mirror of https://github.com/jlizier/jidt
merge
This commit is contained in:
David Shorten
commit
b4e32727c9
20
README.md
20
README.md
|
|
@ -14,11 +14,11 @@ JIDT is distributed under the [GNU GPL v3 license](http://www.gnu.org/licenses/g
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# Getting started
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1. [Download](../../wiki/Downloads) and [Installation](../../wiki/Installation) is very easy!
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1. _Quick start_: download the latest [v1.4 full distribution](http://lizier.me/joseph/software/jidt/download.php?file=infodynamics-dist-1.4.zip) (suitable for all platforms) and see the readme.txt file therein.
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1. [Documentation](../../wiki/Documentation) including: the paper describing JIDT at [arXiv:1408.3270](http://arxiv.org/abs/1408.3270) (distributed with the toolkit), a [Tutorial](../../wiki/Tutorial), and [Javadocs (v1.4 here)](http://lizier.me/joseph/software/jidt/javadocs/v1.4/);
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1. [Demos](../../wiki/Demos) are included with the full distribution, including a [GUI app](../../wiki/AutoAnalyser) for automatic analysis and code generation (see picture below), [simple java demos](../../wiki/SimpleJavaExamples) and [cellular automata (CA) demos](../../wiki/CellularAutomataDemos).
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1. These Java tools can easily be used in [Matlab/Octave](../../wiki/OctaveMatlabExamples), [Python](../../wiki/PythonExamples), [R](../../wiki/R_Examples), [Julia](../../wiki/JuliaExamples) and [Clojure](../../wiki/Clojure_Examples)! (click on each language here for examples)
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1. [Download](../../wiki/Downloads) and [Installation](../../wiki/Installation) is very easy!
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1. _Quick start_: take a `git clone` or download the latest [v1.5 full distribution](http://lizier.me/joseph/software/jidt/download.php?file=infodynamics-dist-1.5.zip) (suitable for all platforms) and see the readme.txt file therein.
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1. [Documentation](../../wiki/Documentation) including: the paper describing JIDT at [arXiv:1408.3270](http://arxiv.org/abs/1408.3270) (distributed with the toolkit), a [Tutorial](../../wiki/Tutorial), and [Javadocs (v1.5 here)](http://lizier.me/joseph/software/jidt/javadocs/v1.5/);
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1. [Demos](../../wiki/Demos) are included with the full distribution, including a [GUI app](../../wiki/AutoAnalyser) for automatic analysis and code generation (see picture below), [simple java demos](../../wiki/SimpleJavaExamples) and [cellular automata (CA) demos](../../wiki/CellularAutomataDemos).
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1. These Java tools can easily be used in [Matlab/Octave](../../wiki/OctaveMatlabExamples), [Python](../../wiki/PythonExamples), [R](../../wiki/R_Examples), [Julia](../../wiki/JuliaExamples) and [Clojure](../../wiki/Clojure_Examples)! (click on each language here for examples)
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@ -39,6 +39,16 @@ See other [PublicationsUsingThisToolkit](../../wiki/PublicationsUsingThisToolkit
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# News
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_26/11/2018_ - New jar and full distribution files available for **release v1.5**; Changes for v1.5 include:
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Added GPU (cuda) capability for KSG Conditional Mutual Information calculator (proper documentation to come), brief [wiki page](../../wiki/GPU) and unit tests included;
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Added auto-embedding for TE/AIS with multivariate KSG, and univariate and multivariate Gaussian estimator (plus unit tests), for Ragwitz criteria and Maximum bias-corrected AIS, and also added Maximum bias corrected AIS and TE to handle source embedding as well;
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Kozachenko entropy estimator adds noise to data by default;
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Added bias-correction property to Gaussian and Kernel estimators for MI and conditional MI, including with surrogates (only option for kernel);
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Enabled use of different bases for different variables in MI discrete estimator;
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All new above features enabled in AutoAnalyser;
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Added drop-down menus for parameters in AutoAnalyser;
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Included long-form lecture slides in course folder;
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_26/11/2017_ - New jar and full distribution files available for **release v1.4**; Changes for v1.4 include:
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Major expansion of functionality for AutoAnalysers: adding Launcher applet and capability to double click jar to launch, added Entropy, CMI, CTE and AIS AutoAnalysers, also added binned estimator type, added all variables/pairs analysis, added statistical significance analysis, and ensured functionality of generated Python code with Python3;
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Added GPU (cuda) capability for KSG Mutual Information calculator (proper documentation and wiki page to come), including unit tests;
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16
build.xml
16
build.xml
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@ -5,12 +5,13 @@
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</description>
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<!-- set global properties for this build -->
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<property name="version" value="1.4"/>
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<property name="version" value="1.5"/>
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<property name="mainfilename" value="infodynamics"/>
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<property name="jarplainname" value="${mainfilename}.jar" />
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<property name="jarversiondistnamezip" value="${mainfilename}-jar-${version}.zip" />
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<property name="distname" value="${mainfilename}-dist-${version}" />
