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Commit cd59cdb6 authored by BBQ's avatar BBQ
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upate learner

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feedbackloop.learner_backup/build.gradle
+ 3
− 0
View file @ cd59cdb6
......@@ -17,6 +17,9 @@ dependencies {
compile group: 'org.encog', name: 'encog-core', version: '3.4'
implementation group: 'com.opencsv', name: 'opencsv', version: '4.1'
implementation group: 'commons-io', name: 'commons-io', version: '2.5'
// https://mvnrepository.com/artifact/org.apache.spark/spark-mllib
//runtime group: 'org.apache.spark', name: 'spark-mllib_2.10', version: '1.3.0'
compile group: 'com.sparkjava', name: 'spark-core', version: '2.9.0'
}
run {
......
feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/CsvTransfer.java
+ 8
− 2
View file @ cd59cdb6
......@@ -8,7 +8,13 @@ import com.opencsv.CSVWriter;
public class CsvTransfer {
private static final String CSV_FILE_PATH
public static void main(String[] args){
Learner learner=new Learner();
learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
}
//learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
/**private static final String CSV_FILE_PATH
= "datasets/backup/activity_data_example.csv";
private static final String OUTPUT_FILE_PATH
= "datasets/backup/activity_data.csv";
......@@ -48,7 +54,7 @@ public class CsvTransfer {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}*/
}
feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
+ 27
− 6
View file @ cd59cdb6
......@@ -4,6 +4,7 @@ import java.io.File;
//import com.sun.javafx.tools.packager.Log;
import org.encog.ConsoleStatusReportable;
import org.encog.Encog;
import org.encog.ml.MLClassification;
import org.encog.ml.MLRegression;
import org.encog.ml.data.MLData;
import org.encog.ml.data.versatile.NormalizationHelper;
......@@ -36,7 +37,7 @@ public class Learner {
private EncogModel p_model;
private NormalizationHelper activity_helper;
private NormalizationHelper preference_helper;
private MLRegression a_best_method;
private MLClassification a_best_method;
private MLRegression p_best_method;
private String[] new_data;
private String[] preference_result;
......@@ -67,6 +68,7 @@ public class Learner {
a_model.selectMethod(a_data, MLMethodFactory.TYPE_FEEDFORWARD);
a_data.normalize();
activity_helper = a_data.getNormHelper();
//System.out.println(activity_helper.toString());
}
......@@ -90,7 +92,7 @@ public class Learner {
p_model.setReport(new ConsoleStatusReportable());
p_data.normalize();
preference_helper = p_data.getNormHelper();
System.out.println(preference_helper.toString());
//System.out.println(preference_helper.toString());
}
void train(String activity_url,String preference_url){
......@@ -105,7 +107,7 @@ public class Learner {
activityDataAnalyser(activity_csv_url);
a_model.holdBackValidation(0.3, true, 1001);
a_model.selectTrainingType(a_data);
a_best_method = (MLRegression)a_model.crossvalidate(5, true);
a_best_method = (MLClassification)a_model.crossvalidate(5, true);
System.out.println(a_best_method);
saveEncogModel(save_activity_model_file);
Encog.getInstance().shutdown();
......@@ -115,7 +117,7 @@ public class Learner {
p_model.holdBackValidation(0.3, true, 1001);
p_model.selectTrainingType(p_data);
p_best_method = (MLRegression)p_model.crossvalidate(5, true);
System.out.println(p_best_method);
//System.out.println(p_best_method);
saveEncogModel(save_preference_model_file);
Encog.getInstance().shutdown();
}
......@@ -149,6 +151,7 @@ public class Learner {
activityDataAnalyser("datasets/backup/activity_data.csv");
BasicNetwork activity_method = (BasicNetwork) loadObject(new File(save_activity_model_file));
MLData input = activity_helper.allocateInputVector();
System.out.println("input"+activity_helper);
