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SCG printing prettyfied

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Max Zwiessele 2013-05-17 17:17:30 +01:00
parent 3bb42481be
commit 6b63237adf
2 changed files with 34 additions and 29 deletions
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38
GPy/examples/classification.py
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@ -9,8 +9,8 @@ import pylab as pb
import numpy as np
import GPy
default_seed=10000
def crescent_data(seed=default_seed): #FIXME
default_seed = 10000
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'].
@ -27,10 +27,10 @@ def crescent_data(seed=default_seed): #FIXME
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'],distribution)
likelihood = GPy.likelihoods.EP(data['Y'], distribution)
m = GPy.models.GP(data['X'],likelihood,kernel)
m = GPy.models.GP(data['X'], likelihood, kernel)
m.ensure_default_constraints()
m.update_likelihood_approximation()
@ -54,10 +54,10 @@ def oil():
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1], distribution)
# Create GP model
m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
m = GPy.models.GP(data['X'], likelihood=likelihood, kernel=kernel)
# Contrain all parameters to be positive
m.constrain_positive('')
@ -85,17 +85,17 @@ def toy_linear_1d_classification(seed=default_seed):
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y,distribution)
likelihood = GPy.likelihoods.EP(Y, distribution)
# Model definition
m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
m = GPy.models.GP(data['X'], likelihood=likelihood, kernel=kernel)
m.ensure_default_constraints()
# Optimize
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.pseudo_EM() #FIXME
# m.pseudo_EM() #FIXME
# Plot
pb.subplot(211)
@ -121,20 +121,20 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(Y,distribution)
likelihood = GPy.likelihoods.EP(Y, distribution)
Z = np.random.uniform(data['X'].min(),data['X'].max(),(10,1))
Z = np.random.uniform(data['X'].min(), data['X'].max(), (10, 1))
# Model definition
m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z,normalize_X=False)
m.set('len',2.)
m = GPy.models.sparse_GP(data['X'], likelihood=likelihood, kernel=kernel, Z=Z, normalize_X=False)
m.set('len', 2.)
m.ensure_default_constraints()
# Optimize
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.EPEM() #FIXME
# m.EPEM() #FIXME
# Plot
pb.subplot(211)
@ -162,15 +162,15 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
# Likelihood object
distribution = GPy.likelihoods.likelihood_functions.probit()
likelihood = GPy.likelihoods.EP(data['Y'],distribution)
likelihood = GPy.likelihoods.EP(data['Y'], distribution)
sample = np.random.randint(0,data['X'].shape[0],inducing)
Z = data['X'][sample,:]
sample = np.random.randint(0, data['X'].shape[0], inducing)
Z = data['X'][sample, :]
# create sparse GP EP model
m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z)
m = GPy.models.sparse_GP(data['X'], likelihood=likelihood, kernel=kernel, Z=Z)
m.ensure_default_constraints()
m.set('len',10.)
m.set('len', 10.)
m.update_likelihood_approximation()