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bgplvm integrated

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Max Zwiessele 2013-11-07 08:52:33 +00:00
parent 8c02e4af36
commit 765ab41045
7 changed files with 22 additions and 23 deletions
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9
GPy/examples/dimensionality_reduction.py
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@ -4,10 +4,9 @@
import numpy as np
from matplotlib import pyplot as plt, cm
from ..models.bayesian_gplvm import BayesianGPLVM
from ..likelihoods.gaussian import Gaussian
import GPy
from GPy.core.transformations import Logexp
from GPy.models.bayesian_gplvm import BayesianGPLVM
from GPy.likelihoods.gaussian import Gaussian
default_seed = np.random.seed(123344)
@ -26,10 +25,10 @@ def BGPLVM(seed=default_seed):
lik = Gaussian(Y, normalize=True)
# k = GPy.kern.rbf_inv(input_dim, .5, np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
k = GPy.kern.rbf(input_dim, ARD=1)
k = GPy.kern.rbf(input_dim, ARD=1, name="rbf1") + GPy.kern.rbf(input_dim, ARD=1, name='rbf2') + GPy.kern.linear(input_dim, ARD=1, name='linear_part')
# k = GPy.kern.rbf(input_dim, ARD = False)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m = BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
# m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1)