folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Documentation for v0.1 version (#199)

Browse Source 
* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
Download Patch File Download Diff File Expand all files Collapse all files

48
pina/label_tensor.py
View File

@@ -17,9 +17,9 @@ class LabelTensor(torch.Tensor):
labels. Such labels uniquely identify the columns of the tensor,
allowing for an easier manipulation.
:param torch.Tensor x: the data tensor.
:param labels: the labels of the columns.
:type labels: str or iterable(str)
:param torch.Tensor x: The data tensor.
:param labels: The labels of the columns.
:type labels: str | list(str) | tuple(str)
:Example:
>>> from pina import LabelTensor
@@ -72,10 +72,8 @@ class LabelTensor(torch.Tensor):
labels = [labels]
if len(labels) != x.shape[-1]:
raise ValueError(
'the tensor has not the same number of columns of '
'the passed labels.'
)
raise ValueError('the tensor has not the same number of columns of '
'the passed labels.')
self._labels = labels
@property
@@ -90,11 +88,10 @@ class LabelTensor(torch.Tensor):
@labels.setter
def labels(self, labels):
if len(labels) != self.shape[self.ndim - 1]: # small check
raise ValueError(
'the tensor has not the same number of columns of '
'the passed labels.')
raise ValueError('The tensor has not the same number of columns of '
'the passed labels.')
self._labels = labels # assign the label
self._labels = labels # assign the label
@staticmethod
def vstack(label_tensors):
@@ -123,7 +120,7 @@ class LabelTensor(torch.Tensor):
Clone the LabelTensor. For more details, see
:meth:`torch.Tensor.clone`.
:return: a copy of the tensor
:return: A copy of the tensor.
:rtype: LabelTensor
"""
# # used before merging
@@ -173,12 +170,12 @@ class LabelTensor(torch.Tensor):
def extract(self, label_to_extract):
"""
Extract the subset of the original tensor by returning all the columns
corresponding to the passed `label_to_extract`.
corresponding to the passed ``label_to_extract``.
:param label_to_extract: the label(s) to extract.
:type label_to_extract: str or iterable(str)
:raises TypeError: labels are not str
:raises ValueError: label to extract is not in the labels list
:param label_to_extract: The label(s) to extract.
:type label_to_extract: str | list(str) | tuple(str)
:raises TypeError: Labels are not ``str``.
:raises ValueError: Label to extract is not in the labels ``list``.
"""
if isinstance(label_to_extract, str):
@@ -211,7 +208,7 @@ class LabelTensor(torch.Tensor):
return detached
def requires_grad_(self, mode = True) -> Tensor:
def requires_grad_(self, mode = True):
lt = super().requires_grad_(mode)
lt.labels = self.labels
return lt
@@ -220,9 +217,9 @@ class LabelTensor(torch.Tensor):
"""
Return a copy of the merged tensors.
:param LabelTensor lt: the tensor to merge.
:param LabelTensor lt: The tensor to merge.
:param str mode: {'std', 'first', 'cross'}
:return: the merged tensors
:return: The merged tensors.
:rtype: LabelTensor
"""
if set(self.labels).intersection(lt.labels):
@@ -239,12 +236,9 @@ class LabelTensor(torch.Tensor):
n1 = tensor1.shape[0]
n2 = tensor2.shape[0]
tensor1 = LabelTensor(
tensor1.repeat(n2, 1),
labels=tensor1.labels)
tensor2 = LabelTensor(
tensor2.repeat_interleave(n1, dim=0),
labels=tensor2.labels)
tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
tensor2 = LabelTensor(tensor2.repeat_interleave(n1, dim=0),
labels=tensor2.labels)
new_tensor = torch.cat((tensor1, tensor2), dim=1)
new_tensor = new_tensor.as_subclass(LabelTensor)
@@ -290,7 +284,7 @@ class LabelTensor(torch.Tensor):
def __len__(self) -> int:
return super().__len__()
def __str__(self):
if hasattr(self, 'labels'):
s = f'labels({str(self.labels)})\n'