The module "cummean" of the Mastrave modelling library

Daniele de Rigo

The file cummean.m is part of Mastrave.

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Function declaration

answer = cummean( values       ,
dim    = []  ,
mode   = 'a' )



Description

Utility to extend the function mean(.) providing cumulative means of the elements of the array values along a given dimension dim .

Input arguments


values            ::numeric::
Vector, matrix or multi-dimensional array of numbers.

dim               ::scalar_index|empty::
Scalar positive integer representing the dimension along
which the cumulative means have to be computed.
If  dim  is an empty array  [] , the dimension is the
first non-singleton dimension.  In case  values  is a
vector, this definition means that the default
dimension is the one along which the elements of the
vector  values  are aligned.
If omitted, the default value is  [].

mode              ::string::
String declaring the kind of mean to be computed.
If omitted, its default value is: 'a'.
Valid modes are:

mode        │      meaning
─────────────────┼──────────────────────────────────
'a'            │ Compute the ordinary arithmetic
'--arithmetic' │ mean.
─────────────────┼──────────────────────────────────
'g'            │ Compute the geometric mean.
'--geometric'  │
─────────────────┼──────────────────────────────────
'h'            │ Compute the harmonic mean.
'--harmonic'   │



Example of usage


% Basic usage

% Vectors:
m  = ceil( rand(1,7)* 100 )
cm = cummean( m )
assert( cm(end) == mean(m) )

% Matrices:
m  = ceil( rand(5,7)* 100 )
cm = cummean( m )
assert( cm(end,:) == mean(m) )

% Passing a custom dimension
cm = cummean( m , 2 )
assert( cm(:,end) == mean(m,2) )

% Dealing with multi-dimensional arrays
m  = ceil( rand(5,7,3)* 100 )
cm = cummean( m )
cm = cummean( m , [] )
assert( cm(end,:,:) == mean(m) )

cm = cummean( m , 3 )
assert( cm(:,:,end) == mean(m,3) )

% Passing the kind of mean to be computed
cm = cummean( m , [], '--arithmetic' )
cm = cummean( m , [], 'a' )
assert( cm(end,:,:) == mean(m) )

% Geometric mean
cm = cummean( m , [], '--geometric' )
cm = cummean( m , [], 'g' )
assert( cm(end,:,:) == prod(m,1).^(1/size(m,1)) )
cm = cummean( m , 2, 'g' )
assert( cm(:,end,:) == prod(m,2).^(1/size(m,2)) )

% Harmonic mean
cm = cummean( m , [], '--harmonic' )
cm = cummean( m , [], 'h' )
assert( cm(end,:,:) == size(m,1)./sum(1./m,1) )
cm = cummean( m , 3, '--harmonic' )
assert( cm(:,:,end) == size(m,3)./sum(1./m,3) )


Memory requirements:
O( numel(  values  ) )

cumstd, cumvar, cumsumsq, groupfun

Keywords:
cumulative operators, scan

Version: 0.4.3

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