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<property name="distnamezip" value="${distname}.zip" />
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<property name="distnametargz" value="${distname}.tar.gz" />
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<property name="src" location="java/source"/>
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<property name="cudasrc" location="cuda"/>
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<property name="bin" location="bin"/>
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@ -148,6 +149,7 @@
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<delete file="demos/clojure/project.clj"/>
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<delete file="${jarversiondistnamezip}"/>
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<delete file="${distnamezip}"/>
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<delete file="${distnametargz}"/>
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<delete file="${jarplainname}"/>
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<delete>
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<fileset dir="demos/AutoAnalyser" includes="GeneratedCalculator.*"/>
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@ -293,24 +295,30 @@
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</zip>
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</target>
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<!-- Developer build - builds everything and makes the full distribution file -->
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<!-- Developer build - builds everything and makes the full distribution file in zip and tar.gz -->
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<target name="dist" depends="jar,junit,javadocs,readmefiles" description="developer: generate the full distribution">
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<echo message="${ant.project.name}: ${ant.file}"/>
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<zip destfile="${distnamezip}">
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<fileset file="build.xml"/>
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<fileset file="${jarplainname}"/>
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<zipfileset file="${jarplainname}" filemode="755"/>
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<fileset file="license-gplv3.txt"/>
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<fileset file="readme.txt"/>
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<fileset file="InfoDynamicsToolkit.pdf"/>
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<fileset file="JIDT-logo.png" erroronmissingdir="false"/> <!-- This file is missing in full repository versions -->
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<fileset file="${versionfile}"/>
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<zipfileset dir="java" includes="**/*.java" prefix="java"/>
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<zipfileset dir="demos" includes="**/*.*,**/*" excludes="clojure/deploy,clojure/deploy/*.*,python/*.pyc" prefix="demos"/>
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<zipfileset dir="demos" includes="**/*.*,**/*" excludes="clojure/deploy,clojure/deploy/*.*,python/*.pyc,**/*.sh,**/*.bat" prefix="demos"/>
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<zipfileset dir="demos" includes="**/*.sh,**/*.bat" prefix="demos" filemode="755"/> <!-- Do these separately to get executable permissions -->
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<zipfileset dir="javadocs" includes="**/*.*,**/*" prefix="javadocs"/>
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<zipfileset dir="notices" includes="**/*.*,**/*" prefix="notices"/>
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<zipfileset dir="cuda" prefix="cuda" excludes="benchmark.sh"/>
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<zipfileset dir="cuda" prefix="cuda" includes="benchmark.sh" filemode="755"/> <!-- Do this separately to get executable permissions -->
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<zipfileset dir="course" prefix="course"/>
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<zipfileset dir="tutorial" prefix="tutorial"/> <!-- Get rid of this when tutorial is subsumed in course... -->
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<zipfileset dir="web" includes="JIDT-logo.png" prefix="" erroronmissingdir="false"/> <!-- This file is missing in zip dist versions -->
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</zip>
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<tar destfile="${distnametargz}" compression="gzip" longfile="posix"> <!-- for longfiles could also use "gnu" but apparently is slightly less widely supported -->
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<zipfileset src="${distnamezip}"/>
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</tar>
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</target>
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</project>
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Binary file not shown.
Binary file not shown.
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserAIS
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserAIS
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserCMI
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserCMI
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserCTE
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserCTE
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserEntropy
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserEntropy
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserMI
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserMI
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@ -1,5 +0,0 @@
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@ECHO OFF
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REM Run the example:
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java -classpath "..\..\infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserTE
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@ -1,5 +0,0 @@
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#!/bin/bash
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# Run the example:
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java -classpath "../../infodynamics.jar" infodynamics.demos.autoanalysis.AutoAnalyserTE
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Binary file not shown.
