coco_pipe.decoding.scalers
==========================

.. py:module:: coco_pipe.decoding.scalers


Classes
-------

.. autoapisummary::

   coco_pipe.decoding.scalers.SubjectStandardScaler


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

.. py:class:: SubjectStandardScaler

   Bases: :py:obj:`sklearn.base.BaseEstimator`, :py:obj:`sklearn.base.TransformerMixin`


   Standardization Strategy:
   1. Global standardization across all samples (StandardScaler).
   2. Per-Subject centering (Mean subtraction within each subject).


   .. py:attribute:: global_scaler


   .. py:method:: fit(X, y=None, groups=None)


   .. py:method:: transform(X, groups=None)


   .. py:method:: fit_transform(X, y=None, groups=None)

      Fit the scaler to the data and then transform it.

      :param X: Input data.
      :type X: array-like of shape (n_samples, n_features)
      :param y: Target values (ignored).
      :type y: array-like of shape (n_samples,), default=None
      :param groups: Group labels for per-subject centering.
      :type groups: array-like of shape (n_samples,), default=None

      :returns: **X_scaled** -- Scaled and centered data.
      :rtype: array-like of shape (n_samples, n_features)