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Use exp(F) as likelihood function.
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@skywaLKer518
skywaLKer518 committed Feb 1, 2012
1 parent 7ba7da3 commit 7efd7c4
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Showing 3 changed files with 97 additions and 10 deletions.
28 changes: 25 additions & 3 deletions src/Vector4.java
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,10 @@
class Vector4 extends Data{
private double v1[],v2[],v3[],v4[];
private int lable[];

// test TODO
private double F = 0;
private double num = 0;
Vector4(){
v1 = new double[Environment.dataSetSize];
v2 = new double[Environment.dataSetSize];
Expand Down Expand Up @@ -61,12 +65,24 @@ public double disFunc(double[] eta,int i){ // in train
}

public double disFunc(double[] eta,int i,int y){ // in test
double f = 0;
if (y == 0){// then f(y,x) = (x[1],x[2],x[3],x[4],0,0,0,0)
return (eta[0] * v1[i] + eta[1] * v2[i] + eta[2] * v3[i] + eta[3] * v4[i]);
f =(eta[0] * v1[i] + eta[1] * v2[i] + eta[2] * v3[i] + eta[3] * v4[i]);
// printV(i);
// System.out.println("F = "+f);
F += f;
num ++;
return f;
}
else{ // then f(y,x) = (0,0,0,0,x[1],x[2],x[3],x[4])
return (eta[4] * v1[i] + eta[5] * v2[i] + eta[6] * v3[i] + eta[7] * v4[i]);
f = (eta[4] * v1[i] + eta[5] * v2[i] + eta[6] * v3[i] + eta[7] * v4[i]);
// printV(i);
// System.out.println("F = "+f);
F += f;
num ++;
return f;
}

}

public int lableTest(int predic,int i){
Expand All @@ -79,6 +95,12 @@ public int getLable(int i){
public void printV(int i){
System.out.println("the "+i+"th : "+v1[i]+" "+v2[i]+" "+v3[i]+" "+v4[i]+" Lable: "+lable[i]);
}

public void printF(){
System.out.println();System.out.println();
System.out.println("F = "+F);
System.out.println("num= "+num);
System.out.println("average: "+F * 1.0 / num);
System.out.println();System.out.println();
}

}
78 changes: 71 additions & 7 deletions src/iSVM.java
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ public class iSVM {
private static final double alphaDP = 1.0;
private final int etaLength1 = 8; // for data setting 1
private final int R = 5; // repeating times in Algorithm 5
private final int Times = 5000; // TODO
private final int Times = 100; // TODO
private int z[];
private double eta[][];
private int prediction[];
Expand Down Expand Up @@ -52,7 +52,7 @@ public class iSVM {
public void go(Vector4 v4, int setSize, int trainSize) {
init();
train(v4,setSize,trainSize);
test(v4,setSize,trainSize);
test2(v4,setSize,trainSize);
}

private void train(Vector4 v4, int setSize, int trainSize) {
Expand All @@ -66,6 +66,17 @@ private void train(Vector4 v4, int setSize, int trainSize) {
change ++;
}
}

// without learning, totally random
/*
for (int i = 0; i < 4; i++){
drawEta(i);
}
number[1] = 33;
number[2] = 33;
number[3] = 34;
cateIndexMax = 3;
*/
}

private void init() {
Expand All @@ -86,17 +97,49 @@ private void init() {

/*
* test ( from 100 to 10000 - 1 )
* regard p(z = i) = number[i] / trainSize;
*/
private void test(Vector4 v4, int setSize, int trainSize) {
double disF0,disF1;score = 0;
for (int i = 100; i < Environment.dataSetSize; i++){
for (int i = trainSize; i < Environment.dataSetSize; i++){
disF0 = 0;
disF1 = 0;
for (int j = 0; j <= cateIndexMax; j++){
if (number[j] <= 0)
continue;
disF0 += v4.disFunc(eta[j],i,0) * number[j] / (1.0 * trainSize);
disF1 += v4.disFunc(eta[j],i,1) * number[j] / (1.0 * trainSize);
}
if (disF0 > disF1) prediction[i] = 0;
else prediction[i] = 1;

//test TODO
if (prediction[i] == 0) predic0++;
else predic1++;
if (v4.getLable(i) == 0) data0++;
else data1++;

score += v4.lableTest(prediction[i], i);

}
}

/*
* test ( from 100 to 10000 - 1 )
* regard p(z = i) = 1 / cateNumber;
*/
private void test2(Vector4 v4, int setSize, int trainSize) {
double disF0,disF1;score = 0;
for (int i = trainSize; i < Environment.dataSetSize; i++){
disF0 = 0;
disF1 = 0;
for (int j = 0; j <= cateIndexMax; j++){
if (number[j] <= 0)
continue;
disF0 += v4.disFunc(eta[j],i,0) * number[j] / (1.0 * cateNumber);
disF1 += v4.disFunc(eta[j],i,1) * number[j] / (1.0 * cateNumber);
disF0 += v4.disFunc(eta[j],i,0) * 1 / (1.0 * cateNumber);
disF1 += v4.disFunc(eta[j],i,1) * 1 / (1.0 * cateNumber);
// System.out.println("F =: (0)"+v4.disFunc(eta[j],i,0) * 1 / (1.0 * cateNumber));
// System.out.println("F =: (1)"+v4.disFunc(eta[j],i,0) * 1 / (1.0 * cateNumber));
}
if (disF0 > disF1) prediction[i] = 0;
else prediction[i] = 1;
Expand All @@ -107,7 +150,27 @@ private void test(Vector4 v4, int setSize, int trainSize) {
if (v4.getLable(i) == 0) data0++;
else data1++;

int t = score; // test
score += v4.lableTest(prediction[i], i);
// if (t == score){ // wrong
// System.out.print("--------------\nWrong\n :\n");
// v4.printV(i);
// System.out.println(cateNumber+" component");
// for (int j = 0; j <= cateIndexMax; j++){
// if (number[j] <= 0)
// continue;
// System.out.println("com "+j+" :"+v4.disFunc(eta[j],i,prediction[i]));
// }
// }
// else{
// System.out.print("--------------\nCorrect\n :\n");
// v4.printV(i);
// for (int j = 0; j <= cateIndexMax; j++){
// if (number[j] <= 0)
// continue;
// System.out.println("com "+j+" :"+v4.disFunc(eta[j],i,prediction[i]));
// }
// }

}
}
Expand All @@ -120,7 +183,7 @@ public void evaluation() {


/*
* here I use F(x,y,eta,z) (in paper iSVM, discriminative function) as the likelihood function.(problem //TODO)
* here I use exp_F(x,y,eta,z) (in paper iSVM, discriminative function) as the likelihood function.(problem //TODO)
*/
private void step1(Vector4 v4, int setSize, int trainSize) {
int c;
Expand All @@ -141,7 +204,8 @@ private void step1(Vector4 v4, int setSize, int trainSize) {
if (m2 >= m1)
accept = 1;
else
accept = m2/m1;
// accept = m2/m1;
accept = Math.exp(m2 - m1);
r = Math.random();
if (r <= accept){
updateCate(k,c);
Expand Down
1 change: 1 addition & 0 deletions src/main.java
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ public static void main(String args[]) throws IOException{
lk.evaluation();
}
lk.report();
v4.printF();
System.out.println(lk.changeTimes);

// for (int i = 5000; i < 5030; i ++){
Expand Down

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