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@ -67,9 +67,10 @@ public class Example10GPUBenchmark {
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miCalc.setProperty("k", "4");
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miCalc.initialise(src[0].length, tgt[0].length);
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miCalc.setObservations(src, tgt);
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int nb_surrogates = 150;
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double[] timeAndValue = new double[2];
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long startTime = System.nanoTime();
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timeAndValue[1] = miCalc.computeAverageLocalOfObservations();
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timeAndValue[1] = miCalc.computeSignificance(nb_surrogates).actualValue;
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timeAndValue[0] = (System.nanoTime() - startTime)/1000000.0;
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return timeAndValue;
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}
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|
|
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Binary file not shown.
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@ -717,9 +717,10 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov
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System.load(gpuLibraryPath);
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}
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} catch (Throwable e) {
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String errmsg = "GPU library not found. To compile GPU code set the enablegpu flag to true in build.xml";
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String errmsg = "GPU library not found. To compile GPU code set the enablegpu flag to true in build.xml, or run `ant gpu jar`.";
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errmsg += "\nFor more information see the JIDT GPU wiki page: https://github.com/jlizier/jidt/wiki/GPU";
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if (gpuLibraryPath.length() > 0) {
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errmsg += "\nGPU library was not found in the path provided. Provide full path including library file name.";
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errmsg += "\n\nGPU library was not found in the path provided. Provide full path including library file name.";
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errmsg += "\nExample: /home/johndoe/myfolder/libKraskov.so";
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}
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throw new Exception(errmsg);
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|
|
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@ -282,68 +282,11 @@ public interface TransferEntropyCalculatorSpiking {
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* will vary depending on the underlying implementation
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*/
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public SpikingLocalInformationValues computeLocalOfPreviousObservations() throws Exception;
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/**
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* Generate a bootstrapped distribution of what the TE would look like,
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* under a null hypothesis that the source values of our
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* samples had no temporal relation to the destination value.
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*
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* <p>See Section II.E "Statistical significance testing" of
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* the JIDT paper below for a description of how this is done for MI and TE in general.
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* </p>
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*
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* <p>Note that if several disjoint time-series have been added
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* as observations using {@link #addObservations(double[])} etc.,
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* then these separate "trials" will be mixed up in the generation
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* of surrogates here.</p>
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*
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* <p>This method (in contrast to {@link #computeSignificance(int[][])})
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* creates <i>random</i> shufflings of the source embedding vectors for the surrogate
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* calculations.</p>
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*
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* @param numPermutationsToCheck number of surrogate samples to bootstrap
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* to generate the distribution.
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* @return the distribution of TE scores under this null hypothesis.
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* @see "J.T. Lizier, 'JIDT: An information-theoretic
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* toolkit for studying the dynamics of complex systems', 2014."
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*/
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public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck) throws Exception;
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|
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/**
|
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* Generate a bootstrapped distribution of what the TE would look like,
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* under a null hypothesis that the source values of our
|
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* samples had no relation to the destination value.
|
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*
|
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* <p>See Section II.E "Statistical significance testing" of
|
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* the JIDT paper below for a description of how this is done for MI and TE.
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* </p>
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*
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* <p>Note that if several disjoint time-series have been added
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* as observations using {@link #addObservations(double[])} etc.,
|
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* then these separate "trials" will be mixed up in the generation
|
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* of surrogates here.</p>
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*
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* <p>This method (in contrast to {@link #computeSignificance(int)})
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* allows the user to specify how to construct the surrogates,
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* such that repeatable results may be obtained.</p>
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*
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* @param newOrderings a specification of how to shuffle the source embedding vectors
|
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* between all of our samples to create the surrogates to generate the distribution with. The first
|
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* index is the permutation number (i.e. newOrderings.length is the number
|
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* of surrogate samples we use to bootstrap to generate the distribution here.)
|
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* Each array newOrderings[i] should be an array of length N (where
|
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* would be the value returned by {@link #getNumObservations()}),
|
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* containing a permutation of the values in 0..(N-1).
|
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* TODO Need to think this through a little more before implementing.
|
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* @return the distribution of channel measure scores under this null hypothesis.
|
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* @see "J.T. Lizier, 'JIDT: An information-theoretic
|
||||
* toolkit for studying the dynamics of complex systems', 2014."
|
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* @throws Exception where the length of each permutation in newOrderings
|
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* is not equal to the number N samples that were previously supplied.
