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Commit 32c522e8 authored by boqiren's avatar boqiren
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implement interfaces

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influx_test/out/production/resources/log4j2.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/eraser.log"
filePattern="logs/eraser-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="debug">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
</Loggers>
</Configuration>
integration/out/production/resources/openhab2-data.json 0 → 100644
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learner_test/.gitignore 0 → 100644
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/build/
learner_test/build.gradle 0 → 100644
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repositories {
mavenCentral()
}
sourceCompatibility = 1.8
apply plugin: 'java'
apply plugin: 'application'
dependencies {
compile project(':eraser-base')
compile group: 'com.fasterxml.jackson.core', name: 'jackson-databind', version: '2.8.8.1'
compile group: 'org.apache.logging.log4j', name: 'log4j-api', version: '2.10.0'
compile group: 'org.apache.logging.log4j', name: 'log4j-core', version: '2.10.0'
testCompile group: 'junit', name: 'junit', version: '4.12'
testCompile group: 'org.hamcrest', name: 'hamcrest-junit', version: '1.0.0.0'
compile 'org.encog:encog-core:3.4'
}
run {
mainClassName = 'de.tudresden.inf.st.eraser.learner_test.Main'
standardInput = System.in
if (project.hasProperty("appArgs")) {
args Eval.me(appArgs)
}
}
sourceSets {
main {
java {
srcDir 'src/main/java'
}
}
}
learner_test/out/production/resources/log4j2.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/jastadd-mquat.log"
filePattern="logs/jastadd-mquat-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="debug">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
</Loggers>
</Configuration>
learner_test/src/main/java/de/tudresden/inf/st/eraser/learner_test/Main.java 0 → 100644
+ 112
− 0
View file @ 32c522e8
package de.tudresden.inf.st.eraser.learner_test;
import org.encog.Encog;
import org.encog.ml.MLClassification;
import org.encog.ml.data.MLData;
import org.encog.persist.EncogDirectoryPersistence;
import org.encog.util.csv.CSVFormat;
import org.encog.util.csv.ReadCSV;
import org.encog.util.simple.EncogUtility;
import org.encog.ml.data.versatile.NormalizationHelper;
import org.encog.ml.data.versatile.VersatileMLDataSet;
import org.encog.ml.data.versatile.columns.ColumnDefinition;
import org.encog.ml.data.versatile.columns.ColumnType;
import org.encog.ml.data.versatile.sources.VersatileDataSource;
import org.encog.ml.data.versatile.sources.CSVDataSource;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.ml.model.EncogModel;
import org.encog.ConsoleStatusReportable;
import org.encog.ml.MLRegression;
import java.io.File;
import java.util.Arrays;
import static org.encog.persist.EncogDirectoryPersistence.*;
public class Main {
public static void main(String[] args) {
//mapping the data into model
String savefile = "src/main/java/de/tudresden/inf/st/eraser/learner_test/save_model.eg";
String File = "src/main/java/de/tudresden/inf/st/eraser/learner_test/preference_data.csv";
File file = new File(File);
VersatileDataSource source = new CSVDataSource(file, false, CSVFormat.DECIMAL_POINT);
VersatileMLDataSet data = new VersatileMLDataSet(source);
data.defineSourceColumn("monat", 0, ColumnType.continuous);
data.defineSourceColumn("day", 1, ColumnType.continuous);
data.defineSourceColumn("hour", 2, ColumnType.continuous);
data.defineSourceColumn("minute", 3, ColumnType.continuous);
ColumnDefinition outputColumn = data.defineSourceColumn("labels", 4, ColumnType.continuous);
data.defineSingleOutputOthersInput(outputColumn);
data.analyze();
System.out.println("get data ");
EncogModel model = new EncogModel(data);
model.selectMethod(data, MLMethodFactory.TYPE_FEEDFORWARD);
//model.setReport(new ConsoleStatusReportable());
data.normalize();
NormalizationHelper helper = data.getNormHelper();
System.out.println(helper.toString());
model.holdBackValidation(0.3, true, 1001);
model.selectTrainingType(data);
MLRegression bestMethod = (MLRegression)model.crossvalidate(5, true);
MLClassification bestMethodtest=(MLClassification)model.crossvalidate(5,true);
/**System.out.println( "Training error: " + EncogUtility.calculateRegressionError(bestMethod, model.getTrainingDataset()));
System.out.println( "testTraining error: " + EncogUtility.calculateClassificationError(bestMethodtest, model.getTrainingDataset()));
System.out.println( "Validation error: " + EncogUtility.calculateRegressionError(bestMethod, model.getValidationDataset()));
System.out.println( "testValidation error: " + EncogUtility.calculateClassificationError(bestMethodtest, model.getValidationDataset()));
System.out.println(helper.getClass());
System.out.println(helper.toString());
System.out.println("Final model: " + bestMethod);
System.out.println("Final testmodel: " + bestMethodtest);**/
//NormalizationHelper helper = data.getNormHelper();
//test
String helperstr=helper.toString();
String [] split=helperstr.split(";");
String [] finalStr = split[split.length-1].replace("]","").replace("[","").
