mt_metadata.features.weights.monotonic_weight_kernel
====================================================

.. py:module:: mt_metadata.features.weights.monotonic_weight_kernel


Classes
-------

.. autoapisummary::

   mt_metadata.features.weights.monotonic_weight_kernel.ThresholdEnum
   mt_metadata.features.weights.monotonic_weight_kernel.StyleEnum
   mt_metadata.features.weights.monotonic_weight_kernel.MonotonicWeightKernel


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

.. py:class:: ThresholdEnum

   Bases: :py:obj:`mt_metadata.common.enumerations.StrEnumerationBase`


   str(object='') -> str
   str(bytes_or_buffer[, encoding[, errors]]) -> str

   Create a new string object from the given object. If encoding or
   errors is specified, then the object must expose a data buffer
   that will be decoded using the given encoding and error handler.
   Otherwise, returns the result of object.__str__() (if defined)
   or repr(object).
   encoding defaults to 'utf-8'.
   errors defaults to 'strict'.


   .. py:attribute:: low_cut
      :value: 'low cut'



   .. py:attribute:: high_cut
      :value: 'high cut'



.. py:class:: StyleEnum

   Bases: :py:obj:`mt_metadata.common.enumerations.StrEnumerationBase`


   str(object='') -> str
   str(bytes_or_buffer[, encoding[, errors]]) -> str

   Create a new string object from the given object. If encoding or
   errors is specified, then the object must expose a data buffer
   that will be decoded using the given encoding and error handler.
   Otherwise, returns the result of object.__str__() (if defined)
   or repr(object).
   encoding defaults to 'utf-8'.
   errors defaults to 'strict'.


   .. py:attribute:: taper
      :value: 'taper'



   .. py:attribute:: activation
      :value: 'activation'



.. py:class:: MonotonicWeightKernel(**data)

   Bases: :py:obj:`mt_metadata.features.weights.base.Base`


   MonotonicWeightKernel

   Base class for monotonic weight kernels.
   Handles bounds, normalization, and direction.

   A weighting kernel that applies a monotonic activation/taper function between defined
   lower and upper bounds, based on a given threshold direction.

   There are two main types of monotonic kernels: taper and activation. The taper function
   is used to smoothly transition between the lower and upper bounds over some finite interval,
   while the activation style offers options that asymptote to 0 or 1, such as sigmoid or tanh.
   Thus the activation style supports +/- infinity bounds, while the taper style requires finite bounds.



   .. py:attribute:: threshold
      :type:  Annotated[ThresholdEnum, Field(default=ThresholdEnum.low_cut, description='Which side of a threshold should be downweighted.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['low cut']})]


   .. py:attribute:: style
      :type:  Annotated[StyleEnum, Field(default=StyleEnum.taper, description='Tapering/activation function to use between transition bounds.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['activation']})]


   .. py:attribute:: transition_lower_bound
      :type:  Annotated[float, Field(default=-1000000000.0, description='Start of the taper region (weight begins to change).', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['-inf']})]


   .. py:attribute:: transition_upper_bound
      :type:  Annotated[float, Field(default=1000000000.0, description='End of the taper region (weight finishes changing).', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['+inf']})]