bridgescaler.deep
=================

.. py:module:: bridgescaler.deep


Classes
-------

.. autoapisummary::

   bridgescaler.deep.DeepStandardScaler
   bridgescaler.deep.DeepMinMaxScaler
   bridgescaler.deep.DeepQuantileTransformer


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

.. py:class:: DeepStandardScaler

   Bases: :py:obj:`object`


   Calculate standard scaler scores on an arbitrarily dimensional dataset as long as the last dimension is
   the variable dimension.



   .. py:attribute:: mean_
      :value: None



   .. py:attribute:: sd_
      :value: None



   .. py:method:: fit(x)


   .. py:method:: transform(x)


   .. py:method:: fit_transform(x)


   .. py:method:: inverse_transform(x)


.. py:class:: DeepMinMaxScaler

   Bases: :py:obj:`object`


   .. py:attribute:: max_
      :value: None



   .. py:attribute:: min_
      :value: None



   .. py:method:: fit(x)


   .. py:method:: transform(x)


   .. py:method:: fit_transform(x)


   .. py:method:: inverse_transform(x)


.. py:class:: DeepQuantileTransformer(n_quantiles=1000, stochastic=False)

   Bases: :py:obj:`object`


   Performs a quantile transform on N-dimensional arrays where the variable dimension is the last one.

   .. attribute:: n_quantiles

      number of quantiles to calculate and store

   .. attribute:: stochastic

      When transforming to quantile space, whether to take the mean of the left and right interpolation values (False)
      or to pick a random point in between (True).


   .. py:attribute:: n_quantiles
      :value: 1000



   .. py:attribute:: stochastic
      :value: False



   .. py:attribute:: quantiles_
      :value: None



   .. py:attribute:: references_
      :value: None



   .. py:attribute:: fitted_
      :value: False



   .. py:attribute:: x_column_names_
      :value: None



   .. py:method:: fit(x)


   .. py:method:: transform(x)


   .. py:method:: fit_transform(x)


   .. py:method:: inverse_transform(x)


   .. py:method:: _transform_col(x_col, col_index)


   .. py:method:: _inverse_transform_col(x_col, col_index)