rbfnetwork.java

RBFN for nuro analysis

			switch((int)Idata.getPoint()[q].DefaultClassify) {
			case 1:
				if(CentralPoint2[0].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[0]))
					CentralPoint2[0].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[0]);
				CentralPoint2[0].EspectedOutputSum=CentralPoint2[0].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
				break;
			case 2:
				if(CentralPoint2[1].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[1]))
					CentralPoint2[1].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[1]);
				CentralPoint2[1].EspectedOutputSum=CentralPoint2[1].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
				break;
			case 3:
				if(CentralPoint2[2].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[2]))
					CentralPoint2[2].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[2]);
				CentralPoint2[2].EspectedOutputSum=CentralPoint2[2].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
				break;
			case 4:
				if(CentralPoint2[3].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[3]))
					CentralPoint2[3].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[3]);
				CentralPoint2[3].EspectedOutputSum=CentralPoint2[3].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
				break;
			}
		}
		
		for(int f=0;f<Idata.getMaxClusterNum();f++) {
			hnode[f].center=CentralPoint[f];
			hnode[f].sigma=CentralPoint[f].MaxDistance;
			
			switch((int)(CentralPoint[f].EspectedOutputSum/CentralPoint[f].NCenterCluster)) {
			case 1:
				hnode[f].weight0=0;
				hnode[f].weight1=0;
				break;
			case 2:
				hnode[f].weight0=1;
				hnode[f].weight1=0;
				break;
			case 3:
				hnode[f].weight0=0;
				hnode[f].weight1=1;
				break;
			case 4:
				hnode[f].weight0=1;
				hnode[f].weight1=1;
				break;
			}
		//	hnode[f].weight0=;
		}
	}*/
	public double F(Point g) {
		double Sum=0;
		for(int e=0;e<Idata.getMaxClusterNum();e++) {
			
			Sum=Sum+(hnode[e].weight*(basis(g,hnode[e])));
			
		}
		Sum+=sita;
	//	System.out.println("sum:"+Sum);
		return Sum;
	}
	public double basis(Point g,HiddenNode h) {
		return Math.exp( 
				-(Math.pow((distance(g,h.center)),2) /  (2*h.sigma*h.sigma)  )
				);
	}
	public void WeightUpdate() {
		
	}
	public void SitaUpdate() {
		
	}
	public void training() {
		int count=0;
		double F=0;
		double miss=Double.MAX_VALUE;
		while(count<Times && miss>tolerance ) {
			miss=0.0;
			
			for(int g=0;g<Idata.getTrainingCount();g++) {  //training every point
				F=F(Idata.getPoint()[g]);
				miss=miss+
				( ((Idata.getPoint()[g].DefaultClassify-F)*(Idata.getPoint()[g].DefaultClassify-F))/2 );
			    System.out.println("F_points["+g+"]"+F+"| |"+"default:"+Idata.getPoint()[g].DefaultClassify);
				output.append("F_points["+g+"]"+F+"| |"+"default:"+Idata.getPoint()[g].DefaultClassify+"\n");
			    for(int d=0;d<Idata.getMaxClusterNum();d++) {
					System.out.println("weight:"+hnode[d].weight);
					//updata weight
					hnode[d].weight=hnode[d].weight+
					(LearningRate*(Idata.getPoint()[g].DefaultClassify-F)*basis(Idata.getPoint()[g],hnode[d])  );
				/*	//updata center
					hnode[d].center=hnode[d].center.sum(
						hnode[d].center,
					hnode[d].center.multiply(hnode[d].center.substract(Idata.getPoint()[g],hnode[d].center), 
					(LearningRate*(Idata.getPoint()[g].DefaultClassify-F)*basis(Idata.getPoint()[g],hnode[d]))
					)
					)
					; 
					*/
				/*	hnode[d].weight0=;
					hnode[d].weight1=;
					
					hnode[d].sigma=;
					hnode[d].sita=;
				*/	
				}
				sita=sita+(LearningRate* (Idata.getPoint()[g].DefaultClassify-F) );
			}
			 miss=miss/Idata.getTrainingCount();
			count++;
		}
		
		
	}
	public void testing() {
		
	double F=0;
		for(int g=0;g<Idata.getTestingCount();g++) {  //training every point
			F=F(Idata.getTestPoint()[g]);
			System.out.println("test_points["+g+"]"+F+"| |"+"default:"+Idata.getTestPoint()[g].FinalClassify);
		}
	}
	
	public void training2() {
		int count=0;
		double F=0;
		double miss=Double.MAX_VALUE;
		while(count<Times && miss>tolerance ) {
			miss=0.0;
			
