poplar.nn.rescaling
===================

.. py:module:: poplar.nn.rescaling

.. autoapi-nested-parse::

   Rescaling objects to be passed to LinearModel to handle the rescaling of input/output data. 

   These are some standard rescalers to provide something easy to use out of the box; for more complex rescaling behaviour, create your own
   rescaling class using these examples as a template.

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Classes
-------

.. autoapisummary::

   poplar.nn.rescaling.IdentityRescaler
   poplar.nn.rescaling.ZScoreRescaler
   poplar.nn.rescaling.UniformRescaler


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

.. py:class:: IdentityRescaler(yfunctions=None)

   
   A placeholder rescaler that leaves input/output data unchanged. Functions may still be applied to the targets by passing them to yfunctions.


   :Parameters:

       **yfunctions** : list, optional
           A list containing a function and its inverse to apply to the labels prior to rescaling, by default None (i.e. no function is applied)














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.. py:class:: ZScoreRescaler(xdata: torch.Tensor, ydata, yfunctions=None)

   
   Rescales data to the unit normal distribution.


   :Parameters:

       **xdata** : torch.Tensor
           Input data.

       **ydata** : torch.Tensor
           Input labels corresponding to xdata.

       **yfunctions** : list, optional
           A list containing a function and its inverse to apply to the labels prior to rescaling, by default None (i.e. no function is applied)














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.. py:class:: UniformRescaler(xdata, ydata, yfunctions=None)

   
   Rescales data to the uniform distribution with bounds [-1, 1].


   :Parameters:

       **xdata** : torch.Tensor
           Input data.

       **ydata** : torch.Tensor
           Input labels corresponding to xdata.

       **yfunctions** : list, optional
           A list containing a function and its inverse to apply to the labels prior to rescaling, by default None (i.e. no function is applied)














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