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[indexing&memory] in memory view more efficient, catching some indexing errors

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mzwiessele 2014-05-14 11:38:00 +01:00
parent 8d6eed6010
commit 5b8bd88acf
2 changed files with 18 additions and 8 deletions
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11
GPy/core/parameterization/param.py
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@ -265,9 +265,16 @@ class Param(OptimizationHandlable, ObsAr):
and len(set(map(len, clean_curr_slice))) <= 1):
return numpy.fromiter(itertools.izip(*clean_curr_slice),
dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
expanded_index = list(self._expand_index(slice_index))
return numpy.fromiter(itertools.product(*expanded_index),
try:
expanded_index = list(self._expand_index(slice_index))
indices = numpy.fromiter(itertools.product(*expanded_index),
dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
except:
print "Warning: extended indexing was used"
indices = np.indices(self._realshape_, dtype=int)
indices = indices[(slice(None),)+slice_index]
indices = np.rollaxis(indices, 0, indices.ndim)
return indices
def _max_len_names(self, gen, header):
gen = map(lambda x: " ".join(map(str, x)), gen)
return reduce(lambda a, b:max(a, len(b)), gen, len(header))

15
GPy/core/parameterization/parameter_core.py
View file

@ -541,7 +541,7 @@ class Constrainable(Nameable, Indexable, Observable):
def _add_to_index_operations(self, which, reconstrained, what, warning):
"""
Helper preventing copy code.
This addes the given what (transformation, prior etc) to parameter index operations which.
This adds the given what (transformation, prior etc) to parameter index operations which.
revonstrained are reconstrained indices.
warn when reconstraining parameters if warning is True.
TODO: find out which parameters have changed specifically
@ -710,7 +710,7 @@ class Parameterizable(OptimizationHandlable):
Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization.
"""
if self._param_array_ is None:
if self.__dict__.get('_param_array_', None) is None:
self._param_array_ = np.empty(self.size, dtype=np.float64)
return self._param_array_
@ -769,7 +769,7 @@ class Parameterizable(OptimizationHandlable):
#=========================================================================
@property
def gradient(self):
if not hasattr(self, '_gradient_array_'):
if self.__dict__.get('_gradient_array_', None) is None:
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
return self._gradient_array_
@ -909,10 +909,12 @@ class Parameterizable(OptimizationHandlable):
if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
# no parameters for this class
return
old_size = 0
self.param_array = np.empty(self.size, dtype=np.float64)
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
if self.param_array.size != self.size:
self.param_array = np.empty(self.size, dtype=np.float64)
if self.gradient.size != self.size:
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
old_size = 0
self._param_slices_ = []
for i, p in enumerate(self._parameters_):
p._parent_ = self
@ -927,6 +929,7 @@ class Parameterizable(OptimizationHandlable):
if not p.param_array.flags['C_CONTIGUOUS']:
raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
p.param_array.data = self.param_array[pslice].data
p.full_gradient.data = self.full_gradient[pslice].data