def __init__(self, tensor, slice_indices):
View.__init__(self, tensor)
self._slice = slice_indices
self._tensor_ndim = len(slice_indices)
# Compute the slice's dimensionality and shape.
ndim = 0
shape = []
offset = numpy.zeros(self._tensor_ndim)
interval = numpy.zeros(self._tensor_ndim)
dim_map = {}
base_indices = numpy.zeros(self._tensor_ndim)
for i in xrange(0, self._tensor_ndim):
index = slice_indices[i]
if hasattr(index, 'indices'):
start, end, step = index.indices(tensor.shape[i])
shape.append(int(ceil(float(end - start) / step)))
offset[ndim] = start
interval[ndim] = step
dim_map[ndim] = i
ndim += 1
else:
base_indices[i] = index
self.ndim = ndim
self.shape = tuple(shape)
# These vars help perform the mapping from slice indices -> tensor indices
self._offset = offset
self._interval = interval
self._dim_map = dim_map
self._base_indices = base_indices
def __init__(self, tensor, slice_indices):
View.__init__(self, tensor)
self._slice = slice_indices
self._tensor_ndim = len(slice_indices)
# Compute the slice's dimensionality and shape.
ndim = 0
shape = []
offset = numpy.zeros(self._tensor_ndim)
interval = numpy.zeros(self._tensor_ndim)
dim_map = {}
base_indices = numpy.zeros(self._tensor_ndim)
for i in xrange(0, self._tensor_ndim):
index = slice_indices[i]
if hasattr(index, 'indices'):
start, end, step = index.indices(tensor.shape[i])
shape.append(int(ceil(float(end - start)/step)))
offset[ndim] = start
interval[ndim] = step
dim_map[ndim] = i
ndim += 1
else:
base_indices[i] = index
self.ndim = ndim
self.shape = tuple(shape)
# These vars help perform the mapping from slice indices -> tensor indices
self._offset = offset
self._interval = interval
self._dim_map = dim_map
self._base_indices = base_indices
def __init__(self, tensor, dim):
View.__init__(self, tensor)
if dim >= tensor.ndim:
raise IndexError('Unfolding index (%s) out of bounds (%s dims)' %
(dim, tensor.ndim))
self.dim = dim
self.otherdims = [x for x in range(self.tensor.ndim) if x != dim]
def __init__(self, tensor, dim):
View.__init__(self, tensor)
if dim >= tensor.ndim:
raise IndexError('Unfolding index (%s) out of bounds (%s dims)' %
(dim, tensor.ndim))
self.dim = dim
self.otherdims = [x for x in range(self.tensor.ndim) if x != dim]
def __init__(self, tensor, label_lists=None):
View.__init__(self, tensor)
shape = tensor.shape
if label_lists is None:
label_lists = [None]*self.tensor.ndim
if tensor.ndim != len(label_lists):
raise IndexError('Number of label lists (%d) does not match number of dimensions (%s)' % (len(label_lists), tensor.ndim))
self._labels = [indexable_set(l, shape[mode])
for mode, l in enumerate(label_lists)]
self.tensor.ensure_index_is_valid(tuple(len(label_list)-1 for label_list in self._labels))
def __init__(self, tensor, label_lists=None):
View.__init__(self, tensor)
shape = tensor.shape
if label_lists is None:
label_lists = [None] * self.tensor.ndim
if tensor.ndim != len(label_lists):
raise IndexError(
"Number of label lists (%d) does not match number of dimensions (%s)" % (len(label_lists), tensor.ndim)
)
self._labels = [indexable_set(l, shape[mode]) for mode, l in enumerate(label_lists)]
self.tensor.ensure_index_is_valid(tuple(len(label_list) - 1 for label_list in self._labels))
def __init__(self, tensor, mode=None):
'''
* tensor: tensor to view.
* mode: which mode to normalize over (0 = rows, 1 = columns, etc.)
'''
if len(tensor) == 0:
raise ValueError('Tensor ' + repr(tensor) + ' is empty.')
# If this isn't a MeanSubtractedView, the underlying tensor can't be a normalized view.
if not isinstance(self, MeanSubtractedView) and tensor.stack_contains(BaseNormalizedView):
raise TypeError('Already normalized; .unnormalized() first.')
# The underlying tensor must be numeric.
for part in tensor.example_key():
if not isinstance(part, (int, long)):
raise InvalidLayeringException
View.__init__(self, tensor)
self.base = 0.0
self._normalize_mode = mode
self.refresh_norms()
def __init__(self, tensor, mode=None):
'''
* tensor: tensor to view.
* mode: which mode to normalize over (0 = rows, 1 = columns, etc.)
'''
if len(tensor) == 0:
raise ValueError('Tensor ' + repr(tensor) + ' is empty.')
# If this isn't a MeanSubtractedView, the underlying tensor can't be a normalized view.
if not isinstance(self, MeanSubtractedView) and tensor.stack_contains(
BaseNormalizedView):
raise TypeError('Already normalized; .unnormalized() first.')
# The underlying tensor must be numeric.
for part in tensor.example_key():
if not isinstance(part, (int, long)):
raise InvalidLayeringException
View.__init__(self, tensor)
self.base = 0.0
self._normalize_mode = mode
self.refresh_norms()