			for(int g=0;g<Idata.getTrainingCount();g++) {  //training every point
				F=F(Idata.getPoint()[g]);
				miss=miss+
				( ((Idata.getPoint()[g].FinalClassify-F)*(Idata.getPoint()[g].FinalClassify-F))/2 );
			    System.out.println("F_points["+g+"]"+F+"| |"+"default:"+Idata.getPoint()[g].FinalClassify);
				output.append("F_points["+g+"]"+F+"| |"+"default:"+Idata.getPoint()[g].FinalClassify+"\n");
			    for(int d=0;d<Idata.getMaxClusterNum();d++) {
					System.out.println("weight:"+hnode[d].weight);
					//updata weight
					hnode[d].weight=hnode[d].weight+
					(LearningRate*(Idata.getPoint()[g].FinalClassify-F)*basis(Idata.getPoint()[g],hnode[d])  );
				/*	//updata center
					hnode[d].center=hnode[d].center.sum(
						hnode[d].center,
					hnode[d].center.multiply(hnode[d].center.substract(Idata.getPoint()[g],hnode[d].center), 
					(LearningRate*(Idata.getPoint()[g].DefaultClassify-F)*basis(Idata.getPoint()[g],hnode[d]))
					)
					)
					; 
					*/
				/*	hnode[d].weight0=;
					hnode[d].weight1=;
					
					hnode[d].sigma=;
					hnode[d].sita=;
				*/	
				}
				sita=sita+(LearningRate* (Idata.getPoint()[g].FinalClassify-F) );
			}
			 miss=miss/Idata.getTrainingCount();
			count++;
		}
		
		
	}


public void Init2() {
	//find center point 
	System.out.println("init finish");
	for(int s=0;s<Idata.getTrainingCount();s++) {
		
		int tmp=(int)Idata.getPoint()[s].DefaultClassify;
		System.out.println("tmp:"+tmp);
		switch(tmp) {
		case 1:
			CentralPoint2[0].SumOfVector=CentralPoint2[0].sum(CentralPoint2[0].SumOfVector,
					Idata.getPoint()[s]);
			CentralPoint2[0].NCenterCluster++;
			System.out.println("1 problem:"+CentralPoint2[0].NCenterCluster);
			break;
		
		case 2:
			CentralPoint2[1].SumOfVector=CentralPoint2[1].sum(CentralPoint2[1].SumOfVector,
					Idata.getPoint()[s]);
			CentralPoint2[1].NCenterCluster++;
			System.out.println("2 problem:"+CentralPoint2[1].NCenterCluster);
			break;
		
		case 3:
			CentralPoint2[2].SumOfVector=CentralPoint2[2].sum(CentralPoint2[2].SumOfVector,
					Idata.getPoint()[s]);
			CentralPoint2[2].NCenterCluster++;
			System.out.println("3 problem:"+CentralPoint2[2].NCenterCluster);
			System.out.println("Cluster number"+Idata.getMaxClusterNum());
			break;
		
		case 4:
			CentralPoint2[3].SumOfVector=CentralPoint2[3].sum(CentralPoint2[3].SumOfVector,
					Idata.getPoint()[s]);
			CentralPoint2[3].NCenterCluster++;
			System.out.println("4 problem:"+CentralPoint2[3].NCenterCluster);
			break;
			
		default :
			System.out.println("Default problem!");
		}

	}
	System.out.println("init finish");
	for(int w=0;w<Idata.getMaxClusterNum();w++) {
		System.out.println("NCenterCluster:"+CentralPoint2[w].NCenterCluster);
		CentralPoint2[w].Divide(CentralPoint2[w], CentralPoint2[w].NCenterCluster);
	}
	//
	//find sigma ,W
	System.out.println("init finish");
	for(int q=0;q<Idata.getTrainingCount();q++) {
		
		switch((int)Idata.getPoint()[q].DefaultClassify) {
		case 1:
			if(CentralPoint2[0].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[0]))
				CentralPoint2[0].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[0]);
			CentralPoint2[0].EspectedOutputSum=CentralPoint2[0].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
			break;
		case 2:
			if(CentralPoint2[1].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[1]))
				CentralPoint2[1].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[1]);
			CentralPoint2[1].EspectedOutputSum=CentralPoint2[1].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
			break;
		case 3:
			if(CentralPoint2[2].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[2]))
				CentralPoint2[2].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[2]);
			CentralPoint2[2].EspectedOutputSum=CentralPoint2[2].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
			break;
		case 4:
			if(CentralPoint2[3].MaxDistance< distance(Idata.getPoint()[q],CentralPoint2[3]))
				CentralPoint2[3].MaxDistance=distance(Idata.getPoint()[q],CentralPoint2[3]);
			CentralPoint2[3].EspectedOutputSum=CentralPoint2[3].EspectedOutputSum+Idata.getPoint()[q].DefaultClassify;
			break;
		}
		Idata.getPoint()[q].FinalClassify=(Idata.getPoint()[q].DefaultClassify-1)/(Idata.getMaxClusterNum()-1);

	}
	System.out.println("init finish");
	for(int f=0;f<Idata.getMaxClusterNum();f++) {
		System.out.println("Update_Weight_NCenterCluster:"+CentralPoint2[f].NCenterCluster);
		hnode[f].center=CentralPoint2[f];
		System.out.println("Center_point"+f+":"+CentralPoint2[f].position[0]+","+CentralPoint2[f].position[1]+","
				+CentralPoint2[f].position[2]+","+CentralPoint2[f].position[3]);
		hnode[f].sigma=CentralPoint2[f].MaxDistance;
		hnode[f].weight=(CentralPoint2[f].EspectedOutputSum/CentralPoint2[f].NCenterCluster);
		hnode[f].weight=(hnode[f].weight-1)/(Idata.getMaxClusterNum()-1);
	}
	System.out.println("init finish");
	}
}