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RBF (both ARD and non-ARD) kernels working nicely with psi statistics

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This commit is contained in:
Nicolo Fusi 2013-01-30 16:27:45 +00:00
parent be5b775729
commit 8a5f075ef0
3 changed files with 47 additions and 29 deletions
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10
GPy/examples/BGPLVM_demo.py
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@ -7,17 +7,17 @@ import GPy
np.random.seed(123344)
N = 10
M = 3
Q = 2
D = 3
M = 5
Q = 3
D = 4
#generate GPLVM-like data
X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1)