.. _`Standard Scaler`:

.. _`org.sysess.sympathy.machinelearning.standard_scaler`:

Standard Scaler
```````````````

.. image:: scaler.svg
   :width: 48



Standardize features by removing the mean and scaling to unit variance.

Centering and scaling happen independently on each feature by
computing the relevant statistics on the samples in the training
set. Mean and standard deviation are then stored to be used on later
data using the transform method.  Standardization of a dataset is a
common requirement for many machine learning estimators: they might
behave badly if the individual feature do not more or less look like
standard normally distributed data (e.g. Gaussian with 0 mean and unit
variance).




Documentation
:::::::::::::

Attributes
==========

    **mean_**
        The mean value for each feature in the training set.
        Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.


    **n_samples_seen_**
        The number of samples processed by the estimator for each feature.
        If there are no missing samples, the ``n_samples_seen`` will be an
        integer, otherwise it will be an array of dtype int. If
        `sample_weights` are used it will be a float (if no missing data)
        or an array of dtype float that sums the weights seen so far.
        Will be reset on new calls to fit, but increments across
        ``partial_fit`` calls.


    **scale_**
        Per feature relative scaling of the data to achieve zero mean and unit
        variance. Generally this is calculated using `np.sqrt(var_)`. If a
        variance is zero, we can't achieve unit variance, and the data is left
        as-is, giving a scaling factor of 1. `scale_` is equal to `None`
        when `with_std=False`.

        .. versionadded:: 0.17
           *scale_*


    **var_**
        The variance for each feature in the training set. Used to compute
        `scale_`. Equal to ``None`` when ``with_mean=False`` and
        ``with_std=False``.


Definition
::::::::::


Output ports
============

    **model**  model
        Model




Configuration
=============

    **Center the data before scaling** (with_mean)
        If True, center the data before scaling.
        This does not work (and will raise an exception) when attempted on
        sparse matrices, because centering them entails building a dense
        matrix which in common use cases is likely to be too large to fit in
        memory.
    **Scale to unit variance** (with_std)
        If True, scale the data to unit variance (or equivalently,
        unit standard deviation).









Implementation
==============

.. automodule:: node_preprocessing
    :noindex:

.. class:: StandardScaler
    :noindex: