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kern stash conflict

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Max Zwiessele 2013-04-23 16:34:31 +01:00
parent ebc3b8756e
commit 264f0d21b6
4 changed files with 370 additions and 61 deletions
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84
GPy/examples/dimensionality_reduction.py
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@ -112,14 +112,14 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
s3 = s3(x)
sS = sS(x)
s1 -= s1.mean()
s2 -= s2.mean()
s3 -= s3.mean()
sS -= sS.mean()
s1 /= .5 * (np.abs(s1).max() - np.abs(s1).min())
s2 /= .5 * (np.abs(s2).max() - np.abs(s2).min())
s3 /= .5 * (np.abs(s3).max() - np.abs(s3).min())
sS /= .5 * (np.abs(sS).max() - np.abs(sS).min())
# s1 -= s1.mean()
# s2 -= s2.mean()
# s3 -= s3.mean()
# sS -= sS.mean()
# s1 /= .5 * (np.abs(s1).max() - np.abs(s1).min())
# s2 /= .5 * (np.abs(s2).max() - np.abs(s2).min())
# s3 /= .5 * (np.abs(s3).max() - np.abs(s3).min())
# sS /= .5 * (np.abs(sS).max() - np.abs(sS).min())
S1 = np.hstack([s1, sS])
S2 = np.hstack([s2, sS])
@ -129,9 +129,9 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
Y1 += .5 * np.random.randn(*Y1.shape)
Y2 += .5 * np.random.randn(*Y2.shape)
Y3 += .5 * np.random.randn(*Y3.shape)
Y1 += .3 * np.random.randn(*Y1.shape)
Y2 += .3 * np.random.randn(*Y2.shape)
Y3 += .3 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0)
@ -162,8 +162,11 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
return slist, [S1, S2, S3], Ylist
def bgplvm_simulation(burnin='scg', plot_sim=False, max_f_eval=12):
D1, D2, D3, N, M, Q = 2000, 8, 8, 500, 2, 6
def bgplvm_simulation(burnin='scg', plot_sim=False,
max_burnin=100, true_X=False,
do_opt=True,
max_f_eval=1000):
D1, D2, D3, N, M, Q = 10, 8, 8, 50, 30, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd
@ -171,53 +174,73 @@ def bgplvm_simulation(burnin='scg', plot_sim=False, max_f_eval=12):
reload(mrd); reload(kern)
Y = Ylist[1]
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.white(Q, .00001) # + kern.bias(Q)
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k)
# k = kern.white(Q, .00001) + kern.bias(Q)
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
# m.set('noise',)
m.ensure_default_constraints()
# m.auto_scale_factor = True
# m.scale_factor = 1.
m.ensure_default_constraints()
if burnin:
print "initializing beta"
cstr = "noise"
m.unconstrain(cstr); m.constrain_fixed(cstr, Y.var() / 100.)
m.optimize(burnin, messages=1, max_f_eval=max_f_eval)
m.unconstrain(cstr); m.constrain_fixed(cstr, Y.var() / 70.)
m.optimize(burnin, messages=1, max_f_eval=max_burnin)
print "releasing beta"
cstr = "noise"
m.unconstrain(cstr); m.constrain_positive(cstr)
true_X = np.hstack((slist[1], slist[3], 0. * np.ones((N, Q - 2))))
m.set('X_\d', true_X)
m.constrain_fixed("X_\d")
if true_X:
true_X = np.hstack((slist[0], slist[3], 0. * np.ones((N, Q - 2))))
m.set('X_\d', true_X)
m.constrain_fixed("X_\d")
# # cstr = 'variance'
# # m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 1.)
cstr = 'X_variance'
# m.unconstrain(cstr), m.constrain_fixed(cstr, .0001)
m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-7, .1)
# cstr = 'X_variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-3, 1.)
m.set('X_var', np.ones(N * Q) * .5 + np.random.randn(N * Q) * .01)
# cstr = "iip"
# m.unconstrain(cstr); m.constrain_fixed(cstr)
# cstr = 'variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 1.)
# cstr = 'X_\d'
# m.unconstrain(cstr), m.constrain_bounded(cstr, -100., 100.)
# m.unconstrain(cstr), m.constrain_bounded(cstr, -10., 10.)
#
# cstr = 'noise'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-3, 1.)
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-5, 1.)
#
# cstr = 'white'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-6, 1.)
#
# cstr = 'linear_variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 10.) # m.constrain_positive(cstr)
#
# cstr = 'X_variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 1.) # m.constrain_positive(cstr)
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 10.)
# cstr = 'variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-10, 10.)
# np.seterr(all='call')
# def ipdbonerr(errtype, flags):
# import ipdb; ipdb.set_trace()
# np.seterrcall(ipdbonerr)
if do_opt and burnin:
try:
m.optimize(burnin, messages=1, max_f_eval=max_f_eval)
except:
pass
finally:
return m
return m
def mrd_simulation(plot_sim=False):
@ -261,6 +284,7 @@ def mrd_simulation(plot_sim=False):
m.set('{}_noise'.format(i + 1), Y.var() / 100.)
m.ensure_default_constraints()
m.auto_scale_factor = True
# cstr = 'variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)