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some minor example modifications and cgd adjustments

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Max Zwiessele 2013-05-03 14:22:18 +01:00
parent 9229100af7
commit 5321bfc8c9
4 changed files with 33 additions and 31 deletions
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14
GPy/examples/dimensionality_reduction.py
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@ -176,13 +176,12 @@ def bgplvm_simulation_matlab_compare():
Y = sim_data['Y']
S = sim_data['S']
mu = sim_data['mu']
M, [_, Q] = 30, mu.shape
Q = 2
M, [_, Q] = 20, mu.shape
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
#k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
# k = kern.rbf(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
# X=mu,
@ -191,14 +190,15 @@ def bgplvm_simulation_matlab_compare():
m.ensure_default_constraints()
m.auto_scale_factor = True
m['noise'] = Y.var() / 100.
m['linear_variance'] = .01
lscstr = '{}'.format(k.parts[0].name)
# lscstr = '{}'.format(k.parts[0].name)
# m[lscstr] = .01
m.unconstrain(lscstr); m.constrain_fixed(lscstr, 10)
# m.unconstrain(lscstr); m.constrain_fixed(lscstr, 10)
lscstr = 'X_variance'
# lscstr = 'X_variance'
# m[lscstr] = .01
m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
# m.unconstrain(lscstr); m.constrain_fixed(lscstr, .1)
# cstr = 'white'
# m.unconstrain(cstr); m.constrain_bounded(cstr, .01, 1.)