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testAllNetworksQueue.java
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testAllNetworksQueue.java
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import java.io.IOException;
/*
* Hand Writing Recognition and Simple CAPTCHA Neural Network
* CS 3425 Final project
* Spring 2014
* Min "Ivy" Xing, Zackery Leman
*
* This class creates multiple instances of each network type and allows tests to be run one after another and have the results neatly collected in output files in subfolders.
* Make sure to pass the argument of 3 GB minimum of Ram to the JVM, in order to run properly.
* 1. First unzip the NeuralNetOutput.zip in a directory.
* 2. Change "/Users/zackeryleman/Desktop" to be the file path of the NeuralNetOutput folder.
* 3. Change the NUMBER_OF_CORES to 24 or 8 in each neural net class, depending on your machine. The processors are required to share memory and thus must be run on the same machine.
*/
public class testAllNetworksQueue {
public static void main(String[] args) throws IOException, ClassNotFoundException {
//These are for demonstration purposes:--------------------------------
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/Results";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/TrainedSetOutputWeights.txt";
String filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/TrainedSetHiddenWeights.txt";
//Runs a FF on CAPTCHAS
// Need to set NUMBER_OF_OUTPUT_NODES=36 in the FF neural net class for this to work.
//NeuralNet secondFF4 = new NeuralNet(30,1000,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);
// 30 Hidden Nodes, learning rate 0.3, binary input, training 5 epochs on the full data set
FeedForward FFdemo = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/KNNResults";
KNearestNeighbors kNNDemo = new KNearestNeighbors(3,false,5,1000,filePathResults);
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/Demo/TrainedRBFSetOutputWeights.txt";
RadialBasisFunction RBFa = new RadialBasisFunction(20,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction RBFb = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction RBFc = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
//-------------------------------------------------------------
//These are all tests--------------------------------
//runFFHiddenNodeEx(); //Exp #1
//runFFLearningRateEx(); //Exp #2
//runFFTrainingExampleEx(); //Exp #3
//runRBFSigmaEx(); //Exp #4
//runRBFLearningRateEx(); //Exp #5
//runRBFTrainingExamplesEx(); //Exp #6
//runKNNKEx();// Exp 7
//runKNNTrainingExamplesEx(); //Exp 8
//This class will then read the individual human readable output files and summarize the data in a way that makes it easier to graph.
//This does not work with the Demo
}
/*
* Runs Experiment #1
*/
public static void runFFHiddenNodeEx() throws IOException, ClassNotFoundException{
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/15/Results";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/15/TrainedSetOutputWeights.txt";
String filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/15/TrainedSetHiddenWeights.txt";
// 15 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on the full data set
FeedForward firstFF1 = new FeedForward(15,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward firstFF2 = new FeedForward(15,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward firstFF3 = new FeedForward(15,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward firstFF4 = new FeedForward(15,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
firstFF1=null;
firstFF2=null;
firstFF3=null;
firstFF4=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on the full data set
FeedForward firstFF1A = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward firstFF2A = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward firstFF3A = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward firstFF4A = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
firstFF1A=null;
firstFF2A=null;
firstFF3A=null;
firstFF4A=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/50/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/50/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/50/TrainedSetHiddenWeights.txt";
// 50 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on the full data set
FeedForward firstFF1AA = new FeedForward(50,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward firstFF2AA = new FeedForward(50,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward firstFF22AA = new FeedForward(50,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward firstFF3AA = new FeedForward(50,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
firstFF1AA=null;
firstFF2AA=null;
firstFF22AA=null;
firstFF3AA=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/100/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/100/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/100/TrainedSetHiddenWeights.txt";
// 100 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on the full data set
FeedForward FF1B = new FeedForward(100,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward FF2B = new FeedForward(100,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward FF3B = new FeedForward(100,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward FF4B = new FeedForward(100,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
FF1B=null;
FF2B=null;
FF3B=null;
FF4B=null;
}
/*
* Runs Experiment #2
*/
public static void runFFLearningRateEx() throws IOException, ClassNotFoundException{
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.1/Results";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.1/TrainedSetOutputWeights.txt";
String filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.1/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.1, binary input, continue training 5 epochs at a time on the full data set
FeedForward secondFF1 = new FeedForward(30,5,0.1,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward secondFF2 = new FeedForward(30,5,0.1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward secondFF3 = new FeedForward(30,5,0.1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward secondFF4 = new FeedForward(30,5,0.1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
secondFF1=null;
secondFF2=null;
secondFF3=null;
secondFF4=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.2/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.2/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.2/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.2, binary input, continue training 5 epochs at a time on the full data set