split(",");
System.out.println(helper);
// save network...
//to delete
saveObject(new File(savefile), bestMethodtest);
ReadCSV csv = new ReadCSV(File, false, CSVFormat.DECIMAL_POINT);
String[] line = new String[4];
MLData input = helper.allocateInputVector();
System.out.println("input test---------------");
System.out.println(input);
while(csv.next()) {
StringBuilder result = new StringBuilder();
line[0] = csv.get(0);
line[1] = csv.get(1);
line[2] = csv.get(2);
line[3] = csv.get(3);
String correct = csv.get(4);
helper.normalizeInputVector(line,input.getData(),false);
MLData output = bestMethod.compute(input);
System.out.println("inputs:");
System.out.println(input);
System.out.println("outputs:");
System.out.println(output);
String brightnessChosen = helper.denormalizeOutputVectorToString(output)[0];
result.append(Arrays.toString(line));
result.append(" -> predicted: ");
result.append(brightnessChosen);
result.append("(correct: ");
result.append(correct);
result.append(")");
System.out.println(result.toString());
break;
}
// Delete data file and shut down.
//File.delete();
Encog.getInstance().shutdown();
/**Training error: 0.299928703107046
testTraining error: 0.9931740614334471
Validation error: 0.41277024952020763
testValidation error: 0.992*/
}
}
learner_test/src/main/java/de/tudresden/inf/st/eraser/learner_test/final_data.csv 0 → 100644
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− 0
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7,20,12,13,2
7,20,14,40,1
7,20,14,40,2
7,21,13,2,2
7,21,13,2,2
7,21,14,23,2
7,21,14,23,2
7,21,15,41,2
7,21,16,54,2
7,21,16,54,2
7,21,17,45,3
7,22,12,28,3
7,22,15,35,2
7,22,15,35,2
7,22,18,59,3
7,22,18,59,3
7,23,12,32,2
7,23,12,32,2
7,23,16,7,2
7,23,16,7,2
7,23,16,7,2
7,23,16,7,2
7,23,16,7,2
7,24,12,4,0
7,24,12,4,0
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7,24,14,38,2
7,24,14,38,2
7,24,18,54,3
7,25,12,31,0
7,25,12,32,1
7,25,12,32,1
7,25,15,6,3
7,25,18,56,3
7,26,13,41,2
7,26,19,14,3
7,27,11,39,2
7,27,11,39,3
7,27,11,46,3
7,27,11,46,2
7,27,13,8,2
7,27,13,8,2
7,27,13,9,2
7,27,13,45,2
7,27,13,45,2
7,27,15,38,3
7,28,12,12,2
7,28,12,13,2
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7,28,12,41,2
7,28,12,41,2
7,28,14,0,1
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7,28,15,21,3
7,28,18,56,3
7,29,10,9,1
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7,29,11,54,0
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7,29,11,54,1
7,29,14,10,2
7,29,16,44,2
7,29,16,44,2
7,30,16,7,3
7,30,18,45,3
7,31,13,2,0
7,31,13,2,1
7,31,13,3,1
7,31,13,3,1
7,31,13,3,1
7,31,18,39,3
8,1,12,22,0
8,1,12,22,1
8,1,14,20,2
8,1,14,20,2
8,1,14,20,2
8,1,15,55,3
8,1,18,31,3
8,1,18,37,3
8,1,18,37,3
8,1,19,2,3
8,1,19,2,3
8,1,20,5,3
8,2,10,9,2
8,2,10,9,1
8,2,10,9,2
8,2,10,9,2
8,2,13,58,2
8,2,13,58,2