|
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*/
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public EmpiricalMeasurementDistribution computeSignificance(
|
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int[][] newOrderings) throws Exception;
|
||||
|
||||
|
||||
public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck, double estimatedValue) throws Exception;
|
||||
|
||||
public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck,
|
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double estimatedValue, long randomSeed) throws Exception;
|
||||
/**
|
||||
* Set or clear debug mode for extra debug printing to stdout
|
||||
*
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -723,33 +723,33 @@ public class TransferEntropyCalculatorSpikingIntegrationWindows2 implements
|
|||
|
||||
//kdTreeDestNext.setNormType(3);
|
||||
|
||||
nnPQ =
|
||||
kdTreeDestNext.findKNearestNeighbours(
|
||||
Knns, eventIndexWithinType);
|
||||
// nnPQ =
|
||||
// kdTreeDestNext.findKNearestNeighbours(
|
||||
// Knns, eventIndexWithinType);
|
||||
|
||||
// Find eps_{x,y,z} as the maximum x, y and z norms amongst this set:
|
||||
radius_sourcePast = 0.0;
|
||||
radius_destPast = 0.0;
|
||||
radius_destNext = 0.0;
|
||||
radius_destNext_sampleIndex = -1;
|
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radius_max = 0;
|
||||
for (int j = 0; j < Knns; j++) {
|
||||
// Take the furthest remaining of the nearest neighbours from the PQ:
|
||||
NeighbourNodeData nnData = nnPQ.poll();
|
||||
/*if (nnData.norms[0] > radius_sourcePast) {
|
||||
radius_sourcePast = nnData.norms[0];
|
||||
}*/
|
||||
if (nnData.norms[0] > radius_destPast) {
|
||||
radius_destPast = nnData.norms[0];
|
||||
}
|
||||
if (nnData.norms[1] > radius_destNext) {
|
||||
radius_destNext = nnData.norms[1];
|
||||
radius_destNext_sampleIndex = nnData.sampleIndex;
|
||||
}
|
||||
if(nnData.distance > radius_max) {
|
||||
radius_max = nnData.distance;
|
||||
}
|
||||
}
|
||||
// // Find eps_{x,y,z} as the maximum x, y and z norms amongst this set:
|
||||
// radius_sourcePast = 0.0;
|
||||
// radius_destPast = 0.0;
|
||||
// radius_destNext = 0.0;
|
||||
// radius_destNext_sampleIndex = -1;
|
||||
// radius_max = 0.0;
|
||||
// for (int j = 0; j < Knns; j++) {
|
||||
// // Take the furthest remaining of the nearest neighbours from the PQ:
|
||||
// NeighbourNodeData nnData = nnPQ.poll();
|
||||
// /*if (nnData.norms[0] > radius_sourcePast) {
|
||||
// radius_sourcePast = nnData.norms[0];
|
||||
// }*/
|
||||
// if (nnData.norms[0] > radius_destPast) {
|
||||
// radius_destPast = nnData.norms[0];
|
||||
// }
|
||||
// if (nnData.norms[1] > radius_destNext) {
|
||||
// radius_destNext = nnData.norms[1];
|
||||
// radius_destNext_sampleIndex = nnData.sampleIndex;
|
||||
// }
|
||||
// if(nnData.distance > radius_max) {
|
||||
// radius_max = nnData.distance;
|
||||
// }
|
||||
// }
|
||||
|
||||
//radius_max = Math.max(radius_destPast, radius_destNext);
|
||||
|
||||
|
|
@ -834,8 +834,8 @@ public class TransferEntropyCalculatorSpikingIntegrationWindows2 implements
|
|||
double rawRateGivenSourceAndDest = (double) Knns / timeInWindowWithMatchingJointHistories;
|
||||
double rawRateGivenDest = (double) countOfDestNextMatched / timeInWindowWithMatchingDestHistory;
|
||||
// Attempt at bias correction:
|
||||
double logRateGivenSourceAndDestCorrected = -Math.log(timeInWindowWithMatchingJointHistories);
|
||||
double logRateGivenDestCorrected = -Math.log(timeInWindowWithMatchingDestHistory);