String[] activity_new_data = new String[12];
activity_new_data[0] = new_data[0];
activity_new_data[1] = new_data[1];
......@@ -163,7 +166,24 @@ public class Learner {
activity_new_data[10] = new_data[10];
activity_new_data[11] = new_data[11];
activity_helper.normalizeInputVector(activity_new_data,input.getData(),false);
//System.out.println("activity output "+activity_method.getLayerOutput(2,1));
//System.out.println("activity output "+activity_method.getLayerOutput(2,0));
// System.out.println("helper" + activity_helper);
/**System.out.println("activity output "+activity_method.getLayerOutput(2,0));
System.out.println("activity output "+activity_method.getLayerOutput(2,1));
System.out.println("activity output "+activity_method.getLayerOutput(2,2));
System.out.println("activity output "+activity_method.getLayerOutput(2,3));
System.out.println("activity output "+activity_method.getLayerOutput(2,4));
System.out.println("activity output "+activity_method.getLayerOutput(2,5));
System.out.println("activity output "+activity_method.getLayerOutput(2,6));
System.out.println("activity output "+activity_method.getLayerOutput(2,10));
System.out.println("activity"+activity_method.getLayerNeuronCount(2));
System.out.println("activity"+activity_method.getLayerNeuronCount(1));
System.out.println("activity count"+activity_method.getOutputCount());
System.out.println(activity_method.getLayerTotalNeuronCount(2));*/
System.out.println("input"+input);
MLData output = activity_method.compute(input);
System.out.println("output:"+output);
activity_result = activity_helper.denormalizeOutputVectorToString(output)[0];
return activity_result;
}
......@@ -173,13 +193,14 @@ public class Learner {
preferenceDataAnalyser("datasets/backup/preference_data.csv");
BasicNetwork preference_method = (BasicNetwork)loadObject(new File(save_preference_model_file));
MLData input = preference_helper.allocateInputVector();
System.out.print("input: "+input);
//
// System.out.print("input: "+input);
String[] preference_new_data = new String[2];
//preference_new_data[0] = activity_result;
preference_new_data[0] = new_data[0];
preference_new_data[1] = new_data[1];
preference_helper.normalizeInputVector(preference_new_data, input.getData(),false);
System.out.println(preference_helper);
//System.out.println(preference_helper);
MLData output = preference_method.compute(input);
preference_result[0] = preference_helper.denormalizeOutputVectorToString(output)[0];
preference_result[1] = preference_helper.denormalizeOutputVectorToString(output)[1];
......
feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java
+ 44
− 34
View file @ cd59cdb6
......@@ -15,29 +15,32 @@ public class Main {
/**
* new data from KB
* */
String[] new_data = new String[13];
new_data[0]="0.10654198";
new_data[1]="8.6574335";
new_data[2]="4.414908";
new_data[3]="0.040269";
new_data[4]="0.516884";
new_data[5]="0.853285";
new_data[6]="1.2066777";
new_data[7]="-1.1444284";
new_data[8]="9.648633";
new_data[9]="1.2207031E-4";
new_data[10]="-0.055358887";
new_data[11]="0.5834961";
new_data[12]="bright";
Learner learner = new Learner();
String[] result =learner.predictor(new_data);
System.out.println(result[0]);
System.out.println(result[1]);
System.out.println(result[2]);
ReaderCSV reader = new ReaderCSV("datasets/backup/activity_data.csv");
reader.updater();
//Learner learner=new Learner();
//learner.preference_train("datasets/backup/preference_data.csv");
//learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
//0.5793968,1.2126632,-4.6244006,-0.030779,0.801127,0.590978,-3.1411927,-0.93373865,-0.31124622,-0.35992432,0.33746338,-0.79608154,dancing
/**String[] new_data = new String[12];
new_data[0]="0.5793968";
new_data[1]="1.2126632";
new_data[2]="-4.6244006";
