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minor adjustments

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Max Zwiessele 2013-06-28 11:13:20 +01:00
parent 96829862cc
commit a8eb7eb5f7
2 changed files with 4 additions and 4 deletions
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7
GPy/examples/dimensionality_reduction.py
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@ -254,7 +254,7 @@ def bgplvm_simulation(optimize='scg',
Y = Ylist[0] Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q) k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, _debug=True) m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
# m.constrain('variance|noise', logexp_clipped()) # m.constrain('variance|noise', logexp_clipped())
m['noise'] = Y.var() / 100. m['noise'] = Y.var() / 100.
@ -279,11 +279,12 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
reload(mrd); reload(kern) reload(mrd); reload(kern)
k = kern.linear(Q, [.05] * 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 = mrd.MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw) m = mrd.MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
m.ensure_default_constraints()
for i, Y in enumerate(Ylist): for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100. m['{}_noise'.format(i)] = Y.var() / 100.
# DEBUG # DEBUG

1
GPy/testing/cgd_tests.py
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@ -7,7 +7,6 @@ import unittest
import numpy import numpy
from GPy.inference.conjugate_gradient_descent import CGD, RUNNING from GPy.inference.conjugate_gradient_descent import CGD, RUNNING
import pylab import pylab
import time
from scipy.optimize.optimize import rosen, rosen_der from scipy.optimize.optimize import rosen, rosen_der
from GPy.inference.gradient_descent_update_rules import PolakRibiere from GPy.inference.gradient_descent_update_rules import PolakRibiere