|
||||
double logRateGivenSourceAndDestCorrected = MathsUtils.digamma(Knns) - Math.log(timeInWindowWithMatchingJointHistories);
|
||||
double logRateGivenDestCorrected = MathsUtils.digamma(countOfDestNextMatched) - Math.log(timeInWindowWithMatchingDestHistory);
|
||||
//System.out.println(logRateGivenSourceAndDestCorrected + " " + logRateGivenDestCorrected);
|
||||
//============================
|
||||
|
||||
|
|
|
|||
|
|
@ -275,10 +275,10 @@ public class KdTree extends NearestNeighbourSearcher {
|
|||
// Could remove these since the code is now functional,
|
||||
// but may be better to leave them in just in case the code breaks:
|
||||
if (leftIndex > leftStart + leftNumPoints) {
|
||||
throw new RuntimeException("Exceeded expected number of points on left");
|
||||
throw new RuntimeException("Exceeded expected number of points on left - likely had an NaN in the data");
|
||||
}
|
||||
if (rightIndex > rightStart + rightNumPoints) {
|
||||
throw new RuntimeException("Exceeded expected number of points on right");
|
||||
throw new RuntimeException("Exceeded expected number of points on right - likely had an NaN in the data");
|
||||
}
|
||||
// Update the pointer for the sorted indices for this dimension,
|
||||
// and keep the new temporary array
|
||||
|
|
@ -1384,19 +1384,19 @@ public class KdTree extends NearestNeighbourSearcher {
|
|||
}
|
||||
|
||||
@Override
|
||||
public void findPointsWithinR(int sampleIndex,
|
||||
public int findPointsWithinR(int sampleIndex,
|
||||
double r, boolean allowEqualToR, boolean[] isWithinR,
|
||||
int[] indicesWithinR) {
|
||||
if (rootNode == null) {
|
||||
indicesWithinR[0] = -1;
|
||||
return;
|
||||
return 0;
|
||||
}
|
||||
int currentIndexInIndicesWithinR =
|
||||
findPointsWithinR(sampleIndex,
|
||||
rootNode, 0, r, allowEqualToR, isWithinR,
|
||||
indicesWithinR, 0);
|
||||
indicesWithinR[currentIndexInIndicesWithinR] = -1;
|
||||
return;
|
||||
return currentIndexInIndicesWithinR;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -2510,20 +2510,21 @@ public class KdTree extends NearestNeighbourSearcher {
|
|||
* @param allowEqualToR if true, then count points at radius r also, otherwise only those strictly within r
|
||||
* @param isWithinR the array MUST be passed in with all points set to false initially, and is returned indicating whether each sample was found to be within r of that at sampleIndex.
|
||||
* @param indicesWithinR a list of array indices for points marked as true in isWithinR, terminated with a -1 value.
|
||||
* @return the number of points found to match
|
||||
*/
|
||||
public void findPointsWithinRs(int sampleIndex,
|
||||
public int findPointsWithinRs(int sampleIndex,
|
||||
double[] rs, int dynCorrExclTime, boolean allowEqualToR,
|
||||
boolean[] isWithinR, int[] indicesWithinR) {
|
||||
if (rootNode == null) {
|
||||
indicesWithinR[0] = -1;
|
||||
return;
|
||||
return 0;
|
||||
}
|
||||
int currentIndexInIndicesWithinR =
|
||||
findPointsWithinRs(sampleIndex,
|
||||
rootNode, 0, rs, dynCorrExclTime, allowEqualToR, isWithinR,
|
||||
indicesWithinR, 0);
|
||||
indicesWithinR[currentIndexInIndicesWithinR] = -1;
|
||||
return;
|
||||
return currentIndexInIndicesWithinR;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
|
|
@ -326,8 +326,9 @@ public abstract class NearestNeighbourSearcher {
|
|||
* found to be within r of that at sampleIndex.
|
||||
* @param indicesWithinR a list of array indices
|
||||
* for points marked as true in isWithinR, terminated with a -1 value.
