Predictor

%Hidden Layer - Gaussian Activation Function
%Input and Output Layers - Linear
%MaxDataNo = No. of Years x 24
%Open and read file
disp(' ');
file=input('Please enter path of data file:', 's');
fid=fopen(file,'rt');
[DataArray]=fscanf(fid,'%d');
x=max(DataArray);
DataArray=DataArray/x;
DataArray2=DataArray;
%Initialization
InputUnitNo=24;
HiddenUnitNo=10;
OutputUnitNo=1;
MaxDataNo=120;
MaxPatternNo=MaxDataNo-InputUnitNo;
Eta=0.2;
Alpha=0.2;
Error=0.13;
Epoch=0;
RMSError=1;
%Weights
bias_k=rand(OutputUnitNo,1);
dbias_k=0;
bias_j=rand(HiddenUnitNo,1);
dbias_j=0;
vji=rand(HiddenUnitNo,InputUnitNo);
dvji=0;
wkj=rand(OutputUnitNo,HiddenUnitNo);
dwkj=0;
while RMSError>Error,
  if Epoch>1000,
   break;
  else
   
   for i=1:MaxPatternNo,
     %Start to learn
     Input=DataArray2(i:i+23);
     Target=DataArray(i+24);
     
     %Count netj
     netj=bias_j+vji*Input;
     
     %Gaussian
     HiddenOutput=exp(-(netj.^2));
     %Count netk
     netk=bias_k+wkj*HiddenOutput;
     Output=netk;
     
     %Correction Term
     delk=(Target-Output);
     delj=((-2*(netj.')*HiddenOutput)*(delk*wkj).');
     
     %Update Weights
     dwkj=Eta*delk*(HiddenOutput.')+Alpha*dwkj;
     dvji=Eta*delj*(Input.')+Alpha*dvji;
     wkj=wkj+dwkj;
     vji=vji+dvji;
     
     %Update Biases
     dbias_k=Eta*delk+Alpha*dbias_k;
     dbias_j=Eta*delj+Alpha*dbias_j;
     bias_k=bias_k+dbias_k;
     bias_j=bias_j+dbias_j;
     
     %Calculating Error Function
     ErrorFunc(i)=((Target-Output).^2);
     
     %Update Data Array with Output Value
     DataArray2(i+24)=Output;
     
   end
   RMSError=0.5*sum(ErrorFunc)
   Epoch=Epoch+1
  end
  
end
%Close Data File
fclose(fid);
disp('Weights are saved in C:\My Documents by default');
%Save Weights
fid2=fopen('C:\My Documents\Wkj.txt','wt');
fprintf(fid2,'%1.4f\n',wkj);
fclose(fid2);
fid3=fopen('C:\My Documents\Vji.txt','wt');
fprintf(fid3,'%1.4f\n',vji);
fclose(fid3);
%Save Biases
fid4=fopen('C:\My Documents\Biask.txt','wt');
fprintf(fid4,'%1.4f\n',bias_k);
fclose(fid4);
fid5=fopen('C:\My Documents\Biasj.txt','wt');
fprintf(fid5,'%1.4f\n',bias_j);
fclose(fid5);
DataArray=x*DataArray;
DataArray2=x*DataArray2;
Error=abs(DataArray-DataArray2);
i=0:MaxDataNo-1;
subplot(2,1,1);
hold on
plot(i,DataArray2(1:MaxDataNo),'m-.');
plot(i,DataArray(1:MaxDataNo));
subplot(2,1,2);
plot(i,Error(1:MaxDataNo),'g');
==================
%Recall Phase
%Load Data,Weights and Biases
disp(' ');
file=input('Please enter path of data file:', 's');
fid=fopen(file,'rt');
[DataArray]=fscanf(fid, '%d');
x=max(DataArray);
DataArray=DataArray/x;
fclose(fid);
disp(' ');
file=input('Please enter path of wkj file:', 's');
fid=fopen(file,'rt');
[readwkj]=fscanf(fid,'%f')
fclose(fid);
disp(' ');
file=input('Please enter path of vji file:', 's');
fid=fopen(file,'rt');
[readvji]=fscanf(fid,'%f');
fclose(fid);
disp(' ');
file=input('Please enter path of bias_k file:', 's');
fid=fopen(file,'rt');
[readbias_k]=fscanf(fid,'%f');
fclose(fid);
disp(' ');
file=input('Please enter in path of bias_j file:', 's');
fid=fopen(file,'rt');
[readbias_j]=fscanf(fid,'%f');
fclose(fid);
%Initialization
InputUnitNo=24;
HiddenUnitNo=10;
OutputUnitNo=1;
MaxDataNo=144;
MaxPatternNo=MaxDataNo-InputUnitNo;
PredictionDataNo=24;
DataArray2=[DataArray(1:MaxDataNo-PredictionDataNo);zeros(PredictionDataNo,1)];
%Initialize biases and weights
bias_j=readbias_j;
wkj=readwkj.';
bias_k=readbias_k;
%Convert vji to matrix
vji=readvji(1:HiddenUnitNo);
for i=11:10:InputUnitNo*HiddenUnitNo-9,
  vji=[vji,readvji(i:i+9)];
end

for i=1:MaxPatternNo,
  Input=DataArray2(i:i+23);
  %Count netj
  netj=bias_j+vji*Input;
  
  %Gaussian
  HiddenOutput=exp(-(netj.^2));
  
  %Count netk
  netk=bias_k+wkj*HiddenOutput;
  Output=abs(netk);  
  %Update Data Array with Output Value
  DataArray2(i+24)=Output;
   
end
DataArray2=DataArray2*x;
DataArray=DataArray*x;
i=0:MaxDataNo-1;
hold on
plot(i,DataArray2,'m-.');
plot(i,DataArray);
Error=abs(DataArray-DataArray2);
i=0:MaxDataNo-1;
subplot(2,1,1);
hold on
plot(i,DataArray2(1:MaxDataNo),'m-.');
plot(i,DataArray(1:MaxDataNo));
subplot(2,1,2);
plot(i,Error(1:MaxDataNo),'g');
title('Recall Phase using weights with RMS Error = 0.1163')