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[missing data] general implementation for subsetting data

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This commit is contained in:
Max Zwiessele 2014-10-08 12:03:51 +01:00
parent d93fac8c13
commit fa7807ee6f
7 changed files with 329 additions and 108 deletions
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6
GPy/examples/dimensionality_reduction.py
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@ -367,14 +367,14 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
Y = Ylist[0]
k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
inan = _np.random.binomial(1, .8, size=Y.shape).astype(bool) # 80% missing data
inan = _np.random.binomial(1, .2, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = _np.nan
m = BayesianGPLVM(Ymissing, Q, init="random", num_inducing=num_inducing,
inference_method=VarDTCMissingData(inan=inan), kernel=k)
kernel=k, missing_data=True)
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
m.X.variance[:] = _np.random.uniform(0,.1,m.X.shape)
m.likelihood.variance = .01
m.parameters_changed()
m.Yreal = Y