|
||||
* @return the number of matching points found
|
||||
*/
|
||||
public abstract void findPointsWithinR(
|
||||
public abstract int findPointsWithinR(
|
||||
int sampleIndex, double r,
|
||||
boolean allowEqualToR, boolean[] isWithinR, int[] indicesWithinR);
|
||||
|
||||
|
|
|
|||
|
|
@ -685,7 +685,7 @@ public class UnivariateNearestNeighbourSearcher extends NearestNeighbourSearcher
|
|||
* </p>
|
||||
*
|
||||
*/
|
||||
public void findPointsWithinR(
|
||||
public int findPointsWithinR(
|
||||
int sampleIndex, double r, boolean allowEqualToR,
|
||||
boolean[] isWithinR, int[] indicesWithinR) {
|
||||
int indexInIndicesWithinR = 0;
|
||||
|
|
@ -718,7 +718,8 @@ public class UnivariateNearestNeighbourSearcher extends NearestNeighbourSearcher
|
|||
break;
|
||||
}
|
||||
// Write the terminating integer into the indicesWithinR array:
|
||||
indicesWithinR[indexInIndicesWithinR++] = -1;
|
||||
indicesWithinR[indexInIndicesWithinR] = -1;
|
||||
return indexInIndicesWithinR;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
|
|
@ -86,14 +86,14 @@ public class GPUPerformanceTester extends TestCase {
|
|||
startTime = Calendar.getInstance().getTimeInMillis();
|
||||
miCalc.initialise(1,1);
|
||||
miCalc.setObservations(source, dest);
|
||||
cpu_val = miCalc.computeSignificance(nb_surrogates).getMeanOfDistribution();
|
||||
cpu_val = miCalc.computeSignificance(nb_surrogates).actualValue;
|
||||
cpu_duration = Calendar.getInstance().getTimeInMillis() - startTime;
|
||||
|
||||
miCalc.setProperty("USE_GPU", "true");
|
||||
miCalc.initialise(1,1);
|
||||
startTime = Calendar.getInstance().getTimeInMillis();
|
||||
miCalc.setObservations(source, dest);
|
||||
gpu_val = miCalc.computeSignificance(nb_surrogates).getMeanOfDistribution();
|
||||
gpu_val = miCalc.computeSignificance(nb_surrogates).actualValue;
|
||||
gpu_duration = Calendar.getInstance().getTimeInMillis() - startTime;
|
||||
|
||||
assertEquals(cpu_val, gpu_val, 0.0001);
|
||||
|
|
@ -124,12 +124,12 @@ public class GPUPerformanceTester extends TestCase {
|
|||
return;
|
||||
}
|
||||
|
||||
int timeSteps = 10000;
|
||||
int timeSteps = 1000;
|
||||
RandomGenerator rg = new RandomGenerator();
|
||||
double[] source = rg.generateNormalData(timeSteps, 0, 1);
|
||||
double[] dest = rg.generateNormalData(timeSteps, 0, 1);
|
||||
|
||||
compareGPUPerformance(source, dest, "Random low-dimensional data");
|
||||
compareGPUPerformanceSurrogates(source, dest, 150, "Random low-dimensional data");
|
||||
|
||||
return;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -134,6 +134,18 @@ Notices for this software are found in the notices/JAMA directory.
|
|||
Release notes
|
||||
===============
|
||||
|
||||
v1.5 26/11/2018
|
||||
---------------
|
||||
(after 753 commits recorded by github, repository as at https://github.com/jlizier/jidt/tree/603445651cc0bf155a42c9ba336141bc7f29bccd save for this file update)
|
||||
Added GPU (cuda) capability for KSG Conditional Mutual Information calculator (proper documentation to come), including unit tests and brief wiki page;
|
||||
Added auto-embedding for TE/AIS with multivariate KSG, and univariate and multivariate Gaussian estimator (plus unit tests), for Ragwitz criteria and Maximum bias-corrected AIS, and also added Maximum bias corrected AIS and TE to handle source embedding as well;
|
||||
Kozachenko entropy estimator adds noise to data by default;
|
||||
Added bias-correction property to Gaussian and Kernel estimators for MI and conditional MI, including with surrogates (only option for kernel);
|
||||
Enabled use of different bases for different variables in MI discrete estimator;
|
||||
All new above features enabled in AutoAnalyser;
|
||||
Added drop-down menus for parameters in AutoAnalyser;
|
||||
Included long-form lecture slides in course folder;
|
||||
|
||||
v1.4 26/11/2017
|
||||
---------------
|
||||
(after 638 commits recorded by github, repository as at https://github.com/jlizier/jidt/tree/589d51674e6a9cfb569432679e515bea17092876 save for this file update)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,186 @@
|
|||
##
|
||||
## Java Information Dynamics Toolkit (JIDT)
|
||||
## Copyright (C) 2012, Joseph T. Lizier
|
||||
##
|
||||
## This program 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.
|
||||
##
|
||||
## This program 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 this program. If not, see <http://www.gnu.org/licenses/>.
