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various work on BGPLVM oil demo

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James Hensman 2013-04-10 09:28:58 +01:00
parent bb734a6dd7
commit fd0b172e81
4 changed files with 66 additions and 29 deletions
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28
GPy/examples/dimensionality_reduction.py
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@ -47,7 +47,7 @@ def GPLVM_oil_100(optimize=True,M=15):
# create simple GP model
kernel = GPy.kern.rbf(6, ARD = True) + GPy.kern.bias(6)
m = GPy.models.Bayesian_GPLVM(data['X'], 6, kernel=kernel, M=M)
m = GPy.models.GPLVM(data['X'], 6, kernel=kernel, M=M)
m.data_labels = data['Y'].argmax(axis=1)
# optimize
@ -60,22 +60,32 @@ def GPLVM_oil_100(optimize=True,M=15):
m.plot_latent(labels=m.data_labels)
return m
def BGPLVM_oil_100(optimize=True):
data = GPy.util.datasets.oil_100()
def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
data = GPy.util.datasets.oil()
# create simple GP model
kernel = GPy.kern.rbf(6, ARD = True) + GPy.kern.bias(6)
m = GPy.models.GPLVM(data['X'], 6, kernel = kernel)
m.data_labels = data['Y'].argmax(axis=1)
kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q,0.001)
m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel = kernel,M=M)
m.data_labels = data['Y'][:N].argmax(axis=1)
#initial conditions
# optimize
m.ensure_default_constraints()
if optimize:
m.optimize(messages=1)
m.constrain_fixed('noise',0.05)
m.ensure_default_constraints()
m.optimize('scg',messages=1)
m.unconstrain('noise')
m.constrain_positive('noise')
m.optimize('scg',messages=1)
else:
m.ensure_default_constraints()
# plot
print(m)
m.plot_latent(labels=data['Y'].argmax(axis=1))
m.plot_latent(labels=m.data_labels)
pb.figure()
pb.bar(np.arange(m.kern.D),1./m.input_sensitivity())
return m
def oil_100():