natas_hw
/
jidt
mirror of https://github.com/jlizier/jidt
0
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Patching CA demo scripts for full Matlab compatibility, including loading initial state of CAs from file (since octave and matlab generate different initial states)

This commit is contained in:
joseph.lizier 2013-09-10 14:37:28 +00:00
parent 28911ef2f4
commit 6cd0e522fa
3 changed files with 68 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

60
demos/octave/CellularAutomata/TeBook2013.m Executable file
View File

@ -0,0 +1,60 @@
% To recreate the plots in:
% chapter 5 of T. Bossomaier, L. Barnett, M. Harré and J.T. Lizier, "An Introduction to Transfer Entropy: Information Flow in Complex Systems", Springer, to be published, 2013.
% These plots were first published in (see DirectedMeasuresChapterDemo2013.m):
% J. T. Lizier, "Measuring the dynamics of information processing on a local scale in time and space",
% accepted for "Directed Information Measures in Neuroscience", edited by M. Wibral, R. Vicente, J. T. Lizier, to be published by Springer, 2013
clear all;
% Set up simulation options:
cells = 10000;
timeSteps = 600;
neighbourhood = 3;
caStates = 2;
% Set up options for information dynamics analysis, and which segment of the CA to plot
measureParams.k=16;
options.saveImages = true;
options.saveImagesFormat = 'pdf';
options.plotOptions.scaleColoursToSubsetOfPlot = true;
scaleColoursToExtremesDefault = false;
options.plotOptions.scaleColoursToExtremes = scaleColoursToExtremesDefault;
% Turn up the contrast so that the small values aren't disproportionately visible: (0.15, 0.30 was good, except for separable which was better with 0.15, 0.35)
options.plotOptions.scalingMainComponent = 0.15;
options.plotOptions.scalingScdryComponent = 0.30;
options.plotOptions.gammaPower = 0.5;
%%%%%%%%%
% Examining rule 54:
options.plotOptions.plotRows = 35;
options.plotOptions.plotCols = 35;
options.plotOptions.plotStartRow = 20+20;
options.plotOptions.plotStartCol = 1+10;
options.seed = 3; % Set up the random number generator to give reproducible initial states for all measurements
if (exist('initialStates/DirectedMeasuresChapterDemo2013-rule54.txt', 'file'))
% A file specifying the initial state exists -- this
% ensures that Matlab and Octave use the same initial state
% (otherwise only Octave recreates the same initial state used in our chapter).
% (You can delete/move the initial state file if you want them generated from scratch.)
options.initialState = load('initialStates/DirectedMeasuresChapterDemo2013-rule54.txt');
end
fprintf('\nStarting rule 54 ...\n');
fprintf('\nPlotting apparent transfer entropy j = 1 ...\n');
% Use the full red scale for transfer and separable info, since we need to see the extreme negative values properly
options.plotOptions.scaleColoursToExtremes = true;
options.plotOptions.scalingScdryComponent = 0.35;
options.plotOptions.scalingMainComponent = 0.35;
measureParams.j = 1;
plotLocalInfoMeasureForCA(neighbourhood, caStates, 54, cells, timeSteps, 'transfer', measureParams, options);
fprintf('\nPress any key when ready for apparent transfer entropy j = -1 ...\n');
pause
options.plotRawCa = false;
measureParams.j = -1;
plotLocalInfoMeasureForCA(neighbourhood, caStates, 54, cells, timeSteps, 'transfer', measureParams, options);
% If we were going to apply this to another rule:
% fprintf('\nPress any key when ready to apply to the next rule\n')
% pause
options.plotOptions.scaleColoursToExtremes = scaleColoursToExtremesDefault; % return to default value
options.plotOptions.scalingScdryComponent = 0.30; % return to previous value

14
demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m
View File

@ -119,7 +119,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Active information storage
if ((ischar(measureId) && (strcmpi('active', measureId) || strcmpi('all', measureId))) || ...
((measureId == 0) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 0) || (measureId == -1))))
% Compute active information storage
activeCalc = javaObject('infodynamics.measures.discrete.ActiveInformationCalculator', base, measureParams.k);
activeCalc.initialise();
@ -149,7 +149,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Apparent transfer entropy
if ((ischar(measureId) && (strcmpi('transfer', measureId) || strcmpi('all', measureId) || strcmpi('apparenttransfer', measureId))) || ...
((measureId == 1) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 1) || (measureId == -1))))
% Compute apparent transfer entropy
if (measureParams.j == 0)
error('Cannot compute transfer entropy from a cell to itself (setting measureParams.j == 0)');
@ -182,7 +182,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Complete transfer entropy
if ((ischar(measureId) && (strcmpi('transfercomplete', measureId) || strcmpi('completetransfer', measureId) || strcmpi('all', measureId))) || ...
((measureId == 2) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 2) || (measureId == -1))))
% Compute complete transfer entropy
if (measureParams.j == 0)
error('Cannot compute transfer entropy from a cell to itself (setting measureParams.j == 0)');
@ -218,7 +218,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Separable information
if ((ischar(measureId) && (strcmpi('separable', measureId) || strcmpi('all', measureId))) || ...
((measureId == 3) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 3) || (measureId == -1))))
% Compute separable information
separableCalc = javaObject('infodynamics.measures.discrete.SeparableInfoCalculator', ...
base, measureParams.k, neighbourhood - 1);
@ -251,7 +251,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Entropy
if ((ischar(measureId) && (strcmpi('entropy', measureId) || strcmpi('all', measureId))) || ...
((measureId == 4) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 4) || (measureId == -1))))
% Compute entropy
entropyCalc = javaObject('infodynamics.measures.discrete.EntropyCalculator', ...
base);
@ -283,7 +283,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Entropy rate
if ((ischar(measureId) && (strcmpi('entropyrate', measureId) || strcmpi('all', measureId))) || ...
((measureId == 5) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 5) || (measureId == -1))))
% Compute entropy rate
entRateCalc = javaObject('infodynamics.measures.discrete.EntropyRateCalculator', base, measureParams.k);
entRateCalc.initialise();
@ -313,7 +313,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
%============================
% Excess entropy
if ((ischar(measureId) && (strcmpi('excess', measureId) || strcmpi('all', measureId))) || ...
((measureId == 6) || (measureId == -1)))
(not(ischar(measureId)) && ((measureId == 6) || (measureId == -1))))
% Compute excess entropy
excessEntropyCalc = javaObject('infodynamics.measures.discrete.PredictiveInformationCalculator', base, measureParams.k);
excessEntropyCalc.initialise();

2
demos/octave/CellularAutomata/runCA.m
View File

@ -49,7 +49,7 @@ function [caStates, ruleTable, executedRules] = runCA(neighbourhood, base, rule,
else
% User has supplied the initial state for the CA:
fprintf('User has supplied initial state for CA\n');
if (length(seedOrState) != cells)
if (length(seedOrState) ~= cells)
error('Supplied initial ca state vector [seedOrState] is not of length [cells]');
end
ca = seedOrState;