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mrd and bgplvm simulation examples working

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Max Zwiessele 2013-06-04 15:33:46 +01:00
parent e68d8d7f34
commit b7083513c0
2 changed files with 22 additions and 17 deletions
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32
GPy/examples/dimensionality_reduction.py
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@ -7,6 +7,7 @@ from matplotlib import pyplot as plt
import GPy import GPy
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.util.datasets import swiss_roll_generated from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp
default_seed = np.random.seed(123344) default_seed = np.random.seed(123344)
@ -17,11 +18,11 @@ def BGPLVM(seed=default_seed):
D = 4 D = 4
# generate GPLVM-like data # generate GPLVM-like data
X = np.random.rand(N, Q) X = np.random.rand(N, Q)
k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001) k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X) K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, D).T Y = np.random.multivariate_normal(np.zeros(N), K, D).T
k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q) k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q) # k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001) # k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
@ -187,8 +188,8 @@ def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
Y2 = S2.dot(np.random.randn(S2.shape[1], D2)) Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
Y3 = S3.dot(np.random.randn(S3.shape[1], D3)) Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
Y1 += .1 * np.random.randn(*Y1.shape) Y1 += .3 * np.random.randn(*Y1.shape)
Y2 += .1 * np.random.randn(*Y2.shape) Y2 += .2 * np.random.randn(*Y2.shape)
Y3 += .1 * np.random.randn(*Y3.shape) Y3 += .1 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0) Y1 -= Y1.mean(0)
@ -262,13 +263,13 @@ def bgplvm_simulation(optimize='scg',
# m.constrain('variance|noise', logexp_clipped()) # m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints() m.ensure_default_constraints()
m['noise'] = Y.var() / 100. m['noise'] = Y.var() / 100.
m['linear_variance'] = .01 m['linear_variance'] = .001
if optimize: if optimize:
print "Optimizing model:" print "Optimizing model:"
m.optimize('bfgs', max_iters=max_f_eval, m.optimize('scg', max_iters=max_f_eval,
max_f_eval=max_f_eval, max_f_eval=max_f_eval,
messages=True, gtol=1e-2) messages=True, gtol=1e-6)
if plot: if plot:
import pylab import pylab
m.plot_X_1d() m.plot_X_1d()
@ -277,8 +278,8 @@ def bgplvm_simulation(optimize='scg',
m.kern.plot_ARD() m.kern.plot_ARD()
return m return m
def mrd_simulation(optimize=True, plot_sim=False, **kw): def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
D1, D2, D3, N, M, Q = 150, 200, 400, 300, 3, 7 D1, D2, D3, N, M, Q = 150, 200, 400, 500, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim) slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd from GPy.models import mrd
@ -287,13 +288,12 @@ def mrd_simulation(optimize=True, plot_sim=False, **kw):
reload(mrd); reload(kern) reload(mrd); reload(kern)
k = kern.linear(Q, [0.05] * Q, True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) k = kern.linear(Q, [.05] * Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(Ylist, Q=Q, M=M, kernels=k, initx="concat", initz='permute', **kw) m = mrd.MRD(Ylist, Q=Q, M=M, kernels=k, initx="", initz='permute', **kw)
for i, Y in enumerate(Ylist): for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100. m['{}_noise'.format(i + 1)] = Y.var() / 100.
# m.constrain('variance|noise', logexp_clipped(1e-6))
m.ensure_default_constraints() m.ensure_default_constraints()
# DEBUG # DEBUG
@ -301,8 +301,10 @@ def mrd_simulation(optimize=True, plot_sim=False, **kw):
if optimize: if optimize:
print "Optimizing Model:" print "Optimizing Model:"
m.optimize('bfgs', messages=1, max_iters=3e3) m.optimize('scg', messages=1, max_iters=5e4, max_f_eval=5e4)
if plot:
m.plot_X_1d()
m.plot_scales()
return m return m
def brendan_faces(): def brendan_faces():
@ -323,7 +325,7 @@ def brendan_faces():
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize('scg', messages=1, max_f_eval=10000) m.optimize('scg', messages=1, max_f_eval=10000)
ax = m.plot_latent(which_indices=(0,1)) ax = m.plot_latent(which_indices=(0, 1))
y = m.likelihood.Y[0, :] y = m.likelihood.Y[0, :]
data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False) data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax) lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)

7
GPy/models/mrd.py
View file

@ -33,8 +33,11 @@ class MRD(model):
Initial latent space Initial latent space
:param X_variance: :param X_variance:
Initial latent space variance Initial latent space variance
:param init: [PCA|random] :param init: [cooncat|single|random]
initialization method to use initialization method to use:
*concat: PCA on concatenated outputs
*single: PCA on each output
*random: random
:param M: :param M:
number of inducing inputs to use number of inducing inputs to use
:param Z: :param Z: