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Examples changed to use new link_functions

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Ricardo 2013-06-04 16:32:12 +01:00
parent 2dfda94176
commit 78aedd84af
1 changed files with 15 additions and 9 deletions
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24
GPy/examples/classification.py
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@ -21,13 +21,15 @@ def crescent_data(seed=default_seed): # FIXME
"""
data = GPy.util.datasets.crescent_data(seed=seed)
Y = data['Y']
Y[Y.flatten()==-1] = 0
# 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)
distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
m = GPy.models.GP(data['X'], likelihood, kernel)
@ -49,12 +51,15 @@ def oil():
Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
"""
data = GPy.util.datasets.oil()
Y = data['Y'][:, 0:1]
Y[Y.flatten()==-1] = 0
# Kernel object
kernel = GPy.kern.rbf(12)
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1], distribution)
distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
# Create GP model
m = GPy.models.GP(data['X'], likelihood=likelihood, kernel=kernel)
@ -87,8 +92,6 @@ def toy_linear_1d_classification(seed=default_seed):
# Likelihood object
link = GPy.likelihoods.link_functions.probit
distribution = GPy.likelihoods.likelihood_functions.binomial(link)
#distribution = GPy.likelihoods.likelihood_functions.binomial()
#distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y, distribution)
# Model definition
@ -119,12 +122,13 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0
# Kernel object
kernel = GPy.kern.rbf(1) + GPy.kern.white(1)
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
Z = np.random.uniform(data['X'].min(), data['X'].max(), (10, 1))
@ -160,13 +164,15 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
"""
data = GPy.util.datasets.crescent_data(seed=seed)
Y = data['Y']
Y[Y.flatten()==-1]=0
# Kernel object
kernel = GPy.kern.rbf(data['X'].shape[1]) + GPy.kern.white(data['X'].shape[1])
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'], distribution)
distribution = GPy.likelihoods.likelihood_functions.binomial()
likelihood = GPy.likelihoods.EP(Y, distribution)
sample = np.random.randint(0, data['X'].shape[0], inducing)
Z = data['X'][sample, :]