8,2,15,44,3
8,2,15,44,3
8,2,15,44,3
8,2,17,21,3
8,2,17,21,3
8,2,17,21,3
8,3,13,31,1
8,3,13,31,2
8,3,13,32,2
8,3,16,43,3
8,4,13,20,1
8,4,13,20,2
8,4,18,27,3
8,5,13,37,2
8,5,13,37,2
8,5,18,33,3
8,6,11,24,3
8,6,11,24,3
8,6,11,24,3
8,6,13,50,3
8,7,13,4,2
8,7,13,4,2
8,7,14,56,3
8,8,12,13,2
8,8,12,13,2
8,8,15,51,2
8,8,15,51,2
8,8,15,51,3
8,9,13,32,2
8,9,13,32,2
8,9,13,32,2
8,9,15,8,2
8,9,15,8,2
8,9,15,8,2
8,9,16,19,2
8,10,11,32,0
8,10,11,32,1
8,10,11,32,1
8,10,13,13,1
8,10,13,13,1
8,10,13,13,2
8,10,16,42,3
8,10,16,42,3
8,11,14,6,2
8,11,14,7,2
8,11,18,54,3
8,11,18,54,3
8,11,18,54,3
8,12,12,27,1
8,12,12,27,1
8,12,12,28,1
8,12,13,53,2
8,12,13,53,2
8,12,13,53,2
8,12,15,21,3
8,13,13,16,1
8,13,13,16,1
8,13,13,16,1
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8,13,17,18,3
8,14,13,7,1
8,14,13,7,1
8,14,13,7,1
8,14,13,7,1
8,14,13,7,2
8,14,13,7,2
8,14,15,6,3
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8,15,14,5,2
8,15,14,6,2
8,15,14,6,2
8,15,16,41,3
8,15,16,41,3
8,15,17,30,3
8,16,13,40,2
8,16,13,40,2
8,16,17,52,3
8,16,17,53,3
8,17,13,34,1
8,17,13,35,2
8,17,14,7,2
8,17,19,2,3
8,18,10,21,3
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8,18,11,14,2
8,18,11,14,2
8,18,11,14,2
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8,20,14,28,2
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8,28,17,40,3
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8,30,13,32,1
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9,6,12,54,1
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9,6,14,21,2
9,6,19,20,3
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9,7,14,57,3
9,7,14,57,3
9,7,16,56,3
9,7,16,56,3
9,7,16,56,3
9,7,16,56,3
9,7,18,38,3
9,7,18,38,3
9,8,11,4,2
9,8,11,4,2
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9,17,18,12,3
9,17,18,13,3
learner_test/src/main/java/de/tudresden/inf/st/eraser/learner_test/save_model.eg 0 → 100644
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encog,BasicNetwork,java,3.4.0,1,1554196571101
[BASIC]
[BASIC:PARAMS]
[BASIC:NETWORK]
beginTraining=0
connectionLimit=0
contextTargetOffset=0,0,0
contextTargetSize=0,0,0
endTraining=2
hasContext=f
inputCount=4
layerCounts=1,8,5
layerFeedCounts=1,7,4
layerContextCount=0,0,0
layerIndex=0,1,9
output=0.2537517424,0.3154675575,-0.8739039638,-0.4408848221,-0.8484433638,-0.999915299,-0.6964984771,-0.208278439,1,0,0,-0.4545454545,0.3559322034,1
outputCount=1
weightIndex=0,8,43
weights=0.5976774048,-0.7925906525,0.7127327881,-0.9611660362,0.8031350986,-0.7286657218,1.0990482817,-0.5985785536,-0.0783115433,0.575612931,1.1267500918,1.7184744034,0.2271044512,-1.0525796764,0.0900869671,1.1492323512,0.6141715555,-1.0455927965,-0.0925453451,0.2471651431,2.3634316872,0.3939369257,0.4607437082,-0.1435186798,0.8428535365,-0.0848896791,-0.070602589,-1.2640263565,2.4899996734,-0.2185394776,10.3421332361,-0.1650898311,-0.2750133571,-0.79680959,-0.8051139953,0.8219933747,-0.0727160299,-0.4609522002,-1.0410685492,-0.5354063412,0.3028724456,-0.6835374219,0.169591233
