leaspy.models.logistic
======================

.. py:module:: leaspy.models.logistic


Classes
-------

.. autoapisummary::

   leaspy.models.logistic.LogisticInitializationMixin
   leaspy.models.logistic.LogisticModel


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

.. py:class:: LogisticInitializationMixin

.. py:class:: LogisticModel(name, **kwargs)

   Bases: :py:obj:`LogisticInitializationMixin`, :py:obj:`leaspy.models.riemanian_manifold.RiemanianManifoldModel`


   
   Manifold model for multiple variables of interest (logistic formulation).
















   ..
       !! processed by numpydoc !!

   .. py:method:: get_variables_specs()

      
      Return the specifications of the variables (latent variables, derived variables,
      model 'parameters') that are part of the model.





      :Returns:

          NamedVariables
              A dictionary-like object mapping variable names to their specifications.











      ..
          !! processed by numpydoc !!


   .. py:method:: metric(*, g)
      :staticmethod:


      
      Compute the metric tensor from input tensor `g`.
      This function calculates the metric as \((g + 1)^2 / g\) element-wise.


      :Parameters:

          **g** : t :class:`torch.Tensor`
              Input tensor with values of the population parameter `g` for each feature.



      :Returns:

          :class:`torch.Tensor`
              The computed metric tensor, same shape as g(number of features)











      ..
          !! processed by numpydoc !!


   .. py:method:: model_with_sources(*, rt, space_shifts, metric, v0, g)
      :classmethod:


      
      Return the model output when sources(spatial components) are present.


      :Parameters:

          **rt** : TensorOrWeightedTensor[float]
              Tensor containing the reparametrized time.

          **space_shifts** : TensorOrWeightedTensor[float]
              Tensor containing the values of the space-shifts

          **metric** : TensorOrWeightedTensor[float]
              Tensor containing the metric tensor used for computing the spatial/temporal influence.

          **v0** : TensorOrWeightedTensor[float]
              Tensor containing the values of the population parameter `v0` for each feature.

          **g** : TensorOrWeightedTensor[float]
              Tensor containing the values of the population parameter `g` for each feature.



      :Returns:

          :class:`torch.Tensor`
              Weighted value tensor after applying sigmoid transformation,
              representing the model output with sources.











      ..
          !! processed by numpydoc !!