FeedForward secondFF1B = new FeedForward(30,5,0.2,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward secondFF2B = new FeedForward(30,5,0.2,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward secondFF3B = new FeedForward(30,5,0.2,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward secondFF4B = new FeedForward(30,5,0.2,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
secondFF1B=null;
secondFF2B=null;
secondFF3B=null;
secondFF4B=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.5/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.5/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.5/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.5, binary input, continue training 5 epochs at a time on the full data set
FeedForward secondFF1C = new FeedForward(30,5,0.5,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward secondFF2C = new FeedForward(30,5,0.5,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward secondFF3C = new FeedForward(30,5,0.5,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward secondFF4C = new FeedForward(30,5,0.5,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
secondFF1C=null;
secondFF2C=null;
secondFF3C=null;
secondFF4C=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.7/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.7/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/0.7/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.7, binary input, continue training 5 epochs at a time on the full data set
FeedForward secondFF1D = new FeedForward(30,5,0.7,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward secondFF2D = new FeedForward(30,5,0.7,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward secondFF3D = new FeedForward(30,5,0.7,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward secondFF4D = new FeedForward(30,5,0.7,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
secondFF1D=null;
secondFF2D=null;
secondFF3D=null;
secondFF4D=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 1, binary input, continue training 5 epochs at a time on the full data set
FeedForward secondFF1E = new FeedForward(30,5,1,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 1-5
FeedForward secondFF2E = new FeedForward(30,5,1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 6-10
FeedForward secondFF3E = new FeedForward(30,5,1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 11-15
FeedForward secondFF4E = new FeedForward(30,5,1,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1);//epochs 16-20
secondFF1E=null;
secondFF2E=null;
secondFF3E=null;
secondFF4E=null;
}
/*
* Runs Experiment #3
*/
public static void runFFTrainingExampleEx() throws IOException, ClassNotFoundException{
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1t/Results";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1t/TrainedSetOutputWeights.txt";
String filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/1t/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on a 1000th of the data set
FeedForward secondFF1 = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1000);//epochs 1-5
FeedForward secondFF2 = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1000);//epochs 6-10
FeedForward secondFF3 = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1000);//epochs 11-15
FeedForward secondFF4 = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,1000);//epochs 16-20
secondFF1=null;
secondFF2=null;
secondFF3=null;
secondFF4=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/2t/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/2t/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/2t/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on a half of the data set
FeedForward secondFF1B = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,2);//epochs 1-5
FeedForward secondFF2B = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,2);//epochs 6-10
FeedForward secondFF3B = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,2);//epochs 11-15
FeedForward secondFF4B = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,2);//epochs 16-20
secondFF1B=null;
secondFF2B=null;
secondFF3B=null;
secondFF4B=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/10t/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/10t/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/10t/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.3, binary input, continue training 5 epochs at a time on a tenth of the data set
FeedForward secondFF1C = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,10);//epochs 1-5
FeedForward secondFF2C = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,10);//epochs 6-10
FeedForward secondFF3C = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,10);//epochs 11-15
FeedForward secondFF4C = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,10);//epochs 16-20
secondFF1C=null;
secondFF2C=null;
secondFF3C=null;
secondFF4C=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30t/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30t/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/30t/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 0.7, binary input, continue training 5 epochs at a time on a 30th of the data set
FeedForward secondFF1D = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,30);//epochs 1-5
FeedForward secondFF2D = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,30);//epochs 6-10
FeedForward secondFF3D = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,30);//epochs 11-15
FeedForward secondFF4D = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,30);//epochs 16-20
secondFF1D=null;
secondFF2D=null;
secondFF3D=null;
secondFF4D=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/60t/Results";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/60t/TrainedSetOutputWeights.txt";
filePathTrainedHiddenWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/FF/60t/TrainedSetHiddenWeights.txt";
// 30 Hidden Nodes, learning rate 1, binary input, continue training 5 epochs at a time on a 60th of the data set
FeedForward secondFF1E = new FeedForward(30,5,0.3,0,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,60);//epochs 1-5
FeedForward secondFF2E = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,60);//epochs 6-10
FeedForward secondFF3E = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,60);//epochs 11-15
FeedForward secondFF4E = new FeedForward(30,5,0.3,2,true,filePathResults,filePathTrainedOutputWeights,filePathTrainedHiddenWeights,60);//epochs 16-20
secondFF1E=null;
secondFF2E=null;
secondFF3E=null;
secondFF4E=null;
}
/*
* Runs Experiment #7
*/
public static void runKNNKEx() throws IOException, ClassNotFoundException{
//Runs K-NN networks with multiple K values on the 12,000 images in the training set using raw gray scale images. Tests using 1000 images.
