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pickling and caching

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Max Zwiessele 2014-03-31 12:45:09 +01:00
parent 60a071f18f
commit f3b74fa85f
28 changed files with 481 additions and 686 deletions
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9
GPy/examples/dimensionality_reduction.py
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@ -324,18 +324,15 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD
from GPy.likelihoods import Gaussian
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
#Ylist = [Ylist[0]]
k = [kern.Linear(Q, ARD=True) for _ in range(len(Ylist))]
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="", initz='permute', **kw)
k = kern.Linear(Q, ARD=True)
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="PCA_concat", initz='permute', **kw)
m['.*noise'] = [Y.var()/500. for Y in Ylist]
#for i, Y in enumerate(Ylist):
# m['.*Y_{}.*Gaussian.*noise'.format(i)] = Y.var(1) / 500.
m['.*noise'] = [Y.var()/40. for Y in Ylist]
if optimize:
print "Optimizing Model:"