mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-24 14:15:14 +02:00
Merged
This commit is contained in:
Teo de Campos
commit
9fecda46a4
13 changed files with 392 additions and 268 deletions
|
|
@ -8,6 +8,7 @@ from matplotlib import pyplot as plt, pyplot
|
|||
import GPy
|
||||
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
|
||||
from GPy.util.datasets import simulation_BGPLVM
|
||||
from GPy.core.transformations import square, logexp_clipped
|
||||
|
||||
default_seed = np.random.seed(123344)
|
||||
|
||||
|
|
@ -62,17 +63,21 @@ def GPLVM_oil_100(optimize=True):
|
|||
m.plot_latent(labels=m.data_labels)
|
||||
return m
|
||||
|
||||
def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
|
||||
def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300, plot=False):
|
||||
data = GPy.util.datasets.oil()
|
||||
|
||||
# create simple GP model
|
||||
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.001)
|
||||
m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel=kernel, M=M)
|
||||
kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
|
||||
Y = data['X'][:N]
|
||||
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M)
|
||||
m.data_labels = data['Y'][:N].argmax(axis=1)
|
||||
|
||||
# optimize
|
||||
if optimize:
|
||||
m.constrain_fixed('noise', 0.05)
|
||||
m.constrain_fixed('noise', 1. / Y.var())
|
||||
m.constrain('variance', logexp_clipped())
|
||||
m['lengt'] = 1000
|
||||
|
||||
m.ensure_default_constraints()
|
||||
m.optimize('scg', messages=1, max_f_eval=max(80, max_f_eval))
|
||||
m.unconstrain('noise')
|
||||
|
|
@ -81,14 +86,15 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
|
|||
else:
|
||||
m.ensure_default_constraints()
|
||||
|
||||
y = m.likelihood.Y[0, :]
|
||||
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
plt.sca(latent_axes)
|
||||
m.plot_latent()
|
||||
data_show = GPy.util.visualize.vector_show(y)
|
||||
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
|
||||
raw_input('Press enter to finish')
|
||||
plt.close('all')
|
||||
if plot:
|
||||
y = m.likelihood.Y[0, :]
|
||||
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
plt.sca(latent_axes)
|
||||
m.plot_latent()
|
||||
data_show = GPy.util.visualize.vector_show(y)
|
||||
lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
|
||||
raw_input('Press enter to finish')
|
||||
plt.close('all')
|
||||
# # plot
|
||||
# print(m)
|
||||
# m.plot_latent(labels=m.data_labels)
|
||||
|
|
@ -207,7 +213,7 @@ 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, 250, 10, 6
|
||||
D1, D2, D3, N, M, Q = 15, 8, 8, 350, 3, 6
|
||||
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
|
||||
|
||||
from GPy.models import mrd
|
||||
|
|
@ -221,6 +227,8 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
|
|||
# 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.constrain('variance', logexp_clipped())
|
||||
|
||||
m.ensure_default_constraints()
|
||||
m['noise'] = Y.var() / 100.
|
||||
m['linear_variance'] = .001
|
||||
|
|
@ -304,7 +312,7 @@ 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 = 2000, 34, 8, 500, 3, 6
|
||||
D1, D2, D3, N, M, Q = 150, 250, 300, 700, 3, 7
|
||||
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
|
||||
|
||||
from GPy.models import mrd
|
||||
|
|
@ -316,18 +324,19 @@ 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[0:2]
|
||||
# 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, .001)
|
||||
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', _debug=False)
|
||||
|
||||
for i, Y in enumerate(Ylist):
|
||||
m.set('{}_noise'.format(i + 1), Y.var() / 100.)
|
||||
m['{}_noise'.format(i + 1)] = Y.var() / 100.
|
||||
|
||||
m.constrain('variance', logexp_clipped())
|
||||
m.ensure_default_constraints()
|
||||
m.auto_scale_factor = True
|
||||
# m.auto_scale_factor = True
|
||||
|
||||
# cstr = 'variance'
|
||||
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
|
||||
|
|
@ -335,19 +344,19 @@ def mrd_simulation(plot_sim=False):
|
|||
# cstr = 'linear_variance'
|
||||
# m.unconstrain(cstr), m.constrain_positive(cstr)
|
||||
|
||||
# print "initializing beta"
|
||||
# cstr = "noise"
|
||||
# m.unconstrain(cstr); m.constrain_fixed(cstr)
|
||||
# m.optimize('scg', messages=1, max_f_eval=100)
|
||||
print "initializing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain_fixed(cstr)
|
||||
m.optimize('scg', messages=1, max_f_eval=2e3, gtol=100)
|
||||
|
||||
# print "releasing beta"
|
||||
# cstr = "noise"
|
||||
# m.unconstrain(cstr); m.constrain_positive(cstr)
|
||||
print "releasing beta"
|
||||
cstr = "noise"
|
||||
m.unconstrain(cstr); m.constrain(cstr, logexp_clipped())
|
||||
|
||||
np.seterr(all='call')
|
||||
def ipdbonerr(errtype, flags):
|
||||
import ipdb; ipdb.set_trace()
|
||||
np.seterrcall(ipdbonerr)
|
||||
# np.seterr(all='call')
|
||||
# def ipdbonerr(errtype, flags):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
# np.seterrcall(ipdbonerr)
|
||||
|
||||
return m # , mtest
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue