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added sparsegp with missing data

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This commit is contained in:
Max Zwiessele 2014-02-18 15:52:33 +00:00
parent e96d40b4cc
commit d0c563ff0a
19 changed files with 572 additions and 325 deletions
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43
GPy/examples/dimensionality_reduction.py
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@ -3,12 +3,11 @@
import numpy as _np
default_seed = _np.random.seed(123344)
def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False, output_dim=1e4):
def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
"""
model for testing purposes. Samples from a GP with rbf kernel and learns
the samples with a new kernel. Normally not for optimization, just model cheking
"""
from GPy.likelihoods.gaussian import Gaussian
import GPy
num_inputs = 13
@ -36,12 +35,17 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False,
# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
p = .3
m = GPy.models.BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
if nan:
m.inference_method = GPy.inference.latent_function_inference.var_dtc.VarDTCMissingData()
m.Y[_np.random.binomial(1,p,size=(Y.shape))] = _np.nan
m.parameters_changed()
#===========================================================================
# randomly obstruct data with percentage p
p = .8
Y_obstruct = Y.copy()
Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
#===========================================================================
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
@ -276,6 +280,35 @@ def bgplvm_simulation(optimize=True, verbose=1,
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
def bgplvm_simulation_missing_data(optimize=True, verbose=1,
plot=True, plot_sim=False,
max_iters=2e4,
):
from GPy import kern
from GPy.models import BayesianGPLVM
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
inan = _np.random.binomial(1, .3, size=Y.shape)
m = BayesianGPLVM(Y, Q, init="random", num_inducing=num_inducing, kernel=k)
m.inference_method = VarDTCMissingData()
m.Y[inan] = _np.nan
m.parameters_changed()
if optimize:
print "Optimizing model:"
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.q.plot("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m
def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD