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psi_stat slices for kernels

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Max Zwiessele 2014-03-12 12:03:37 +00:00
parent dfb63860ca
commit 54239555a1
5 changed files with 74 additions and 36 deletions
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18
GPy/kern/_src/kern.py
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@ -15,6 +15,7 @@ class Kern(Parameterized):
# found in kernel_slice_operations
__metaclass__ = KernCallsViaSlicerMeta
#===========================================================================
_debug=False
def __init__(self, input_dim, name, *a, **kw):
"""
The base class for a kernel: a positive definite function
@ -27,12 +28,12 @@ class Kern(Parameterized):
"""
super(Kern, self).__init__(name=name, *a, **kw)
if isinstance(input_dim, int):
self.active_dims = slice(0, input_dim)
self.active_dims = np.r_[0:input_dim]
self.input_dim = input_dim
else:
self.active_dims = input_dim
self.active_dims = np.r_[input_dim]
self.input_dim = len(self.active_dims)
self._sliced_X = False
self._sliced_X = 0
@Cache_this(limit=10)#, ignore_args = (0,))
def _slice_X(self, X):
@ -60,14 +61,13 @@ class Kern(Parameterized):
raise NotImplementedError
def gradients_X_diag(self, dL_dKdiag, X):
raise NotImplementedError
def update_gradients_full(self, dL_dK, X, X2):
"""Set the gradients of all parameters when doing full (N) inference."""
raise NotImplementedError
def update_gradients_diag(self, dL_dKdiag, X):
"""Set the gradients for all parameters for the derivative of the diagonal of the covariance w.r.t the kernel parameters."""
raise NotImplementedError
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
"""
Set the gradients of all parameters when doing inference with
@ -188,7 +188,7 @@ class Kern(Parameterized):
class CombinationKernel(Kern):
def __init__(self, kernels, name):
assert all([isinstance(k, Kern) for k in kernels])
input_dim = reduce(np.union1d, (np.r_[x.active_dims] for x in kernels))
input_dim = reduce(np.union1d, (x.active_dims for x in kernels))
super(CombinationKernel, self).__init__(input_dim, name)
self.add_parameters(*kernels)
@ -196,12 +196,6 @@ class CombinationKernel(Kern):
def parts(self):
return self._parameters_
def update_gradients_full(self, dL_dK, X, X2=None):
[p.update_gradients_full(dL_dK, X, X2) for p in self.parts]
def update_gradients_diag(self, dL_dK, X):
[p.update_gradients_diag(dL_dK, X) for p in self.parts]
def input_sensitivity(self):
in_sen = np.zeros((self.num_params, self.input_dim))
for i, p in enumerate(self.parts):