## The module "mat2groups" of the Mastrave modelling library

Daniele de Rigo

#### Copyright and license notice of the function mat2groups

Copyright © 2005,2006,2007,2008,2009,2010,2011 Daniele de Rigo

The file mat2groups.m is part of Mastrave.

Mastrave is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Mastrave is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with Mastrave. If not, see http://www.gnu.org/licenses/.

#### Function declaration

groups = mat2groups( matrix                   ,
groups_length            ,
mode          = '--none' )



#### Description

Returns a sparse matrix groups whose nonzeros elements are the elements of the input argument matrix . groups has an amount of columns equal to the number of elements of the vector of non-negative integers groups_length . Each column of groups has an amount of rows equal to the corresponding element of groups_length . Depending on the value of mode , the elements of each groups column may be in the same order of the corresponding elements of matrix (default behaviour), or may be ordered in ascending or descending order.

#### Input arguments


matrix            ::numeric::
Matrix of numbers.

groups_length     ::natural::
Vector containing the desired length of each group in
which  matrix  has to be splitted.

mode              ::string::
Direction in which each column of the cell-array  groups
will be sorted. Valid modes are:

mode     │      meaning
──────────────┼───────────────────────────────────────
'none'      │ Leave the elements of  groups  in
'--none'    │ the same order of those of  matrix :
(default)   │ nonzeros( groups ) ==  matrix (:) .
──────────────┼───────────────────────────────────────
'ascend'    │ Sort in  ascending order.
'--ascend'  │
──────────────┼───────────────────────────────────────
'descend'   │ Sort in descending order.
'--descend' │



#### Example of usage


% Basic conversion
n_elems = [2 0 3 4 0 0 5 3 ]
data    = randn( 1, sum(n_elems) )
sp_data = mat2groups( data, n_elems );
full( sp_data )
all( nonzeros(sp_data) == data(:) )

% Adding sort for no-empty elements
sp_data = mat2groups( data, n_elems, '--descend' );
full( sp_data )
sp_data = mat2groups( data, n_elems, '--ascend' );
full( sp_data )

% Difference between  mat2groups  and  score
[rev_id, sorted_data, sort_id] = score( sp_data , '--ascend' );
full( sorted_data )


See also:
mat2multi, multi2mat

Keywords:
matrices, sparse matrices

Version: 0.5.1

#### Support

The Mastrave modelling library is committed to provide reusable and general - but also robust and scalable - modules for research modellers dealing with computational science.  You can help the Mastrave project by providing feedbacks on unexpected behaviours of this module.  Despite all efforts, all of us - either developers or users - (should) know that errors are unavoidable.  However, the free software paradigm successfully highlights that scientific knowledge freedom also implies an impressive opportunity for collectively evolve the tools and ideas upon which our daily work is based.  Reporting a problem that you found using Mastrave may help the developer team to find a possible bug.  Please, be aware that Mastrave is entirely based on voluntary efforts: in order for your help to be as effective as possible, please read carefully the section on reporting problems.  Thank you for your collaboration.

Copyright (C) 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016 Daniele de Rigo

This page is licensed under a Creative Commons Attribution-NoDerivs 3.0 Italy License.

This document is also part of the book:
de Rigo, D. (2012). Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modelling. http://mastrave.org/doc/MTV-1.012-1