biasActivation=0,1,1
[BASIC:ACTIVATION]
"org.encog.engine.network.activation.ActivationTANH"
"org.encog.engine.network.activation.ActivationTANH"
"org.encog.engine.network.activation.ActivationLinear"
learner_test/src/main/java/de/tudresden/inf/st/eraser/learner_test/save_model_test.eg 0 → 100644
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− 0
View file @ 32c522e8
encog,BasicNetwork,java,3.4.0,1,1548158734516
[BASIC]
[BASIC:PARAMS]
[BASIC:NETWORK]
beginTraining=0
connectionLimit=0
contextTargetOffset=0,0,0
contextTargetSize=0,0,0
endTraining=2
hasContext=f
inputCount=4
layerCounts=4,8,5
layerFeedCounts=4,7,4
layerContextCount=0,0,0
layerIndex=0,4,12
output=0.6991387348,-0.8711034513,-0.996886038,-0.832747291,-0.0935682806,-0.9996163977,0.5399150265,0.9411173394,-0.5084989975,0.4850010791,0.9999999957,1,0,-0.6666666667,-0.4545454545,0.6949152542,1
outputCount=4
weightIndex=0,32,67
weights=-2.6901880743,0.6512821123,-1.2270002115,1.63124668,0.1982387305,-0.2994789552,1.5833040739,-0.9450411677,2.0541422847,-0.718279397,-1.1761952241,0.5028631512,0.0690323612,-1.496141565,-0.1955149568,-0.7453976822,-0.3691141073,0.9854755554,2.2113850088,-1.5216550292,0.9652087936,-1.3028209693,-1.3346156171,0.4142247818,1.0821207364,0.1987534858,0.6202881884,-0.2940331887,-1.4643282498,2.6960334656,-0.0167663298,-2.9907087565,0.3469960227,-0.0441249736,-2.5998575813,-0.7106361301,-0.8111809962,2.2216158678,-0.5482762437,-1.7996398291,-3.6734127565,-2.9102547958,0.4845401914,0.3760471288,-0.0124987546,0.3784047483,0.5860932613,-0.2682876707,0.7429004186,-7.559247176,-3.4421363532,1.1989747484,-2.3340717496,-1.4740773042,-0.7795788072,-1.8241693655,-0.630132295,-0.8191869009,-0.4060569987,-1.0997423162,-0.5495165849,0.1407829068,-2.2964930412,0.0798893221,-19.5271913755,2.0474187009,-0.2622671892
biasActivation=0,1,1
[BASIC:ACTIVATION]
"org.encog.engine.network.activation.ActivationTANH"
"org.encog.engine.network.activation.ActivationTANH"
"org.encog.engine.network.activation.ActivationLinear"
learner_test/src/main/resources/log4j2.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/jastadd-mquat.log"
filePattern="logs/jastadd-mquat-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="debug">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
</Loggers>
</Configuration>
learner_test/src/test/java/de/tudresden/inf/st/eraser/learner_test/ATest.java 0 → 100644
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package de.tudresden.inf.st.eraser.learner_test;
import org.junit.Test;
import static org.junit.Assert.fail;
/**
* TODO: Add description.
