apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 12:32:40 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

kernel slices allowed

Browse source
This commit is contained in:
Max Zwiessele 2014-03-14 10:55:16 +00:00
parent b5811011e0
commit 600b1bde3c
3 changed files with 29 additions and 18 deletions
Download patch file Download diff file Expand all files Collapse all files

20
GPy/kern/_src/kern.py
View file

@ -27,10 +27,18 @@ class Kern(Parameterized):
Do not instantiate.
"""
super(Kern, self).__init__(name=name, *a, **kw)
self.active_dims = active_dims or slice(0, input_dim)
self.active_dims = active_dims if active_dims is not None else slice(0, input_dim)
self.input_dim = input_dim
assert isinstance(self.active_dims, (slice, list, tuple, np.ndarray)), 'active_dims needs to be an array-like or slice object over dimensions, {} given'.format(self.active_dims.__class__)
assert self.active_dims.size == self.input_dim, "input_dim {} does not match len(active_dim) {}".format(self.input_dim, self.active_dims.size)
if isinstance(self.active_dims, slice):
self.active_dims = slice(self.active_dims.start or 0, self.active_dims.stop or self.input_dim, self.active_dims.step or 1)
active_dim_size = int(np.round((self.active_dims.stop-self.active_dims.start)/self.active_dims.step))
elif isinstance(self.active_dims, np.ndarray):
assert self.active_dims.ndim == 1, 'only flat indices allowed, given active_dims.shape={}, provide only indexes to the dimensions of the input'.format(self.active_dims.shape)
active_dim_size = self.active_dims.size
else:
active_dim_size = len(self.active_dims)
assert active_dim_size == self.input_dim, "input_dim={} does not match len(active_dim)={}, active_dims={}".format(self.input_dim, active_dim_size, self.active_dims)
self._sliced_X = 0
@Cache_this(limit=10)
@ -207,10 +215,12 @@ class CombinationKernel(Kern):
assert all([isinstance(k, Kern) for k in kernels])
import itertools
# make sure the active dimensions of all underlying kernels are covered:
ma = reduce(lambda a,b: max(a, b.stop if isinstance(b, slice) else max(b)), itertools.chain((x.active_dims for x in kernels), [extra_dims]), 0)
input_dim = np.r_[0:ma+1]
#ma = reduce(lambda a,b: max(a, b.stop if isinstance(b, slice) else max(b)), itertools.chain((x.active_dims for x in kernels)), 0)
active_dims = reduce(np.union1d, (np.r_[x.active_dims] for x in kernels), np.array([], dtype=int))
input_dim = active_dims.max()+1 + len(extra_dims)
active_dims = slice(active_dims.max()+1+len(extra_dims))
# initialize the kernel with the full input_dim
super(CombinationKernel, self).__init__(input_dim, name)
super(CombinationKernel, self).__init__(input_dim, active_dims, name)
self.extra_dims = extra_dims
self.add_parameters(*kernels)