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convenient but not important changes

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Ricardo 2013-05-15 18:12:10 +01:00
parent 053bef0583
commit 95cb52d4a5
1 changed files with 2 additions and 5 deletions
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7
GPy/examples/classification.py
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@ -79,7 +79,6 @@ def toy_linear_1d_classification(seed=default_seed):
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)
@ -96,7 +95,7 @@ def toy_linear_1d_classification(seed=default_seed):
m.update_likelihood_approximation()
# Parameters optimization:
m.optimize()
#m.EPEM() #FIXME
#m.pseudo_EM() #FIXME
# Plot
pb.subplot(211)
@ -109,14 +108,13 @@ def toy_linear_1d_classification(seed=default_seed):
def sparse_toy_linear_1d_classification(seed=default_seed):
"""
Simple 1D classification example
Sparse 1D classification example
:param seed : seed value for data generation (default is 4).
:type seed: 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) + GPy.kern.white(1)
@ -168,7 +166,6 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
sample = np.random.randint(0,data['X'].shape[0],inducing)
Z = data['X'][sample,:]
#Z = (np.random.random_sample(2*inducing)*(data['X'].max()-data['X'].min())+data['X'].min()).reshape(inducing,-1)
# create sparse GP EP model
m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z)