*
* @author rschoene - Initial contribution
*/
public class ATest {
@Test
public void test1() {
fail();
}
}
ml_test/out/production/resources/log4j2.xml 0 → 100644
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− 0
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<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/eraser.log"
filePattern="logs/eraser-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="info">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
</Loggers>
</Configuration>
ml_test_boqi/.gitignore 0 → 100644
+ 1
− 0
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/build/
ml_test_boqi/build.gradle 0 → 100644
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− 0
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repositories {
mavenCentral()
}
sourceCompatibility = 1.8
apply plugin: 'java'
apply plugin: 'application'
dependencies {
compile project(':eraser-base')
compile group: 'com.fasterxml.jackson.core', name: 'jackson-databind', version: '2.9.8'
compile group: 'org.apache.logging.log4j', name: 'log4j-api', version: '2.11.1'
compile group: 'org.apache.logging.log4j', name: 'log4j-core', version: '2.11.1'
testCompile group: 'junit', name: 'junit', version: '4.12'
testCompile group: 'org.hamcrest', name: 'hamcrest-junit', version: '2.0.0.0'
}
run {
mainClassName = 'de.tudresden.inf.st.eraser.ml_test_boqi.Main'
standardInput = System.in
if (project.hasProperty("appArgs")) {
args Eval.me(appArgs)
}
}
sourceSets {
main {
java {
srcDir 'src/main/java'
}
}
}
ml_test_boqi/src/main/java/de/tudresden/inf/st/eraser/ml_test_boqi/Main.java 0 → 100644
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− 0
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package de.tudresden.inf.st.eraser.ml_test_boqi;
import de.tudresden.inf.st.eraser.jastadd.model.*;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.function.Function;
import java.util.stream.Collectors;
import org.apache.commons.math3.stat.StatUtils;
import de.tudresden.inf.st.eraser.jastadd.model.Item;
public class Main {
private static final Logger logger = LogManager.getLogger(Main.class);
public static void main(String[] args) {
logger.info("Hello World!");
createAndTestBrightnessNetwork();
}
private static Root createModel() {
Root model = Root.createEmptyRoot();
Group group = new Group();
group.setID("Group1");
model.addGroup(group);
// inputs items muss normalize 1.0, 0.06666666666666665, 0.4545454545454546, -0.5593220338983051, 1(bias)
NumberItem monthItem = new NumberItem();
monthItem.setState(-1.0);
monthItem.setID("month");
monthItem.setLabel("datetime-month");
NumberItem dayItem = new NumberItem();
dayItem.setState(0.2666666666666666);
dayItem.setID("day");
dayItem.setLabel("datetime-day");
NumberItem hourItem = new NumberItem();
hourItem.setState(-0.6363636363636364);
hourItem.setID("hour");
hourItem.setLabel("datetime-hour");
NumberItem minuteItem = new NumberItem();
minuteItem.setState(-0.5593220338983051);
minuteItem.setID("minute");
minuteItem.setLabel("datetime-minute");
NumberItem biasItem = new NumberItem();
biasItem.setState(1);
biasItem.setID("bias");
biasItem.setLabel("bias");
group.addItem(monthItem);
group.addItem(dayItem);
group.addItem(hourItem);
group.addItem(minuteItem);
group.addItem(biasItem);
return model;
}
private static final int REPETITIONS = 1;
private static void classifyTimed(
NeuralNetworkRoot nn,
Function<NeuralNetworkRoot, DoubleNumber> classify,
Function<DoubleNumber, String> leafToString) {
List<String> results = new ArrayList<>();
List<Long> times = new ArrayList<>();
long before = System.nanoTime();
DoubleNumber classification = classify.apply(nn);
long diff = System.nanoTime() - before;
results.add(leafToString.apply(classification));
times.add(TimeUnit.NANOSECONDS.toMillis(diff));
logger.info("Classification results: {}", results);
