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[updates] updated update structure immensely

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Max Zwiessele 2014-09-22 09:26:01 +01:00
parent 4543fc3480
commit ef05f49b8b
5 changed files with 99 additions and 53 deletions
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90
GPy/examples/dimensionality_reduction.py
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@ -210,7 +210,62 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
plt.close(fig)
return m
def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
Q_signal = 4
import GPy
import numpy as np
np.random.seed(0)
k = GPy.kern.Matern32(Q_signal, 1., lengthscale=np.random.uniform(1,6,Q_signal), ARD=1)
t = np.c_[[np.linspace(-1,5,N) for _ in range(Q_signal)]].T
K = k.K(t)
s1, s2, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
slist = [sS, s1, s2, s3]
slist_names = ["sS", "s1", "s2", "s3"]
Ylist = [Y1, Y2, Y3]
if plot_sim:
import pylab
import matplotlib.cm as cm
import itertools
fig = pylab.figure("MRD Simulation Data", figsize=(8, 6))
fig.clf()
ax = fig.add_subplot(2, 1, 1)
labls = slist_names
for S, lab in itertools.izip(slist, labls):
ax.plot(S, label=lab)
ax.legend()
for i, Y in enumerate(Ylist):
ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
ax.set_title("Y{}".format(i + 1))
pylab.draw()
pylab.tight_layout()
return slist, [S1, S2, S3], Ylist
def _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS):
S1 = _np.hstack([s1, sS])
S2 = _np.hstack([s2, s3, sS])
S3 = _np.hstack([s3, sS])
Y1 = S1.dot(_np.random.randn(S1.shape[1], D1))
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 += .2 * _np.random.randn(*Y2.shape)
Y3 += .25 * _np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0)
Y3 -= Y3.mean(0)
Y1 /= Y1.std(0)
Y2 /= Y2.std(0)
Y3 /= Y3.std(0)
return Y1, Y2, Y3, S1, S2, S3
def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
_np.random.seed(1234)
x = _np.linspace(0, 4 * _np.pi, N)[:, None]
@ -229,24 +284,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
s3 -= s3.mean(); s3 /= s3.std(0)
sS -= sS.mean(); sS /= sS.std(0)
S1 = _np.hstack([s1, sS])
S2 = _np.hstack([s2, s3, sS])
S3 = _np.hstack([s3, sS])
Y1 = S1.dot(_np.random.randn(S1.shape[1], D1))
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 += .2 * _np.random.randn(*Y2.shape)
Y3 += .25 * _np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0)
Y3 -= Y3.mean(0)
Y1 /= Y1.std(0)
Y2 /= Y2.std(0)
Y3 /= Y3.std(0)
Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
slist = [sS, s1, s2, s3]
slist_names = ["sS", "s1", "s2", "s3"]
@ -298,7 +336,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
#k = kern.RBF(Q, ARD=True, lengthscale=10.)
@ -323,7 +361,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
from GPy.models import SSGPLVM
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True, useGPU=useGPU)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
#k = kern.RBF(Q, ARD=True, lengthscale=10.)
@ -349,7 +387,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
@ -380,7 +418,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
#Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
@ -390,7 +428,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)
m.optimize(messages=verbose, max_iters=8e3)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
@ -402,7 +440,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
#Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
@ -542,7 +580,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
y = m.likelihood.Y[0, :]
data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
raw_input('Press enter to finish')
#raw_input('Press enter to finish')
return m