The module "score" of the Mastrave modelling library

Copyright and license notice of the function score

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

The file score.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

[score_idx, sorted_X, sorted_idx] = score( X               ,
                                           dim  = 1        ,
                                           mode = 'ascend' )

Description

Utility for specializing

[sorted_X, sorted_idx] = sort( X , dim , mode )

score(X) returns as first element score_idx a vector representing the ranking of the elements of X when they are considered as scores. Whether X scores are to be ranked in ascending (default) or descending order, is decided with the input argument mode . If X is a vector vec , the relation between score_idx and sorted_idx is:

[score_idx, sorted_vec, sorted_idx] = score( vec );
vec( sorted_idx ) == sorted_vec
sorted_vec( score_idx ) == vec

If X is a matrix M the relation between score_idx and sorted_idx is:

siz = size(M);
[r, c] = ind2sub( siz, 1:numel(M) );
[score_idx, sorted_M, sorted_idx] = score(M);
M( sub2ind( siz, sorted_idx, c ) ) == sorted_M
sorted_M( sub2ind( siz, score_idx, c ) ) == M

If X is a sparse matrix sM , score_idx and sorted_idx are related to the nonzeros elements of sM :
[score_idx, sorted_sM, sorted_idx] = score(sM);
[r1, c1, nz] = find(sM);
[r2, c2, snz] = find(sorted_sM);
sid = nonzeros(sorted_idx);
scr = nonzeros(score_idx );
sorted_sM == sparse( r2, c2, nz(sid) )

Input arguments

 X                 ::sortable::
                   Numeric vector o matrix, or string, or cell-array
                   of strings.

 dim               ::scalar_natural_nonzero::
                   Dimension along which to sort  X .
                   If omitted, default is 1.

 mode              ::string::
                   Direction in which  X  will be sorted.  If omitted,
                   default is 'ascend'.  Valid modes are:

                        mode     │      meaning
                   ──────────────┼────────────────────────────
                     'ascend'    │ Sort in  ascending order.
                     '--ascend'  │
                   ──────────────┼────────────────────────────
                     'descend'   │ Sort in descending order.
                     '--descend' │

Example of usage

   % Basic usage.
   % Passing a column vector:
   A = zeros(5,1); A(:) = randperm(numel(A))*10
   [revid, sA, sid]   = score( A )
   A2    =  A;
   A2(:) = sA( revid );
   all( A(:) == A2(:) )

   % Passing a row vector:
   A = zeros(1,5); A(:) = randperm(numel(A))*10
   [revid, sA, sid]   = score( A )
   A2    =  A;
   A2(:) = sA( revid );
   all( A(:) == A2(:) )

   % Passing a matrix:
   A = zeros(3,4); A(:) = randperm(numel(A))*10

   [revid, sA, sid]     = score( A )
   [r, c]               = ind2sub( size(A), 1:numel(A) );
   A2    =  A;
   A2(:) = sA( sub2ind( size(A), revid(:), c(:) ) );
   all( A(:) == A2(:) )

   % Using the  dim  argument:
   [revid, sA, sid]     = score( A , 2 )
   [r, c]               = ind2sub( size(A), 1:numel(A) );
   A2    =  A;
   A2(:) = sA( sub2ind( size(A), r(:), revid(:) ) );
   all( A(:) == A2(:) )

   % Passing a 3-dimensional array:
   A = zeros(3,4,2); A(:) = ceil(rand(1,numel(A))*numel(A))*10
   [revid, sA, sid]       = score( A , 2 )
   [rr{1:3}]              = ind2sub( size(A), 1:numel(A) );
   A2    =  A;
   A2(:) = sA( sub2ind( size(A), rr{1}(:), revid(:), rr{3}(:) ) );
   all( A(:) == A2(:) )

   % Passing a multidimensional cell array
   cA = mat2cell( sprintf('%03d',A(:)), 1, ones(1,numel(A))*3 );
   cA = reshape( cA, size(A) )
   [revcid, scA, scid]    = score( cA , 2 )
   all( revcid(:) == revid(:) )
   all(   scid(:) ==   sid(:) )
   disp('  A   cA  |  sA  scA');
   for i=1:numel(A)
      fprintf( 1, '%3d ''%s'' | %3d ''%s''\n', A(i), cA{i}, sA(i), scA{i} );
   end

See also:
   tf_score



Keywords:
   ranking



Version: 0.9.2

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