ThreeLayerNetwork.template

group ThreeLayerNetwork;

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

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

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

float netOutput[<outputsize>];

float sigmoidActivate(float x) {
	float y=(float)(1.0f / (1.0f + exp(-x)));
	return y;
}

Result getStep(float netInput[], 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-<outputnodes>L); i=i+1) {
      float sumValue=0.0f;
	  for (j=0L; j \< <inputnodes>L; j=j+1) {
        sumValue=sumValue+weight[j][i]*netOutput[j];
      }
	  activation[i]=bias[i]+sumValue;
	  netOutput[i]=sigmoidActivate(activation[i]);
    }
	for (i=(<nodes>L-<outputnodes>L); i\< <nodes>L; i=i+1) {
	  float sumValue=0.0f;
	  for (j=<inputnodes>L; j \< (<nodes>L-<outputnodes>L); j=j+1) {
	    sumValue=sumValue+weight[j][i]*netOutput[j];
	  }
	  activation[i]=bias[i]+sumValue;
	  netOutput[i]=sigmoidActivate(activation[i]);
	}
	float outputVector [<outputnodes>];
	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, inputsize);
  }
  return getStep(netInput, inputsize);
}

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