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/KNN/k/KNNResults";
KNearestNeighbors a = new KNearestNeighbors(1,false,5,1000,filePathResults);
KNearestNeighbors b = new KNearestNeighbors(2,false,5,1000,filePathResults);
KNearestNeighbors c = new KNearestNeighbors(3,false,5,1000,filePathResults);
KNearestNeighbors d = new KNearestNeighbors(4,false,5,1000,filePathResults);
KNearestNeighbors e2 = new KNearestNeighbors(5,false,5,1000,filePathResults);
KNearestNeighbors f = new KNearestNeighbors(6,false,5,1000,filePathResults);
KNearestNeighbors g = new KNearestNeighbors(7,false,5,1000,filePathResults);
KNearestNeighbors h = new KNearestNeighbors(8,false,5,1000,filePathResults);
KNearestNeighbors i = new KNearestNeighbors(9,false,5,1000,filePathResults);
KNearestNeighbors j = new KNearestNeighbors(10,false,5,1000,filePathResults);
a=null;
b=null;
c=null;
d=null;
e2=null;
f=null;
g=null;
h=null;
i=null;
j=null;
}
/*
* Runs Experiment #8
*/
public static void runKNNTrainingExamplesEx() throws IOException, ClassNotFoundException{
//Runs K-NN networks with multiple K=3 on the various number of images in the training set using raw gray scale images. Tests using 1000 images.
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/KNN/trainingExamples/KNNResults";
KNearestNeighbors a = new KNearestNeighbors(3,false,1,1000,filePathResults);
KNearestNeighbors b = new KNearestNeighbors(3,false,2,1000,filePathResults);
KNearestNeighbors c = new KNearestNeighbors(3,false,3,1000,filePathResults);
KNearestNeighbors d = new KNearestNeighbors(3,false,4,1000,filePathResults);
KNearestNeighbors e2 = new KNearestNeighbors(3,false,5,1000,filePathResults);
KNearestNeighbors f = new KNearestNeighbors(3,false,10,1000,filePathResults);
KNearestNeighbors g = new KNearestNeighbors(3,false,20,1000,filePathResults);
KNearestNeighbors h = new KNearestNeighbors(3,false,50,1000,filePathResults);
KNearestNeighbors i = new KNearestNeighbors(3,false,100,1000,filePathResults);
KNearestNeighbors j = new KNearestNeighbors(3,false,1000,1000,filePathResults);
a=null;
b=null;
c=null;
d=null;
e2=null;
f=null;
g=null;
h=null;
i=null;
j=null;
}
/*
* Runs Experiment #5
*/
public static void runRBFLearningRateEx() throws IOException, ClassNotFoundException{
//50 epochs for each learning rate
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.1/RbfResults";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.1/TrainedRBFSetOutputWeights.txt";
//Learning rate=0.1
RadialBasisFunction a = new RadialBasisFunction(20,false,1000000,5,0.1,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction ee = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i = new RadialBasisFunction(20,false,1000000,5,0.1,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j = new RadialBasisFunction(20,false,700000,5,0.1,2,filePathResults,filePathTrainedOutputWeights);
a=null;
b=null;
c=null;
d=null;
ee=null;
f=null;
g=null;
h=null;
i=null;
j=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.3/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.3/TrainedRBFSetOutputWeights.txt";
//Learning rate=0.3
RadialBasisFunction a2 = new RadialBasisFunction(20,false,1000000,5,0.3,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i2 = new RadialBasisFunction(20,false,1000000,5,0.3,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j2 = new RadialBasisFunction(20,false,700000,5,0.3,2,filePathResults,filePathTrainedOutputWeights);
b2=null;
c2=null;
d2=null;
e2=null;
f2=null;
g2=null;
h2=null;
i2=null;
j2=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.5/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.5/TrainedRBFSetOutputWeights.txt";
//Learning rate=0.5
RadialBasisFunction a3 = new RadialBasisFunction(20,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i3 = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j3 = new RadialBasisFunction(20,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a3=null;
b3=null;
c3=null;
d3=null;
e3=null;
f3=null;
g3=null;
h3=null;
i3=null;
j3=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.9/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/LR/0.9/TrainedRBFSetOutputWeights.txt";
//Learning rate=0.9
RadialBasisFunction a4 = new RadialBasisFunction(20,false,1000000,5,0.9,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i4 = new RadialBasisFunction(20,false,1000000,5,0.9,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j4 = new RadialBasisFunction(20,false,700000,5,0.9,2,filePathResults,filePathTrainedOutputWeights);
a4=null;
b4=null;
c4=null;
d4=null;
e4=null;
f4=null;
g4=null;
h4=null;
i4=null;
j4=null;
}
/*
* Runs Experiment #4
*/
public static void runRBFSigmaEx() throws IOException, ClassNotFoundException{
//50 epochs for each sigmaSquared
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/1000000/RbfResults";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/1000000/TrainedRBFSetOutputWeights.txt";
//sigmaSquared=1000000
