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Minor patch to running of coupledLogisiticMap example

This commit is contained in:
joseph.lizier 2014-11-20 10:58:08 +00:00
parent 7185399680
commit 4345f7da15
2 changed files with 8 additions and 8 deletions
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14
demos/octave/DetectingInteractionLags/coupledLogisticMap.m
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@ -69,7 +69,7 @@ function coupledLogisticMap()
cYToX = 0.2;
cXToY = 0.5;
T = 512;
fprintf('For 1000 repeats, expect the calculations to take ~5 minutes ...\n');
fprintf('For 1000 repeats, expect the calculations to take ~30 seconds ...\n');
repeats = 1000; % General results visible for 100 repeats if you want to see them faster (~20 sec)
k = 1; % history length
@ -136,20 +136,20 @@ function coupledLogisticMap()
% Perform calculation for X -> Y (lag 1)
teCalc.initialise(k,1,1,1,1); % Use history length k (Schreiber k)
teCalc.setProperty('k', KraskovK);
teCalc.setObservations(octaveToJavaDoubleMatrix(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleMatrix(Y(seedSteps:size(Y,1),r)));
teCalc.setObservations(octaveToJavaDoubleArray(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleArray(Y(seedSteps:size(Y,1),r)));
resultsLag1(r) = teCalc.computeAverageLocalOfObservations();
% Perform calculation for X -> Y (lag 2)
teCalc.initialise(k,1,1,1,2); % Use history length k (Schreiber k)
teCalc.setProperty('k', KraskovK);
teCalc.setObservations(octaveToJavaDoubleMatrix(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleMatrix(Y(seedSteps:size(Y,1),r)));
teCalc.setObservations(octaveToJavaDoubleArray(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleArray(Y(seedSteps:size(Y,1),r)));
resultsLag2(r) = teCalc.computeAverageLocalOfObservations();
% Perform calculation for X -> Y (lag 3)
teCalc.initialise(k,1,1,1,3); % Use history length k (Schreiber k)
teCalc.setProperty('k', KraskovK);
teCalc.setObservations(octaveToJavaDoubleMatrix(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleMatrix(Y(seedSteps:size(Y,1),r)));
teCalc.setObservations(octaveToJavaDoubleArray(X(seedSteps:size(X,1),r)), ...
octaveToJavaDoubleArray(Y(seedSteps:size(Y,1),r)));
resultsLag3(r) = teCalc.computeAverageLocalOfObservations();
% Kernel estimator returns the correct ordering of lag 1 and 2 for

2
readme-template.txt
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@ -128,7 +128,7 @@ Notices for this software are found in the notices/JAMA directory.
Release notes
===============
v1.1 14/11/2014 at r573
v1.1 14/11/2014 at r576
-----------------------
Implemented Fast Nearest Neighbour Search for Kraskov-Stögbauer-Grassberger (KSG) estimators for MI, conditional MI, TE, conditional TE, AIS, Predictive info, and multi-information. This includes a general (multivariate) k-d tree implementation;
Added multi-threading (using all available processors by default) for the KSG estimators -- code contributed by Ipek Özdemir;