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.. default-domain:: cpp

.. _alg_basic_statistics:

================
Basic Statistics
================

.. include::  ../../../includes/statistics/basic-statistics-introduction.rst

------------------------
Mathematical formulation
------------------------

.. _basic_statistics_c_math:

Computing
---------

Given a set :math:`X` of :math:`n` :math:`p`-dimensional feature vectors 
:math:`x_1 = (x_{11}, \ldots, x_{1p}), \ldots, x_n = (x_{n1}, \ldots, x_{np})`, 
the problem is to compute the following sample characteristics for each feature in the data set:

.. list-table::
   :widths: 20 60
   :header-rows: 1
   :align: left

   * - Statistic
     - Definition
   * - Minimum
     - :math:`min(j) = \smash{\displaystyle \min_i } \{x_{ij}\}`
   * - Maximum
     - :math:`max(j) = \smash{\displaystyle \max_i } \{x_{ij}\}`
   * - Sum
     - :math:`s(j) = \sum_i x_{ij}`
   * - Sum of squares
     - :math:`s_2(j) = \sum_i x_{ij}^2`
   * - Means
     - :math:`m(j) = \frac {s(j)} {n}`
   * - Second order raw moment
     - :math:`a_2(j) = \frac {s_2(j)} {n}`
   * - Sum of squared difference from the means
     - :math:`\text{SDM}(j) = \sum_i (x_{ij} - m(j))^2`
   * - Variance
     - :math:`k_2(j) = \frac {\text{SDM}(j) } {n - 1}`
   * - Standard deviation
     - :math:`\text{stdev}(j) = \sqrt {k_2(j)}`
   * - Variation coefficient
     - :math:`V(j) = \frac {\text{stdev}(j)} {m(j)}`

.. _basic_statistics_c_math_dense:

Computation method: *dense*
---------------------------
The method computes the basic statistics for each feature in the data set.

---------------------
Programming Interface
---------------------

Refer to :ref:`API Reference: Basic statistics <api_basic_statistics>`.

----------------
Distributed mode
----------------

The algorithm supports distributed execution in SMPD mode (only on GPU).