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Merge branch 'devel' of https://github.com/SheffieldML/GPy into devel

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Neil Lawrence 2014-05-14 12:15:46 +01:00
commit c291d5b2ad
2 changed files with 18 additions and 8 deletions
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11
GPy/core/parameterization/param.py
View file

@ -265,9 +265,16 @@ class Param(OptimizationHandlable, ObsAr):
and len(set(map(len, clean_curr_slice))) <= 1): and len(set(map(len, clean_curr_slice))) <= 1):
return numpy.fromiter(itertools.izip(*clean_curr_slice), return numpy.fromiter(itertools.izip(*clean_curr_slice),
dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_)) dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
expanded_index = list(self._expand_index(slice_index)) try:
return numpy.fromiter(itertools.product(*expanded_index), 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_)) 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): def _max_len_names(self, gen, header):
gen = map(lambda x: " ".join(map(str, x)), gen) gen = map(lambda x: " ".join(map(str, x)), gen)
return reduce(lambda a, b:max(a, len(b)), gen, len(header)) 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): def _add_to_index_operations(self, which, reconstrained, what, warning):
""" """
Helper preventing copy code. 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. revonstrained are reconstrained indices.
warn when reconstraining parameters if warning is True. warn when reconstraining parameters if warning is True.
TODO: find out which parameters have changed specifically TODO: find out which parameters have changed specifically
@ -710,7 +710,7 @@ class Parameterizable(OptimizationHandlable):
Array representing the parameters of this class. Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization. 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) self._param_array_ = np.empty(self.size, dtype=np.float64)
return self._param_array_ return self._param_array_
@ -769,7 +769,7 @@ class Parameterizable(OptimizationHandlable):
#========================================================================= #=========================================================================
@property @property
def gradient(self): 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) self._gradient_array_ = np.empty(self.size, dtype=np.float64)
return self._gradient_array_ return self._gradient_array_
@ -909,10 +909,12 @@ class Parameterizable(OptimizationHandlable):
if not hasattr(self, "_parameters_") or len(self._parameters_) < 1: if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
# no parameters for this class # no parameters for this class
return return
old_size = 0 if self.param_array.size != self.size:
self.param_array = np.empty(self.size, dtype=np.float64) self.param_array = np.empty(self.size, dtype=np.float64)
self._gradient_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_ = [] self._param_slices_ = []
for i, p in enumerate(self._parameters_): for i, p in enumerate(self._parameters_):
p._parent_ = self p._parent_ = self
@ -927,6 +929,7 @@ class Parameterizable(OptimizationHandlable):
if not p.param_array.flags['C_CONTIGUOUS']: 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" 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.param_array.data = self.param_array[pslice].data
p.full_gradient.data = self.full_gradient[pslice].data p.full_gradient.data = self.full_gradient[pslice].data