model.boolean_and
=================

.. py:module:: model.boolean_and


Attributes
----------

.. autoapisummary::

   model.boolean_and.feature


Classes
-------

.. autoapisummary::

   model.boolean_and.BooleanAndNN


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

.. py:class:: BooleanAndNN(n_features: int = 2)

   Bases: :py:obj:`torch.nn.Sequential`


   Implements a neural network model designed to mimic the 'AND' logical operation on input features.

   When only -1 or 1 are passed in as part of the input to this model, it is effectively performing the
   'AND' operation.

   This model is structured as a sequence of layers that progressively compute the 'AND' operation
   on the input features, scaling the dimensionality of the input at each step until the final
   output is obtained. The network employs a specific arrangement of weights and intermediate operations
   such that it is also equivalent to computing the minimum value among a collection of values given as
   as the input.

   Inherits from:
       torch.nn.Sequential: Parent class for implementing neural networks with modules defined in a
           sequential manner.

   .. attribute:: dims

      A list that keeps track of the dimensions of each layer in the network.

      :type: list

   Initializes an AND model with the specified input features dimension.

   :param n_features: The dimension (number of features) of the input data. Defaults to 2.
   :type n_features: int


   .. py:attribute:: n_features
      :value: 2



   .. py:method:: _create_layer_weights() -> Tuple

      Initializes the layers of the network starting from the input dimension.

      :param in_dim: The dimension of the input to the current layer.
      :type in_dim: int

      :returns: A list of initialized layers that make up the network.
      :rtype: list



.. py:data:: feature
   :value: 3