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[testing] BGPLVM

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mzwiessele 2015-10-06 14:22:29 +01:00
parent a6ad9c33a6
commit 133a815950
5 changed files with 33 additions and 19 deletions
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22
GPy/testing/plotting_tests.py
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@ -181,13 +181,33 @@ def test_gplvm():
m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
m.plot_prediction_fit(which_data_ycols=(0,1)) # ignore this test, as plotting is not consistent!!
m.plot_steepest_gradient_map(resolution=7)
plt.close('all')
m.plot_latent()
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=7)
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
def test_bayesian_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import BayesianGPLVM
Q = 3
_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
Y = Ylist[0]
k = RBF(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", kernel=k)
m.likelihood.variance = .1
m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
m.plot_prediction_fit(which_data_ycols=(0,1)) # ignore this test, as plotting is not consistent!!
plt.close('all')
m.plot_latent()
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=7)
for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["latent", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
if __name__ == '__main__':
import nose