|
||||
##
|
||||
|
||||
# = Example 4 - Transfer entropy on continuous data using Kraskov estimators =
|
||||
|
||||
# Simple transfer entropy (TE) calculation on continuous-valued data using the Kraskov-estimator TE calculator.
|
||||
|
||||
from jpype import *
|
||||
import random
|
||||
import math
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
|
||||
NUM_REPS = 2
|
||||
NUM_SPIKES = int(2e3)
|
||||
NUM_OBSERVATIONS = 2
|
||||
NUM_SURROGATES = 10
|
||||
|
||||
# Params for canonical example generation
|
||||
RATE_Y = 1.0
|
||||
RATE_X_MAX = 10
|
||||
|
||||
def generate_canonical_example_processes(num_y_events):
|
||||
event_train_x = []
|
||||
event_train_x.append(0)
|
||||
|
||||
event_train_y = np.random.uniform(0, int(num_y_events / RATE_Y), int(num_y_events))
|
||||
event_train_y.sort()
|
||||
|
||||
most_recent_y_index = 0
|
||||
previous_x_candidate = 0
|
||||
while most_recent_y_index < (len(event_train_y) - 1):
|
||||
|
||||
this_x_candidate = previous_x_candidate + random.expovariate(RATE_X_MAX)
|
||||
|
||||
while most_recent_y_index < (len(event_train_y) - 1) and this_x_candidate > event_train_y[most_recent_y_index + 1]:
|
||||
most_recent_y_index += 1
|
||||
|
||||
delta_t = this_x_candidate - event_train_y[most_recent_y_index]
|
||||
|
||||
rate = 0
|
||||
|
||||
if delta_t > 1:
|
||||
rate = 0.5
|
||||
else:
|
||||
rate = 0.5 + 5.0 * math.exp(-50 * (delta_t - 0.5)**2) - 5.0 * math.exp(-50 * (0.5)**2)
|
||||
if random.random() < rate/float(RATE_X_MAX):
|
||||
event_train_x.append(this_x_candidate)
|
||||
previous_x_candidate = this_x_candidate
|
||||
|
||||
event_train_x.sort()
|
||||
event_train_y.sort()
|
||||
|
||||
return event_train_x, event_train_y
|
||||
|
||||
# Change location of jar to match yours (we assume script is called from demos/python):
|
||||
jarLocation = os.path.join(os.getcwd(), "infodynamics.jar");
|
||||
if (not(os.path.isfile(jarLocation))):
|
||||
exit("infodynamics.jar not found (expected at " + os.path.abspath(jarLocation) + ") - are you running from demos/python?")
|
||||
# Start the JVM (add the "-Xmx" option with say 1024M if you get crashes due to not enough memory space)
|
||||
startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)
|
||||
teCalcClass = JPackage("infodynamics.measures.spiking.integration").TransferEntropyCalculatorSpikingIntegration
|
||||
|
||||
|
||||
teCalc = teCalcClass()
|
||||
teCalc.setProperty("knns", "4")
|
||||
print("Independent Poisson Processes")
|
||||
teCalc.setProperty("DEST_PAST_INTERVALS", "1,2")
|
||||
teCalc.setProperty("SOURCE_PAST_INTERVALS", "1,2")
|
||||
teCalc.appendConditionalIntervals(JArray(JInt, 1)([1, 2]))
|
||||
teCalc.appendConditionalIntervals(JArray(JInt, 1)([1, 2]))
|
||||
teCalc.setProperty("NORM_TYPE", "MAX_NORM")
|
||||
|
||||
results_poisson = np.zeros(NUM_REPS)
|
||||
for i in range(NUM_REPS):
|
||||
teCalc.startAddObservations()
|
||||
for j in range(NUM_OBSERVATIONS):
|
||||
sourceArray = NUM_SPIKES*np.random.random(NUM_SPIKES)
|
||||
sourceArray.sort()
|
||||
destArray = NUM_SPIKES*np.random.random(NUM_SPIKES)
|
||||
destArray.sort()
|
||||
condArray = NUM_SPIKES*np.random.random((2, NUM_SPIKES))
|
||||
condArray.sort(axis = 1)
|
||||
teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray), JArray(JDouble, 2)(condArray))
|
||||
teCalc.finaliseAddObservations();
|
||||
result = teCalc.computeAverageLocalOfObservations()
|
||||
print("TE result %.4f nats" % (result,))
|
||||
sig = teCalc.computeSignificance(NUM_SURROGATES, result)
|
||||
print(sig.pValue)
|
||||
results_poisson[i] = result
|
||||
print("Summary: mean ", np.mean(results_poisson), " std dev ", np.std(results_poisson))
|
||||
|
||||
teCalc = teCalcClass()
|
||||
teCalc.setProperty("knns", "4")
|
||||
print("Noisy copy zero TE")
|
||||
