apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-24 14:15:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

removed logexp_clipped for now

Browse source
This commit is contained in:
Max Zwiessele 2013-05-22 12:36:29 +01:00
parent 3e9c266d0d
commit 26dd629181
1 changed files with 8 additions and 8 deletions
Download patch file Download diff file Expand all files Collapse all files

16
GPy/examples/dimensionality_reduction.py
View file

@ -131,7 +131,7 @@ def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k)
m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
m.constrain('variance|leng', logexp_clipped())
# m.constrain('variance|leng', logexp_clipped())
m['lengt'] = m.X.var(0).max() / m.X.var(0)
m['noise'] = Yn.var() / 100.
@ -246,7 +246,7 @@ def bgplvm_simulation_matlab_compare():
def bgplvm_simulation(optimize='scg',
plot=True,
max_f_eval=2e4):
from GPy.core.transformations import logexp_clipped
# from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, M, Q = 15, 8, 8, 100, 3, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot)
@ -259,8 +259,8 @@ def bgplvm_simulation(optimize='scg',
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
m.constrain('variance|noise', logexp_clipped())
# m.ensure_default_constraints()
# m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints()
m['noise'] = Y.var() / 100.
m['linear_variance'] = .01
@ -273,8 +273,8 @@ def bgplvm_simulation(optimize='scg',
pylab.figure(); pylab.axis(); m.kern.plot_ARD()
return m
def mrd_simulation(optimize=True, plot_sim=False):
D1, D2, D3, N, M, Q = 150, 250, 30, 300, 3, 7
def mrd_simulation(optimize=True, plot_sim=False, **kw):
D1, D2, D3, N, M, Q = 150, 250, 30, 200, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd
@ -284,12 +284,12 @@ def mrd_simulation(optimize=True, plot_sim=False):
reload(mrd); reload(kern)
k = kern.linear(Q, [0.01] * Q, True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute')
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', **kw)
for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100.
m.constrain('variance|noise', logexp_clipped())
# m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints()
# DEBUG