neural_network.perceptron
=========================

.. py:module:: neural_network.perceptron

.. autoapi-nested-parse::

   Perceptron
   w = w + N * (d(k) - y) * x(k)

   Using perceptron network for oil analysis, with Measuring of 3 parameters
   that represent chemical characteristics we can classify the oil, in p1 or p2
   p1 = -1
   p2 = 1

   Reference: https://en.wikipedia.org/wiki/Perceptron



Attributes
----------

.. autoapisummary::

   neural_network.perceptron.network
   neural_network.perceptron.samples
   neural_network.perceptron.target


Classes
-------

.. autoapisummary::

   neural_network.perceptron.Perceptron


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

.. py:class:: Perceptron(sample: list[list[float]], target: list[int], learning_rate: float = 0.01, epoch_number: int = 1000, bias: float = -1, seed: int | None = 0)

   .. py:method:: sign(u: float) -> int

      threshold function for classification
      :param u: input number
      :return: 1 if the input is greater than or equal to 0, otherwise -1
      >>> data = [[0], [-0.5], [0.5]]
      >>> targets = [1, -1, 1]
      >>> perceptron = Perceptron(data, targets)
      >>> perceptron.sign(0)
      1
      >>> perceptron.sign(-0.5)
      -1
      >>> perceptron.sign(0.5)
      1



   .. py:method:: sort(sample: list[float]) -> int

      Classifies a single observation as P1 (-1) or P2 (1). The network must
      be trained first.

      :param sample: example row to classify as P1 or P2
      :return: -1 if the sample is classified as P1, otherwise 1

      >>> data = [[2.0149, 0.6192, 10.9263]]
      >>> targets = [-1]
      >>> perceptron = Perceptron(data, targets)
      >>> perceptron.training()
      5
      >>> perceptron.sort([2.0149, 0.6192, 10.9263])
      -1



   .. py:method:: training() -> int

      Trains the perceptron until it stops misclassifying the training data
      or the maximum number of epochs (``epoch_number``) is reached, whichever
      comes first. The epoch cap guarantees termination even if the data is
      not linearly separable.

      :return: the number of epochs the network was trained for.

      >>> data = [[2.0149, 0.6192, 10.9263]]
      >>> targets = [-1]
      >>> perceptron = Perceptron(data, targets)
      >>> perceptron.training()
      5



   .. py:attribute:: _rng


   .. py:attribute:: bias
      :value: -1



   .. py:attribute:: col_sample


   .. py:attribute:: epoch_number
      :value: 1000



   .. py:attribute:: learning_rate
      :value: 0.01



   .. py:attribute:: number_sample


   .. py:attribute:: sample


   .. py:attribute:: target


   .. py:attribute:: weight
      :type:  list
      :value: []



.. py:data:: network

.. py:data:: samples

.. py:data:: target