#teCalc.appendConditionalIntervals(JArray(JInt, 1)([1]))
|
||||
teCalc.setProperty("DEST_PAST_INTERVALS", "1")
|
||||
teCalc.setProperty("SOURCE_PAST_INTERVALS", "1")
|
||||
#teCalc.setProperty("NORM_TYPE", "MAX_NORM")
|
||||
|
||||
results_noisy_zero = np.zeros(NUM_REPS)
|
||||
for i in range(NUM_REPS):
|
||||
teCalc.startAddObservations()
|
||||
for j in range(NUM_OBSERVATIONS):
|
||||
condArray = np.ones((1, NUM_SPIKES)) + 0.05 * np.random.random((1, NUM_SPIKES))
|
||||
condArray = np.cumsum(condArray, axis = 1)
|
||||
condArray.sort(axis = 1)
|
||||
sourceArray = condArray[0, :] + 0.25 + 0.05 * np.random.normal(size = condArray.shape[1])
|
||||
sourceArray.sort()
|
||||
destArray = condArray[0, :] + 0.5 + 0.05 * np.random.normal(size = condArray.shape[1])
|
||||
destArray.sort()
|
||||
#teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray), JArray(JDouble, 2)(condArray))
|
||||
teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray))
|
||||
teCalc.finaliseAddObservations();
|
||||
result = teCalc.computeAverageLocalOfObservations()
|
||||
print("TE result %.4f nats" % (result,))
|
||||
sig = teCalc.computeSignificance(NUM_SURROGATES, result)
|
||||
print(sig.pValue)
|
||||
results_noisy_zero[i] = result
|
||||
print("Summary: mean ", np.mean(results_noisy_zero), " std dev ", np.std(results_noisy_zero))
|
||||
|
||||
|
||||
|
||||
teCalc = teCalcClass()
|
||||
teCalc.setProperty("knns", "4")
|
||||
print("Noisy copy non-zero TE")
|
||||
teCalc.appendConditionalIntervals(JArray(JInt, 1)([1]))
|
||||
teCalc.setProperty("DEST_PAST_INTERVALS", "1,2")
|
||||
teCalc.setProperty("SOURCE_PAST_INTERVALS", "1")
|
||||
#teCalc.setProperty("NORM_TYPE", "MAX_NORM")
|
||||
|
||||
results_noisy_non_zero = np.zeros(NUM_REPS)
|
||||
for i in range(NUM_REPS):
|
||||
teCalc.startAddObservations()
|
||||
for j in range(NUM_OBSERVATIONS):
|
||||
sourceArray = np.ones((1, NUM_SPIKES)) + 0.05 * np.random.random((1, NUM_SPIKES))
|
||||
sourceArray = np.cumsum(sourceArray)
|
||||
sourceArray.sort()
|
||||
condArray = sourceArray + 0.25 + 0.05 * np.random.normal(size = sourceArray.shape)
|
||||
condArray.sort()
|
||||
condArray = np.expand_dims(condArray, 0)
|
||||
destArray = sourceArray + 0.5 + 0.05 * np.random.normal(size = sourceArray.shape)
|
||||
destArray.sort()
|
||||
teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray), JArray(JDouble, 2)(condArray))
|
||||
teCalc.finaliseAddObservations();
|
||||
result = teCalc.computeAverageLocalOfObservations()
|
||||
print("TE result %.4f nats" % (result,))
|
||||
sig = teCalc.computeSignificance(NUM_SURROGATES, result)
|
||||
print(sig.pValue)
|
||||
results_noisy_non_zero[i] = result
|
||||
print("Summary: mean ", np.mean(results_noisy_non_zero), " std dev ", np.std(results_noisy_zero))
|
||||
|
||||
print("Canonical example")
|
||||
teCalc = teCalcClass()
|
||||
teCalc.setProperty("knns", "4")
|
||||
teCalc.setProperty("DEST_PAST_INTERVALS", "1,2")
|
||||
teCalc.setProperty("SOURCE_PAST_INTERVALS", "1")
|
||||
#teCalc.setProperty("NUM_SAMPLES_MULTIPLIER", "1")
|
||||
#teCalc.setProperty("NORM_TYPE", "MAX_NORM")
|
||||
|
||||
results_canonical = np.zeros(NUM_REPS)
|
||||
for i in range(NUM_REPS):
|
||||
event_train_x, event_train_y = generate_canonical_example_processes(NUM_SPIKES)
|
||||
teCalc.setObservations(JArray(JDouble, 1)(event_train_y), JArray(JDouble, 1)(event_train_x))
|
||||
result = teCalc.computeAverageLocalOfObservations()
|
||||
results_canonical[i] = result
|
||||
print("TE result %.4f nats" % (result,))
|
||||
sig = teCalc.computeSignificance(NUM_SURROGATES, result)
|
||||
print(sig.pValue)
|
||||
print("Summary: mean ", np.mean(results_canonical), " std dev ", np.std(results_canonical))
|
||||
Binary file not shown.
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Reference in New Issue