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Probit likelihood modified for plotting.

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Ricardo Andrade 2013-02-07 11:36:45 +00:00
parent cf3e522069
commit 4563a5f8a6
2 changed files with 31 additions and 21 deletions
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23
GPy/examples/classification.py
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@ -20,11 +20,19 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
:type inducing: int
"""
data = GPy.util.datasets.crescent_data(seed=seed)
likelihood = GPy.inference.likelihoods.probit(data['Y'])
# Kernel object
kernel = GPy.kern.rbf(data['X'].shape[1])
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'],distribution)
if model_type=='Full':
m = GPy.models.GP_EP(data['X'],likelihood)
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)
@ -33,7 +41,7 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
print(m)
# optimize
m.em()
m.optimize()
print(m)
# plot
@ -53,7 +61,7 @@ def oil():
likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
# Create GP model
m = GPy.models.GP(data['X'],kernel,likelihood=likelihood)
m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
# Contrain all parameters to be positive
m.constrain_positive('')
@ -71,17 +79,18 @@ def toy_linear_1d_classification(seed=default_seed):
Simple 1D classification example
:param seed : seed value for data generation (default is 4).
:type seed: int
:type inducing: int
"""
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1]
Y[Y == -1] = 0
# Kernel object
kernel = GPy.kern.rbf(1)
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
likelihood = GPy.likelihoods.EP(Y,distribution)
# Model definition
m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
@ -98,7 +107,7 @@ def toy_linear_1d_classification(seed=default_seed):
# Plot
pb.subplot(211)
m.plot_internal()
m.plot_f()
pb.subplot(212)
m.plot()
print(m)