logger.info("Took {}ms", String.join("ms, ", times.stream().map(l -> Long.toString(l)).collect(Collectors.toList())));
logger.info("Took on average: {}ms",
Arrays.stream(times.toArray(new Long[0])).mapToLong(l -> l).average().orElse(-1));
logger.info("Took on median: {}ms",
Arrays.stream(times.toArray(new Long[0])).mapToLong(l -> l).sorted()
.skip((REPETITIONS - 1) / 2).limit(2 - REPETITIONS % 2).average().orElse(Double.NaN));
}
/**
* Purpose: Create a neural network with 3 layers (5 + 8 + 4 neurons)
*/
private static void createAndTestBrightnessNetwork() {
Root model = createModel();
Item monthItem = model.resolveItem("month").orElseThrow(
() -> new RuntimeException("Month not found"));
Item dayItem = model.resolveItem("day").orElseThrow(
() -> new RuntimeException("Day not found"));
Item hourItem = model.resolveItem("hour").orElseThrow(
() -> new RuntimeException("Hour not found"));
Item minuteItem = model.resolveItem("minute").orElseThrow(
() -> new RuntimeException("Minute not found"));
Item biasItem = model.resolveItem("bias").orElseThrow(
() -> new RuntimeException("Bias not found"));
NeuralNetworkRoot nn = new NeuralNetworkRoot();
DoubleArrayDoubleFunction sigmoid = inputs -> Math.signum(Arrays.stream(inputs).sum());
DoubleArrayDoubleFunction tanh= inputs ->Math.tanh(Arrays.stream(inputs).sum());
DoubleArrayDoubleFunction function_one= inputs->function_one();
//Weights outputs from learner Module
ArrayList<Double> weights= new ArrayList<Double>(Arrays.asList(
-4.8288886204,0.6723236931,2.1451097188,-0.8551053267,-0.7858304445,4.1369566727,-3.3096691918,
-0.2190980261,2.6871317298,1.2272772167,-2.5292510941,-1.2860407542,-4.2280191541,1.004752063,
0.8345207039,0.0123185817,-0.5921808915,0.0967336988,-0.305892589,0.5572392781,-0.7190098073,
-1.6247354373,0.4589248822,-0.0269816271,2.2208040852,-3.6281085698,0.2204999381,4.7263701556,
-4.8348948698,0.231141867,8.7120706018,-1.4912707741,0.9482851705,0.1377551973,-6.6525856465,
-1.321197315,-2.7369948929,17.664289214,-3.1279212743,-0.8245974167,-1.4251924355,0.8370511414,
2.0841638143,-0.210152817,-1.9414132298,-1.7973688846,-2.1977997794,-3.6046836685,-3.3403186721,
-6.1556924635,-2.8952903587,-1.0773989561,0.2300429028,-0.2184650371,0.0297181797,0.5709092417,
1.3960358442,-3.1577981239,0.0423944625,-17.8143314027,-1.4439317172,-0.5137688896,1.0166045804,
0.3059149818,1.0938282764,0.6203368549,0.702449827));
// input layer
InputNeuron month = new InputNeuron();
month.setItem(monthItem);
InputNeuron day = new InputNeuron();
day.setItem(dayItem);
InputNeuron hour = new InputNeuron();
hour.setItem(hourItem);
InputNeuron minute = new InputNeuron();
minute.setItem(minuteItem);
InputNeuron bias = new InputNeuron();
bias.setItem(biasItem);
nn.addInputNeuron(month);
nn.addInputNeuron(day);
nn.addInputNeuron(hour);
nn.addInputNeuron(minute);
nn.addInputNeuron(bias);
// output layer
OutputLayer outputLayer = new OutputLayer();
OutputNeuron output0 = new OutputNeuron();
output0.setActivationFormula(tanh);
OutputNeuron output1 = new OutputNeuron();
output1.setActivationFormula(tanh);
OutputNeuron output2 = new OutputNeuron();
output2.setActivationFormula(tanh);
OutputNeuron output3 = new OutputNeuron();
output3.setActivationFormula(tanh);
outputLayer.addOutputNeuron(output0);
outputLayer.addOutputNeuron(output1);
outputLayer.addOutputNeuron(output2);
outputLayer.addOutputNeuron(output3);
outputLayer.setCombinator(inputs->predictor(inputs));
nn.setOutputLayer(outputLayer);
// hidden layer
HiddenNeuron[] hiddenNeurons = new HiddenNeuron[8];
for (int i = 0; i < (hiddenNeurons.length); i++) {
if (i==7){
HiddenNeuron hiddenNeuron = new HiddenNeuron();
hiddenNeuron.setActivationFormula(function_one);
hiddenNeurons[i] = hiddenNeuron;
nn.addHiddenNeuron(hiddenNeuron);
bias.connectTo(hiddenNeuron,1.0);
hiddenNeuron.connectTo(output0, weights.get(i));
hiddenNeuron.connectTo(output1, weights.get(i+8));
