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dim reduction examples

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mzwiessele 2014-01-29 11:09:14 +00:00
parent 0fb894a2c3
commit 8ccc3e071e
1 changed files with 2 additions and 3 deletions
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5
GPy/examples/dimensionality_reduction.py
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@ -266,7 +266,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
Y = Ylist[0]
k = kern.linear(Q, ARD=True)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m.X_variance = m.X_variance * .05
m.X_variance = m.X_variance * .1
m['noise'] = Y.var() / 100.
if optimize:
@ -289,12 +289,11 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
k = kern.linear(Q, ARD=True)# + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
m.X_variance = m.X_variance * .05
m.ensure_default_constraints()
for i, bgplvm in enumerate(m.bgplvms):
m['{}_noise'.format(i)] = bgplvm.likelihood.Y.var() / 500.
bgplvm.X_variance = bgplvm.X_variance * .1
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)