HebbNet.template

group HebbNet;

getOutputDeclaration(learningrate,stepnumber,inputnodes,outputsize,outputnodes,nodes,biases,weights) ::= <<

float bias[<nodes>]={<biases:{bia|<bia>f}; separator=", ">};

float currentweight[<nodes>][<nodes>]={<weights:{wei|{<wei:{we|<we>f}; separator=", ">}}; separator=", ">};

float netOutput[<outputsize>];

float linearActivate(float x){
	if (x \>= 1)
		{return 1;}
	else {
		if (x \<= 0)
			{return 0;}
		else
			{return x;}
		}
}

Result getStep(float netInput[], boolean learn, long inputsize){
    long i;
	long j;
	float activation [<nodes>];
	for (i=0L; i \< <nodes>L; i=i+1){
		activation[i]=0.0f;
	}
    for (i=0L; i \< inputsize; i=i+1) {
      netOutput[i]=netInput[i];
    }
	for (i=<inputnodes>L; i \< <nodes>L; i=i+1) {
      float sumValue=0.0f;
	  for (j=0L; j \< <nodes>L; j=j+1) {
        sumValue=sumValue+currentweight[j][i]*netOutput[j];
      }
	  activation[i]=bias[i]+sumValue;
    }
	float outputVector [<outputnodes>];
	for (i = <inputnodes>L; i \< <nodes>L; i=i+1) {
      netOutput[i]=linearActivate(activation[i]);
    }
	if (learn==true){
		float delta[<nodes>][<nodes>];
		for (i=<inputnodes>L; i \< <nodes>L; i=i+1) {
			for (j=0L; j \< <nodes>L; j=j+1){
				delta[j][i]=(float)(<learningrate>*(netOutput[i]-0.5f)*(netOutput[j]-0.5f));
			}
		}
		float sumValue=0.0f;
		long n=0L;
		for (i=<inputnodes>L; i \< <nodes>L; i=i+1) {
			for (j=0L; j \< <nodes>L; j=j+1){
				if (i!=j){
					sumValue=sumValue+delta[j][i];
					n=n+1;
				}
			}
		}
		float corr;
		corr=sumValue/((float)n);
		for (i=<inputnodes>L; i \< <nodes>L; i=i+1) {
			for (j=0L; j \< <nodes>L; j=j+1){
				if (i!=j){
					currentweight[j][i]=currentweight[j][i]+delta[j][i]-corr;
				}
			}
		}
	}
	for (i = (<nodes>L - <outputnodes>L); i \< <nodes>L; i=i+1) {
	  j = i - (<nodes>L - <outputnodes>L);
      outputVector[j]=netOutput[i];
    }
	Result r(outputVector,<outputnodes>L);
	return r; 
}

Result getOutput(float netInput[], long inputsize){
  long i;
  for (i=0L; i \< <outputsize>L; i=i+1) {
    netOutput[i]=0;
  }
  for (i=0L; i \< <stepnumber>L - 1; i=i+1) {
    getStep(netInput,false,inputsize);
  }
  return getStep(netInput,true,inputsize);
}

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