GPy/examples/classification.py
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@ -11,7 +11,7 @@ import GPy
default_seed=10000
def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
def crescent_data(seed=default_seed): #FIXME
"""Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
@ -31,11 +31,8 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
likelihood = GPy.likelihoods.EP(data['Y'],distribution)
if model_type=='Full':
m = GPy.models.GP(data['X'],likelihood,kernel)
else:
# create sparse GP EP model
m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
m = GPy.models.GP(data['X'],likelihood,kernel)
m.ensure_default_constraints()
m.update_likelihood_approximation()
print(m)
@ -94,16 +91,13 @@ def toy_linear_1d_classification(seed=default_seed):
# Model definition
m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
m.ensure_default_constraints()
# Optimize
"""
EPEM runs a loop that consists of two steps:
1) EP likelihood approximation:
m.update_likelihood_approximation()
2) Parameters optimization:
m.optimize()
"""
m.EPEM()
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.EPEM() #FIXME
# Plot
pb.subplot(211)