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mrd and bgplvm updates to conform new vardtc

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Max Zwiessele 2014-03-24 13:33:16 +00:00
parent 3db095338d
commit 1294c24a28
3 changed files with 38 additions and 21 deletions
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17
GPy/examples/dimensionality_reduction.py
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@ -277,7 +277,9 @@ def bgplvm_simulation(optimize=True, verbose=1,
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
#k = kern.RBF(Q, ARD=True, lengthscale=10.)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
m.likelihood.variance = .1
if optimize:
print "Optimizing model:"
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
@ -299,15 +301,16 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
_, _, 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, .6, size=Y.shape).astype(bool)
m = BayesianGPLVM(Y.copy(), Q, init="random", num_inducing=num_inducing, kernel=k)
m.inference_method = VarDTCMissingData()
m.Y[inan] = _np.nan
m.X.variance *= .1
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
m.likelihood.variance = .01
m.parameters_changed()
m.Yreal = Y
if optimize:
print "Optimizing model:"
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
@ -325,11 +328,11 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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 = [Ylist[0]]
k = [kern.Linear(Q, ARD=True) + kern.White(Q, 1e-4) for _ in range(len(Ylist))]
k = [kern.Linear(Q, ARD=True) for _ in range(len(Ylist))]
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="", initz='permute', **kw)
m['.*noise'] = [Y.var()/500. for Y in Ylist]
#for i, Y in enumerate(Ylist):
# m['.*Y_{}.*Gaussian.*noise'.format(i)] = Y.var(1) / 500.