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[statespace] tests even more reliable

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mzwiessele 2016-04-07 10:48:54 +01:00
parent 754106e471
commit 7aff0eb3f8
2 changed files with 11 additions and 10 deletions
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GPy/testing/gpy_kernels_state_space_tests.py
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@ -28,7 +28,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
kalman_filter_type=kalman_filter_type, kalman_filter_type=kalman_filter_type,
use_cython=use_cython) use_cython=use_cython)
m1.likelihood[:] = Y.var()/10. m1.likelihood[:] = Y.var()/100.
if check_gradients: if check_gradients:
self.assertTrue(m1.checkgrad()) self.assertTrue(m1.checkgrad())
@ -95,8 +95,8 @@ class StateSpaceKernelsTests(np.testing.TestCase):
plot = False, points_num=50, x_interval = (0, 20), random=True) plot = False, points_num=50, x_interval = (0, 20), random=True)
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
ss_kernel = GPy.kern.sde_RBF(1, Y.var(), X.ptp()/2., active_dims=[0,]) ss_kernel = GPy.kern.sde_RBF(1, 110., 1.5, active_dims=[0,])
gp_kernel = GPy.kern.RBF(1, Y.var(), X.ptp()/2., active_dims=[0,]) gp_kernel = GPy.kern.RBF(1, 110., 1.5, active_dims=[0,])
self.run_for_model(X, Y, ss_kernel, check_gradients=True, self.run_for_model(X, Y, ss_kernel, check_gradients=True,
predict_X=X, predict_X=X,
@ -202,17 +202,18 @@ class StateSpaceKernelsTests(np.testing.TestCase):
# Sine data <- # Sine data <-
Y = Y + Y1 Y = Y + Y1
Y -= Y.mean()
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
def get_new_kernels(): def get_new_kernels():
ss_kernel = GPy.kern.sde_Linear(1,X) + GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_Linear(1,X,variances=1) + GPy.kern.sde_StdPeriodic(1,period=5.0, variance=300, lengthscale=3., active_dims=[0,])
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(3, 8) #ss_kernel.std_periodic.period.constrain_bounded(3, 8)
gp_kernel = GPy.kern.Linear(1) + GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.Linear(1,variances=1) + GPy.kern.StdPeriodic(1,period=5.0, variance=300, lengthscale=3., active_dims=[0,])
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(3, 8) #gp_kernel.std_periodic.period.constrain_bounded(3, 8)
return ss_kernel, gp_kernel return ss_kernel, gp_kernel