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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Nicolas 2013-06-04 15:57:47 +01:00
commit bce4c8723c
4 changed files with 23 additions and 80 deletions
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33
GPy/examples/dimensionality_reduction.py
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@ -7,6 +7,7 @@ from matplotlib import pyplot as plt
import GPy
from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
from GPy.util.datasets import swiss_roll_generated
from GPy.core.transformations import logexp
default_seed = np.random.seed(123344)
@ -17,11 +18,11 @@ def BGPLVM(seed=default_seed):
D = 4
# generate GPLVM-like data
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)
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.bias(Q) + 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))
Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
Y1 += .1 * np.random.randn(*Y1.shape)
Y2 += .1 * np.random.randn(*Y2.shape)
Y1 += .3 * np.random.randn(*Y1.shape)
Y2 += .2 * np.random.randn(*Y2.shape)
Y3 += .1 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
@ -262,13 +263,13 @@ def bgplvm_simulation(optimize='scg',
# m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints()
m['noise'] = Y.var() / 100.
m['linear_variance'] = .01
m['linear_variance'] = .001
if optimize:
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,
messages=True, gtol=1e-2)
messages=True, gtol=1e-6)
if plot:
import pylab
m.plot_X_1d()
@ -277,23 +278,21 @@ def bgplvm_simulation(optimize='scg',
m.kern.plot_ARD()
return m
def mrd_simulation(optimize=True, plot_sim=False, **kw):
D1, D2, D3, N, M, Q = 150, 200, 400, 300, 3, 7
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
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)
from GPy.models import mrd
from GPy import kern
from GPy.core.transformations import logexp_clipped
reload(mrd); reload(kern)
k = kern.linear(Q, [0.05] * Q, 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)
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="", initz='permute', **kw)
for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100.
# m.constrain('variance|noise', logexp_clipped(1e-6))
m.ensure_default_constraints()
# DEBUG
@ -301,8 +300,10 @@ def mrd_simulation(optimize=True, plot_sim=False, **kw):
if optimize:
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
def brendan_faces():
@ -323,7 +324,7 @@ def brendan_faces():
m.ensure_default_constraints()
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, :]
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)