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bug fix: slicing can now be turned of by passing None as active_dims

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Max Zwiessele 2014-04-16 09:49:35 +01:00
parent d2f73f13ec
commit a57ca26c89
3 changed files with 34 additions and 52 deletions
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36
GPy/kern/_src/kern.py
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@ -27,25 +27,28 @@ class Kern(Parameterized):
Do not instantiate.
"""
super(Kern, self).__init__(name=name, *a, **kw)
self.active_dims = active_dims if active_dims is not None else slice(0, input_dim)
self.active_dims = active_dims# if active_dims is not None else slice(0, input_dim, 1)
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__)
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)
if self.active_dims is not None and self.input_dim is not None:
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__)
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 np.all(self.active_dims >= 0), 'active dimensions need to be positive. negative indexing is not allowed'
assert self.active_dims.ndim == 1, 'only flat indices allowed, given active_dims.shape={}, provide only indexes to the dimensions (columns) 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
self.useGPU = self._support_GPU and useGPU
@Cache_this(limit=10)
def _slice_X(self, X):
return X[:, self.active_dims]
if self.active_dims is not None:
return X[:, self.active_dims]
return X
def K(self, X, X2):
"""
@ -205,9 +208,10 @@ class CombinationKernel(Kern):
return self._parameters_
def get_input_dim_active_dims(self, kernels, extra_dims = None):
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(np.r_[extra_dims]) if extra_dims is not None else 0)
active_dims = slice(0, input_dim, 1)
#active_dims = reduce(np.union1d, (np.r_[x.active_dims] for x in kernels), np.array([], dtype=int))
#active_dims = np.array(np.concatenate((active_dims, extra_dims if extra_dims is not None else [])), dtype=int)
input_dim = [k.input_dim for k in kernels]
active_dims = None
return input_dim, active_dims
def input_sensitivity(self):