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sparse gp missing data

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Max Zwiessele 2014-02-26 08:23:08 +00:00
parent 51dca0fcbc
commit b928044f40
4 changed files with 22 additions and 17 deletions
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6
GPy/examples/dimensionality_reduction.py
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@ -164,7 +164,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
_np.random.seed(0)
data = GPy.util.datasets.oil()
kernel = GPy.kern.RBF(Q, 1., _np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
Y = data['X'][:N]
m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
m.data_labels = data['Y'][:N].argmax(axis=1)
@ -270,7 +270,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
from GPy import kern
from GPy.models import BayesianGPLVM
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
_, _, 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)
@ -293,7 +293,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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, 5, 9
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
_, _, 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)