SMALLbox

h1. 2012 Hack Session Notes

h2. Running Using SMALLbox Add-ons

_note: this is an example add-on developed by Daniele Barchiese._

These are the steps needed to interface SMALLbox add-ons to the core SMALLbox distribulation:

# Download the add-on distribution from: http://code.soundsoftware.ac.uk/projects/incoherentdl/files
** The version you wish to download is *version 1.1*
** unzip the downloaded file - this will create a folder named incoherentdl-v1_1
# Place the add-on code in any convenient location of your file system
** if you copy it to somewhere outside the SMALLBox folder, you'll need to system, and add it this to your MATLAB path
*** @addpath(genpath('{incoherentdl-v1_1}'))@
** if you place it under the @{SMALLBox Root}/Toolboxes@ folder, it will be added when you run the init script @addpath(genpath('@{incoherentdl-v1_1}'))@
# Modify the relevant files in the folder @{SMALLBox Root}/config/@
** modify or create @{SMALLBox Root}/config/SMALL_learn_config_local.m@
*** @cp {SMALLBox Root}/config/SMALL_learn_local.m {SMALLBox Root}/config/SMALL_learn_config_local.m@
**** this local file is not tracked by Mercurial
**** if you need to revert to the _default_ SMALLbox setting, you just need to delete/rename your local file
*** add the following code to the local config file:
<pre>
elseif strcmpi(DL.toolbox , 'SMALL_incoherentDL')
DL=SMALL_incoherentDL ( Problem , DL ) ;
 % we need to make sure that columns are normalised to % unit lenght .
for i = 1: size(DL.D,2)
DL.D(: ,i)=DL.D(: ,i)/norm(DL.D(: ,i));
end
D = DL.D;
</pre>
# Run the @SMALLboxInit.m@ script to set environmental variables
# Run any function or script contained in the add-on.
** example:
*** @load test_mocod.mat@
**** located in @incoherentdl-v1_1/results/test_mocod.mat@ (but already in path)
*** @open SMALL_test_mocod.m@
**** located in @incoherentdl-v1_1/open SMALL_test_mocod.m@
*** run the code from the block that starts on line 110 (with the comment Plot Results)
**** this will display two coherence/signal to noise ratio plots based on results previously computed