RadialBasisFunction a = new RadialBasisFunction(20,false,1000000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction ee = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j = new RadialBasisFunction(20,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a=null;
b=null;
c=null;
d=null;
ee=null;
f=null;
g=null;
h=null;
i=null;
j=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100000/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100000/TrainedRBFSetOutputWeights.txt";
//sigmaSquared=100000
RadialBasisFunction a2 = new RadialBasisFunction(20,false,100000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i2 = new RadialBasisFunction(20,false,100000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j2 = new RadialBasisFunction(20,false,70000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a2=null;
b2=null;
c2=null;
d2=null;
e2=null;
f2=null;
g2=null;
h2=null;
i2=null;
j2=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100/TrainedRBFSetOutputWeights.txt";
//sigmaSquared=100
RadialBasisFunction a3 = new RadialBasisFunction(20,false,100,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i3 = new RadialBasisFunction(20,false,100,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j3 = new RadialBasisFunction(20,false,70,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a3=null;
b3=null;
c3=null;
d3=null;
e3=null;
f3=null;
g3=null;
h3=null;
i3=null;
j3=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100000000/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/Sigma/100000000/TrainedRBFSetOutputWeights.txt";
//sigmaSquared=100000000
RadialBasisFunction a4 = new RadialBasisFunction(20,false,100000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i4 = new RadialBasisFunction(20,false,100000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j4 = new RadialBasisFunction(20,false,70000000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a4=null;
b4=null;
c4=null;
d4=null;
e4=null;
f4=null;
g4=null;
h4=null;
i4=null;
j4=null;
}
/*
* Runs Experiment #6
*/
public static void runRBFTrainingExamplesEx() throws IOException, ClassNotFoundException{
// 50 epochs for each number of training examples
String filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/20/RbfResults";
String filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/20/TrainedRBFSetOutputWeights.txt";
// Training data set reduction factor = 20
RadialBasisFunction a = new RadialBasisFunction(20,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction ee = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i = new RadialBasisFunction(20,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j = new RadialBasisFunction(20,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a=null;
b=null;
c=null;
d=null;
ee=null;
f=null;
g=null;
h=null;
i=null;
j=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/1/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/1/TrainedRBFSetOutputWeights.txt";
// Training data set reduction factor = 1
RadialBasisFunction a2 = new RadialBasisFunction(1,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i2 = new RadialBasisFunction(1,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j2 = new RadialBasisFunction(1,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
b2=null;
c2=null;
d2=null;
e2=null;
f2=null;
g2=null;
h2=null;
i2=null;
j2=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/5/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/5/TrainedRBFSetOutputWeights.txt";
// Training data set reduction factor = 5
RadialBasisFunction a3 = new RadialBasisFunction(5,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i3 = new RadialBasisFunction(5,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j3 = new RadialBasisFunction(5,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a3=null;
b3=null;
c3=null;
d3=null;
e3=null;
f3=null;
g3=null;
h3=null;
i3=null;
j3=null;
filePathResults = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/200/RbfResults";
filePathTrainedOutputWeights = "/Users/zackeryleman/Desktop/NeuralNetOutput/RBF/TE/200/TrainedRBFSetOutputWeights.txt";
// Training data set reduction factor = 200
RadialBasisFunction a4 = new RadialBasisFunction(200,false,1000000,5,0.5,0,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction b4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction c4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction d4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction e4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction f4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction g4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction h4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction i4 = new RadialBasisFunction(200,false,1000000,5,0.5,1,filePathResults,filePathTrainedOutputWeights);
RadialBasisFunction j4 = new RadialBasisFunction(200,false,700000,5,0.5,2,filePathResults,filePathTrainedOutputWeights);
a4=null;
b4=null;
c4=null;
d4=null;
e4=null;
f4=null;
g4=null;
h4=null;
i4=null;
j4=null;
}
}