@property
@tc.returns(tc.Optional(tuple))
def labels(self):
"""Returns the tuple containing coordinate labels, else None."""
return self._labels
def index(self, value):
"""Returns the integer position of the given tick label."""
if self._index is None:
raise ValueError('Axis does not have tick labels')
return self._index[value]
# tc class for anything that can be coerced into an Axis
# pylint: disable=invalid-name
AxisLike = tc.Union(Axis, tc.Tuple(string_types, AxisValue))
# pylint: enable=invalid-name
@tc.returns(Axis)
@tc.accepts(AxisLike)
def as_axis(axis_data):
"""Convert an AxisLike object into an Axis.
Args:
axis_data: Axis object or tuple (axis_name, axis_value) describing an axis.
Returns:
Axis object. This may be the original object if axis_data is an Axis.
"""
if isinstance(axis_data, Axis):
@property
@tc.returns(tc.Optional(tuple))
def labels(self):
"""Returns the tuple containing coordinate labels, else None."""
return self._labels
def index(self, value):
"""Returns the integer position of the given tick label."""
if self._index is None:
raise ValueError('Axis does not have tick labels')
return self._index[value]
# tc class for anything that can be coerced into an Axis
# pylint: disable=invalid-name
AxisLike = tc.Union(Axis, tc.Tuple(string_types, AxisValue))
# pylint: enable=invalid-name
@tc.returns(Axis)
@tc.accepts(AxisLike)
def as_axis(axis_data):
"""Convert an AxisLike object into an Axis.
Args:
axis_data: Axis object or tuple (axis_name, axis_value) describing an axis.
Returns:
Axis object. This may be the original object if axis_data is an Axis.
"""
if isinstance(axis_data, Axis):
squeeze_dimensions.append(i)
else:
axes.append(axis)
if squeeze_dimensions:
squeeze_op = array_ops.squeeze(labeled_tensor.tensor,
squeeze_dimensions,
name=scope)
else:
squeeze_op = array_ops.identity(labeled_tensor.tensor, name=scope)
return core.LabeledTensor(squeeze_op, axes)
# pylint: disable=invalid-name
ReduceAxis = tc.Union(string_types, tc.Tuple(string_types,
collections.Hashable))
ReduceAxes = tc.Optional(tc.Union(ReduceAxis, tc.Collection(ReduceAxis)))
# pylint: enable=invalid-name
@tc.returns(core.LabeledTensor)
@tc.accepts(core.LabeledTensorLike, core.LabeledTensorLike,
tc.Optional(string_types))
def matmul(a, b, name=None):
"""Matrix multiply two tensors with rank 1 or 2.
If both tensors have rank 2, a matrix-matrix product is performed.
If one tensor has rank 1 and the other has rank 2, then a matrix-vector
product is performed.
If both tensors have rank 1, then a vector dot-product is performed.
(This behavior matches that of `numpy.dot`.)
else:
axes.append(axis)
if squeeze_dimensions:
squeeze_op = array_ops.squeeze(labeled_tensor.tensor,
squeeze_dimensions,
name=scope)
else:
squeeze_op = array_ops.identity(labeled_tensor.tensor, name=scope)
return core.LabeledTensor(squeeze_op, axes)
# pylint: disable=invalid-name
ReduceAxis = tc.Union(string_types,
tc.Tuple(string_types, collections_abc.Hashable))
ReduceAxes = tc.Optional(tc.Union(ReduceAxis, tc.Collection(ReduceAxis)))
# pylint: enable=invalid-name
@tc.returns(core.LabeledTensor)
@tc.accepts(core.LabeledTensorLike, core.LabeledTensorLike,
tc.Optional(string_types))
def matmul(a, b, name=None):
"""Matrix multiply two tensors with rank 1 or 2.
If both tensors have rank 2, a matrix-matrix product is performed.
If one tensor has rank 1 and the other has rank 2, then a matrix-vector
product is performed.
If both tensors have rank 1, then a vector dot-product is performed.
(This behavior matches that of `numpy.dot`.)