hiddenNeuron.connectTo(output2, weights.get(i+8*2));
hiddenNeuron.connectTo(output3, weights.get(i+8*3));
}
else{
HiddenNeuron hiddenNeuron = new HiddenNeuron();
hiddenNeuron.setActivationFormula(tanh);
hiddenNeurons[i] = hiddenNeuron;
nn.addHiddenNeuron(hiddenNeuron);
month.connectTo(hiddenNeuron, weights.get((hiddenNeurons.length*4)+i*5));
day.connectTo(hiddenNeuron, weights.get((hiddenNeurons.length*4+1)+i*5));
hour.connectTo(hiddenNeuron, weights.get((hiddenNeurons.length*4+2)+i*5));
minute.connectTo(hiddenNeuron, weights.get((hiddenNeurons.length*4+3)+i*5));
bias.connectTo(hiddenNeuron,weights.get((hiddenNeurons.length*4+4)+i*5));
hiddenNeuron.connectTo(output0, weights.get(i));
hiddenNeuron.connectTo(output1, weights.get(i+8));
hiddenNeuron.connectTo(output2, weights.get(i+8*2));
hiddenNeuron.connectTo(output3, weights.get(i+8*3));}
}
model.getMachineLearningRoot().setPreferenceLearning(nn);
System.out.println(model.prettyPrint());
classifyTimed(nn, NeuralNetworkRoot::classify,
classification -> Double.toString(classification.number));
}
private static double function_one() {
return 1.0;
}
private static double predictor(double[] inputs) {
int index=0;
double maxinput=StatUtils.max(inputs);
System.out.println(inputs);
for (int i = 0; i < inputs.length; i++)
{
if (inputs[i] == maxinput){
index=i;
}
}
//outputs from learner
ArrayList<Double> outputs= new ArrayList<Double>(Arrays.asList(2.0,1.0,3.0,0.0));
double output=outputs.get(index);
return output;
}
}
//inputs:
//[BasicMLData:-1.0,0.2666666666666666,-0.6363636363636364,-0.5593220338983051]
//outputs:
//[BasicMLData:-0.9151867668336432,-0.1568555041251098,-0.9786996639280675,-0.9436628188408074]
//[7, 20, 12, 13] -> predicted: 1(correct: 2)
\ No newline at end of file
ml_test_boqi/src/main/resources/log4j2.xml 0 → 100644
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− 0
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<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/jastadd-mquat.log"
filePattern="logs/jastadd-mquat-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="debug">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
</Loggers>
</Configuration>
ml_test_boqi/src/test/java/de/tudresden/inf/st/eraser/ml_test_boqi/ATest.java 0 → 100644
+ 22
− 0
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package de.tudresden.inf.st.eraser.ml_test_boqi;
import de.tudresden.inf.st.eraser.jastadd.model.*;
import org.junit.Test;
import java.util.Set;
import static org.hamcrest.Matchers.*;
import static org.junit.Assert.fail;
/**
* TODO: Add description.
*
* @author rschoene - Initial contribution
*/
public class ATest {
@Test
public void test1() {
fail();
}
}
openhab-mock/out/production/resources/data1.csv 0 → 100644
+ 3
− 0
View file @ 32c522e8
time,topic,qos,message
0,oh2/out/iris1_item/state,0,1
1,oh2/out/iris1_item/state,0,0
openhab-mock/out/production/resources/log4j2.xml 0 → 100644
+ 25
− 0
View file @ 32c522e8
<?xml version="1.0" encoding="UTF-8"?>
<Configuration>
<Appenders>
<Console name="Console">
<PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
</Console>
<RollingFile name="RollingFile" fileName="logs/eraser.log"
filePattern="logs/eraser-%i.log">
<PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
<Policies>
<OnStartupTriggeringPolicy/>
</Policies>
<DefaultRolloverStrategy max="20"/>
</RollingFile>
</Appenders>
<Loggers>
<Root level="info">
<AppenderRef ref="Console"/>
<AppenderRef ref="RollingFile"/>
</Root>
<Logger name="de.tudresden.inf.st.eraser.openhab2.mqtt" level="DEBUG" additivity="false">
<Appender-ref ref="Console"/>
</Logger>
</Loggers>
</Configuration>
org.openhab.action.machinelearn/out/production/resources/readme.txt 0 → 100644
+ 1
− 0
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Bundle resources go in here!
\ No newline at end of file
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