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BGPLVM still failing, doesn't seem to be numerical : (

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Max Zwiessele 2013-04-17 15:45:20 +01:00
parent 0ed7f8dfdd
commit 865e9df255
3 changed files with 117 additions and 84 deletions
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75
GPy/examples/dimensionality_reduction.py
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@ -103,9 +103,9 @@ def oil_100():
def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
x = np.linspace(0, 4 * np.pi, N)[:, None]
s1 = np.vectorize(lambda x: np.sin(x))
s2 = np.vectorize(lambda x: x * np.cos(x))
s3 = np.vectorize(lambda x: np.sin(2 * x))
sS = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
s2 = np.vectorize(lambda x: np.cos(x))
s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
sS = np.vectorize(lambda x: np.sin(2 * x))
s1 = s1(x)
s2 = s2(x)
@ -162,27 +162,57 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
return slist, [S1, S2, S3], Ylist
def bgplvm_simulation(plot_sim=False):
D1, D2, D3, N, M, Q = 50, 34, 8, 100, 2, 6
def bgplvm_simulation(burnin='scg', plot_sim=False, max_f_eval=12):
D1, D2, D3, N, M, Q = 2000, 8, 8, 500, 2, 6
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
Y = Ylist[0]
Y = Ylist[1]
k = kern.linear(Q, ARD=True) + kern.bias(Q, .01) + kern.white(Q, .1)
k = kern.linear(Q, ARD=True) + kern.bias(Q, .0001) + kern.white(Q, .1)
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k)
m.ensure_default_constraints()
m.set('noise', Y.var() / 100.)
m.auto_scale_factor = True
# m.auto_scale_factor = True
# m.scale_factor = 1.
cstr = 'variance'
m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-20, 1.)
m.ensure_default_constraints()
if burnin:
print "initializing beta"
cstr = "noise"
m.unconstrain(cstr); m.constrain_fixed(cstr)
m.optimize(burnin, messages=1, max_f_eval=max_f_eval)
print "releasing beta"
cstr = "noise"
m.unconstrain(cstr); m.constrain_positive(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.)
#
# cstr = 'noise'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-3, 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)
# np.seterr(all='call')
# def ipdbonerr(errtype, flags):
# import ipdb; ipdb.set_trace()
# np.seterrcall(ipdbonerr)
cstr = 'linear_variance'
m.unconstrain(cstr), m.constrain_positive(cstr)
return m
@ -204,7 +234,7 @@ def mrd_simulation(plot_sim=False):
# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
# Y2 -= Y2.mean(0)
# make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
D1, D2, D3, N, M, Q = 50, 34, 8, 100, 2, 6
D1, D2, D3, N, M, Q = 2000, 34, 8, 500, 3, 6
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd
@ -216,24 +246,23 @@ def mrd_simulation(plot_sim=False):
# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(S2), D2).T
# Y3 = np.random.multivariate_normal(np.zeros(N), k.K(S3), D3).T
Ylist = [Ylist[0]]
Ylist = Ylist[0:2]
# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
k = kern.linear(Q, ARD=True) + kern.bias(Q, .01) + kern.white(Q, .1)
k = kern.linear(Q, ARD=True) + kern.bias(Q, .01) + kern.white(Q, .001)
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', _debug=False)
m.ensure_default_constraints()
ardvar = 5. / (m.X.max(axis=0) - m.X.min(axis=0))
for i, Y in enumerate(Ylist):
m.set('{}_noise'.format(i + 1), Y.var() / 100.)
m.ensure_default_constraints()
cstr = 'variance'
m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
cstr = 'linear_variance'
m.unconstrain(cstr), m.constrain_positive(cstr)
# cstr = 'variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
#
# cstr = 'linear_variance'
# m.unconstrain(cstr), m.constrain_positive(cstr)
# print "initializing beta"
# cstr = "noise"