new_data[3]="-0.030779";
new_data[4]="0.801127";
new_data[5]="0.590978";
new_data[6]="-3.1411927";
new_data[7]="-0.93373865";
new_data[8]="-0.31124622";
new_data[9]="-0.35992432";
new_data[10]="0.33746338";
new_data[11]="-0.79608154";
String result =learner.activity_predictor(new_data);
System.out.println(result);
List k=new ArrayList();
k.add(new_data);
k.add(new_data);*/
//learner.preference_train("datasets/backup/preference_data.csv");
//learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
......@@ -46,21 +49,26 @@ public class Main {
//activity_validation_learner();
//0.10654198,8.6574335,4.414908,0.040269,0.516884,0.853285,1.2066777,-1.1444284,9.648633,1.2207031E-4,-0.055358887,0.5834961 working
/**String[] new_data = new String[12];
new_data[0]="0.10654198";
new_data[1]="8.6574335";
new_data[2]="4.414908";
new_data[3]="0.040269";
new_data[4]="0.516884";
new_data[5]="0.853285";
new_data[6]="1.2066777";
new_data[7]="-1.1444284";
new_data[8]="9.648633";
new_data[9]="1.2207031E-4";
new_data[10]="-0.055358887";
new_data[11]="0.5834961";
String[] new_data = new String[12];
new_data[0]="0.010773907";
new_data[1]="8.610746";
new_data[2]="4.4963107";
new_data[3]="0.047136";
new_data[4]="0.515427";
new_data[5]="0.852877";
new_data[6]="0.9720459";
new_data[7]="-1.3694834";
new_data[8]="9.696517";
new_data[9]="-0.0056152344";
new_data[10]="-0.049438477";
new_data[11]="0.5576782";
//0.010773907,8.610746,4.4963107,0.047136,0.515427,0.852877,0.9720459,-1.3694834,9.696517,-0.0056152344,-0.049438477,0.5576782 working
//0.9999999988939648,-0.9995820798966354,-0.9999999999999997,-0.9999988062118802,-0.9974031940544938,-1.0
//String result =learner.activity_predictor(new_data);
//System.out.println(result);
String[] new_data_1 =new String[12];
/**String[] new_data_1 =new String[12];
//-2.6252422,8.619126,-2.7030537,0.552147,0.5078,0.450302,-8.1881695,-1.2641385,0.038307227,-0.34222412,0.49102783,-0.016540527,walking
new_data_1[0]="-2.6252422";
new_data_1[1]="8.619126";
......@@ -74,6 +82,8 @@ public class Main {
new_data_1[9]="-0.34222412";
new_data_1[10]="0.49102783";
new_data_1[11]="-0.016540527";
String result1 =learner.activity_predictor(new_data_1);
System.out.println(result1);
/**
* learner.train(activity_csv_url, preference_data_url)
* learner.predictor get the result from predictor for new data
......
feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/ReaderCSV.java 0 → 100644
+ 43
− 0
View file @ cd59cdb6
package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
import java.io.FileReader;
import java.io.*;
import com.opencsv.CSVReader;
import org.apache.http.client.fluent.Request;
public class ReaderCSV {
//read every 3 s from csv
private static int TIME_PERIOD = 5000;
private String csv_file_path;
private File file;
private FileReader file_reader;
private CSVReader csv_reader;
private String[] next_record;
private static final String ERASER_ITEM_URI = "http://localhost:4567/model/items/";
public ReaderCSV(String csv_file_path) {
this.csv_file_path=csv_file_path;
}
public void updater(){
file=new File(csv_file_path);
try {
file_reader =new FileReader(file);
csv_reader = new CSVReader(file_reader);
while ((next_record = csv_reader.readNext()) != null) {
//data.add(next_record);
Thread.currentThread().sleep(TIME_PERIOD);
System.out.println("this is a wait" );
for (String value : next_record) {
System.out.print(value + "\t");
//TODO identifier for different items
Request.Put(ERASER_ITEM_URI+"/:itentifider/state" + value);
}
System.out.println();
}
}catch (Exception e){
e.printStackTrace();
}
}
}
venv/pip-selfcheck.json deleted 100644 → 0
+ 0
− 1
View file @ 32c522e8
{"last_check":"2019-05-08T08:33:39Z","pypi_version":"19.1.1"}
\ No newline at end of file
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