spateo.alignment.methods.deprecated_morpho_sparse
=================================================

.. py:module:: spateo.alignment.methods.deprecated_morpho_sparse


Functions
---------

.. autoapisummary::

   spateo.alignment.methods.deprecated_morpho_sparse.con_K
   spateo.alignment.methods.deprecated_morpho_sparse.get_P_sparse
   spateo.alignment.methods.deprecated_morpho_sparse.BA_align_sparse


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

.. py:function:: con_K(X: Union[numpy.ndarray, torch.Tensor], Y: Union[numpy.ndarray, torch.Tensor], beta: Union[int, float] = 0.01, use_chunk: bool = False) -> Union[numpy.ndarray, torch.Tensor]

   con_K constructs the Squared Exponential (SE) kernel, where K(i,j)=k(X_i,Y_j)=exp(-beta*||X_i-Y_j||^2).

   :param X: The first vector X\in\mathbb{R}^{N   imes d}
   :param Y: The second vector X\in\mathbb{R}^{M  imes d}
   :param beta: The length-scale of the SE kernel.
   :param use_chunk: Whether to use chunk to reduce the GPU memory usage. Note that if set to ``True'' it will slow down the calculation. Defaults to False.
   :type use_chunk: bool, optional

   :returns: The kernel K\in\mathbb{R}^{N imes M}
   :rtype: K


.. py:function:: get_P_sparse(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], beta2: Union[int, float, numpy.ndarray, torch.Tensor], alpha: Union[numpy.ndarray, torch.Tensor], gamma: Union[float, numpy.ndarray, torch.Tensor], Sigma: Union[numpy.ndarray, torch.Tensor], samples_s: Optional[List[float]] = None, outlier_variance: float = None, label_mask: Optional[numpy.ndarray] = None, batch_capacity: int = 1, labelA: Optional[pandas.Series] = None, labelB: Optional[pandas.Series] = None, label_transfer_prior: Optional[dict] = None, top_k: int = 1024, dissimilarity: str = 'kl')

.. py:function:: BA_align_sparse(sampleA: anndata.AnnData, sampleB: anndata.AnnData, genes: Optional[Union[List, torch.Tensor]] = None, spatial_key: str = 'spatial', key_added: str = 'align_spatial', iter_key_added: Optional[str] = None, vecfld_key_added: Optional[str] = 'VecFld_morpho', layer: str = 'X', use_rep: Optional[str] = None, dissimilarity: str = 'kl', max_iter: int = 200, lambdaVF: Union[int, float] = 100.0, beta: Union[int, float] = 0.01, K: Union[int, float] = 15, beta2: Optional[Union[int, float]] = None, beta2_end: Optional[Union[int, float]] = None, sigma2_init: float = 0.1, normalize_c: bool = True, normalize_g: bool = True, dtype: str = 'float32', device: str = 'cpu', inplace: bool = True, verbose: bool = True, nn_init: bool = False, partial_robust_level: float = 25, use_label_prior: bool = False, label_key: Optional[str] = 'cluster', label_transfer_prior: Optional[dict] = None, SVI_mode: bool = True, batch_size: int = 1024, use_sparse: bool = True, pre_compute_dist: bool = False, batch_capacity: int = 1, guidance_pair: Optional[list] = None, guidance_effect: Optional[Union[bool, str]] = False, guidance_epsilon: float = 1) -> Tuple[Optional[Tuple[anndata.AnnData, anndata.AnnData]], numpy.ndarray, numpy.ndarray]