spateo.alignment.methods.deprecated_morpho_sparse_utils
=======================================================

.. py:module:: spateo.alignment.methods.deprecated_morpho_sparse_utils


Attributes
----------

.. autoapisummary::

   spateo.alignment.methods.deprecated_morpho_sparse_utils.nx_torch
   spateo.alignment.methods.deprecated_morpho_sparse_utils._cat
   spateo.alignment.methods.deprecated_morpho_sparse_utils._dot
   spateo.alignment.methods.deprecated_morpho_sparse_utils._split
   spateo.alignment.methods.deprecated_morpho_sparse_utils._where
   spateo.alignment.methods.deprecated_morpho_sparse_utils._repeat_interleave


Functions
---------

.. autoapisummary::

   spateo.alignment.methods.deprecated_morpho_sparse_utils.calc_distance
   spateo.alignment.methods.deprecated_morpho_sparse_utils.calc_P_related
   spateo.alignment.methods.deprecated_morpho_sparse_utils.get_optimal_R_sparse
   spateo.alignment.methods.deprecated_morpho_sparse_utils._init_guess_sigma2
   spateo.alignment.methods.deprecated_morpho_sparse_utils._init_guess_beta2
   spateo.alignment.methods.deprecated_morpho_sparse_utils._construct_label_mask
   spateo.alignment.methods.deprecated_morpho_sparse_utils._dense_to_sparse
   spateo.alignment.methods.deprecated_morpho_sparse_utils._SparseTensor
   spateo.alignment.methods.deprecated_morpho_sparse_utils._cos_similarity
   spateo.alignment.methods.deprecated_morpho_sparse_utils._cosine_distance_backend
   spateo.alignment.methods.deprecated_morpho_sparse_utils._cos_similarity
   spateo.alignment.methods.deprecated_morpho_sparse_utils._dist
   spateo.alignment.methods.deprecated_morpho_sparse_utils.torch_like_split
   spateo.alignment.methods.deprecated_morpho_sparse_utils._sort


Module Contents
---------------

.. py:function:: calc_distance(X_A: Union[numpy.ndarray, torch.Tensor], X_B: Union[numpy.ndarray, torch.Tensor], metric: str = 'euc', batch_capacity: int = 1, use_sparse: bool = False, sparse_method: str = 'topk', threshold: Union[int, float] = 100, return_mask: bool = False, save_to_cpu: bool = False, **kwargs)

.. py:function:: calc_P_related(XnAHat: Union[numpy.ndarray, torch.Tensor], XnB: Union[numpy.ndarray, torch.Tensor], X_A: Union[numpy.ndarray, torch.Tensor], X_B: Union[numpy.ndarray, torch.Tensor], sigma2: Union[int, float, numpy.ndarray, torch.Tensor], sigma2_robust: Union[int, float, numpy.ndarray, torch.Tensor], beta2: Union[int, float, numpy.ndarray, torch.Tensor], spatial_outlier, col_mul=None, batch_capacity: int = 1, dissimilarity: str = 'kl', labelA: Optional[pandas.Series] = None, labelB: Optional[pandas.Series] = None, label_transfer_prior: Optional[dict] = None, top_k: int = 1024)

.. py:function:: get_optimal_R_sparse(coordsA: Union[numpy.ndarray, torch.Tensor], coordsB: Union[numpy.ndarray, torch.Tensor], P: Union[numpy.ndarray, torch.Tensor, torch.sparse_coo_tensor], R_init: Union[numpy.ndarray, torch.Tensor])

   Get the optimal rotation matrix R

   :param coordsA: The first input matrix with shape n x d
   :type coordsA: Union[np.ndarray, torch.Tensor]
   :param coordsB: The second input matrix with shape n x d
   :type coordsB: Union[np.ndarray, torch.Tensor]
   :param P: The optimal transport matrix with shape n x n
   :type P: Union[np.ndarray, torch.Tensor]

   :returns: The optimal rotation matrix R with shape d x d
   :rtype: Union[np.ndarray, torch.Tensor]


.. py:function:: _init_guess_sigma2(XA, XB, subsample=2000)

.. py:function:: _init_guess_beta2(nx, XA, XB, dissimilarity='kl', partial_robust_level=1, beta2=None, beta2_end=None, subsample=2000)

.. py:function:: _construct_label_mask(nx, labelA, labelB, label_transfer_prior, type_as)

.. py:function:: _dense_to_sparse(mat: Union[numpy.ndarray, torch.Tensor], sparse_method: str = 'topk', threshold: Union[int, float] = 100, axis: int = 0, descending=False)

.. py:function:: _SparseTensor(nx, row, col, value, sparse_sizes)

.. py:function:: _cos_similarity(mat1: Union[numpy.ndarray, torch.Tensor], mat2: Union[numpy.ndarray, torch.Tensor])

.. py:function:: _cosine_distance_backend(X: Union[numpy.ndarray, torch.Tensor], Y: Union[numpy.ndarray, torch.Tensor], eps: float = 1e-08) -> Union[numpy.ndarray, torch.Tensor]

   Compute the pairwise cosine similarity between all pairs of samples in matrices X and Y.

   :param X: Matrix with shape (N, D), where each row represents a sample.
   :type X: np.ndarray or torch.Tensor
   :param Y: Matrix with shape (M, D), where each row represents a sample.
   :type Y: np.ndarray or torch.Tensor
   :param eps: A small value to avoid division by zero. Default is 1e-8.
   :type eps: float, optional

   :returns: Pairwise cosine similarity matrix with shape (N, M).
   :rtype: np.ndarray or torch.Tensor

   :raises AssertionError: If the number of features in X and Y do not match.


.. py:function:: _cos_similarity(mat1: Union[numpy.ndarray, torch.Tensor], mat2: Union[numpy.ndarray, torch.Tensor])

.. py:function:: _dist(mat1: Union[numpy.ndarray, torch.Tensor], mat2: Union[numpy.ndarray, torch.Tensor], metric: str = 'euc') -> Union[numpy.ndarray, torch.Tensor]

.. py:data:: nx_torch

.. py:data:: _cat

.. py:data:: _dot

.. py:data:: _split

.. py:function:: torch_like_split(arr, size, dim=0)

.. py:data:: _where

.. py:data:: _repeat_interleave

.. py:function:: _sort(nx, arr, axis=-1, descending=False)