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TEST: Rename parameters is test function.

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This commit is contained in:
Alexander Grigorievskiy 2016-03-15 18:42:28 +02:00
parent f0660dcde0
commit 2be731ef25
1 changed files with 16 additions and 16 deletions
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32
GPy/testing/gpy_kernels_state_space_tests.py
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@ -110,12 +110,12 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.lengthscales.constrain_bounded(0.25, 1000) ss_kernel.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.wavelengths.constrain_bounded(0.15, 100) ss_kernel.period.constrain_bounded(0.15, 100)
gp_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.lengthscales.constrain_bounded(0.25, 1000) gp_kernel.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.wavelengths.constrain_bounded(0.15, 100) gp_kernel.period.constrain_bounded(0.15, 100)
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,
@ -129,12 +129,12 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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_Matern32(1)*GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_Matern32(1)*GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.wavelengths.constrain_bounded(0.15, 100) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
gp_kernel = GPy.kern.Matern32(1)*GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.Matern32(1)*GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.wavelengths.constrain_bounded(0.15, 100) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
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,
@ -203,12 +203,12 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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) + GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.wavelengths.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) + GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.wavelengths.constrain_bounded(3, 8) gp_kernel.std_periodic.period.constrain_bounded(3, 8)
return ss_kernel, gp_kernel return ss_kernel, gp_kernel
@ -300,15 +300,15 @@ class StateSpaceKernelsTests(np.testing.TestCase):
def get_new_kernels(): def get_new_kernels():
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.wavelengths.constrain_bounded(0.15, 100) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \ ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscales.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.wavelengths.constrain_bounded(0.15, 100) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
return ss_kernel, gp_kernel return ss_kernel, gp_kernel