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BGPLVM updates and debug helper

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This commit is contained in:
Max Zwiessele 2013-04-25 14:57:23 +01:00
parent e5b61030c3
commit e0f94d6d9c
3 changed files with 234 additions and 61 deletions
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35
GPy/examples/dimensionality_reduction.py
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@ -7,6 +7,7 @@ from matplotlib import pyplot as plt, pyplot
import GPy
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.util.datasets import simulation_BGPLVM
default_seed = np.random.seed(123344)
@ -129,9 +130,9 @@ 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 += .3 * np.random.randn(*Y1.shape)
Y2 += .3 * np.random.randn(*Y2.shape)
Y3 += .3 * np.random.randn(*Y3.shape)
Y1 += .2 * np.random.randn(*Y1.shape)
Y2 += .2 * np.random.randn(*Y2.shape)
Y3 += .2 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0)
@ -162,11 +163,31 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
return slist, [S1, S2, S3], Ylist
def bgplvm_simulation_matlab_compare():
sim_data = simulation_BGPLVM()
Y = sim_data['Y']
S = sim_data['S']
mu = sim_data['mu']
M, [_, Q] = 20, mu.shape
from GPy.models import mrd
from GPy import kern
reload(mrd); reload(kern)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
# X=mu,
# X_variance=S,
_debug=True)
m.ensure_default_constraints()
m['noise'] = .01 # Y.var() / 100.
m['linear_variance'] = .01
return m
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
D1, D2, D3, N, M, Q = 10, 8, 8, 250, 10, 6
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd
@ -176,11 +197,13 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.white(Q, .00001) # + kern.bias(Q)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
# 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['noise'] = Y.var() / 100.
m['linear_variance'] = .001
# m.auto_scale_factor = True
# m.scale_factor = 1.
@ -207,7 +230,7 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
# cstr = 'X_variance'
# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-3, 1.)
m['X_var'] = np.ones(N * Q) * .5 + np.random.randn(N * Q) * .01
# m['X_var'] = np.ones(N * Q) * .5 + np.random.randn(N * Q) * .01
# cstr = "iip"
# m.unconstrain(cstr); m.constrain_fixed(cstr)