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[vardtc] missing data handling and stochastic update in d

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This commit is contained in:
Max Zwiessele 2014-10-16 12:52:17 +01:00
parent 3358d06e42
commit 26396939e5
6 changed files with 124 additions and 37 deletions
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7
GPy/examples/dimensionality_reduction.py
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@ -208,12 +208,12 @@ 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)
np.random.seed(3000)
k = GPy.kern.Matern32(Q_signal, 1., lengthscale=np.random.uniform(1,6,Q_signal), ARD=1)
k = GPy.kern.Matern32(Q_signal, 10., lengthscale=1+(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]
s2, s1, 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)
@ -360,7 +360,6 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
):
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 = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)