%script write relate calculation  wavelet by ebrahimi 
clear;
clc;
%Load data
 % load S01;
 % s = S01(1:16);
 is=xlsread('DARAN.xlsx',1, 'C2:C20');
 os1=xlsread('DARAN.xlsx',1, 'D2:D20');
%  os2=xlsread('DARAN.xlsx',1, 'B2:B361');
%  os3=xlsread('DARAN.xlsx',1, 'B2:B361');
   %load leleccum;
  %s = leleccum(1:4096);
  %s=sin(1:.0001:pi);
  lis = length(is);
  los1 = length(os1);
%   los2 = length(os2);
%   los3 = length(os3);

%Plot signal and the 3rd level averaged signal
  [C,L] = wavedec(is,3,'haar');
  bis = waverec(C,L,'haar');
  A3 = wrcoef('a',C,L,'haar',3);
  D1 = wrcoef('d',C,L,'haar',1);
  D2 = wrcoef('d',C,L,'haar',2);
  D3 = wrcoef('d',C,L,'haar',3) ;
inputs = A3';
%inputs = is';
targets = os1';

% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize);

% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};


% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
net.trainFcn = 'trainlm';  % Levenberg-Marquardt

% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse';  % Mean squared error

% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotregression', 'plotfit'};


% Train the Network
[net,tr] = train(net,inputs,targets);

% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)

% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
valTargets = targets  .* tr.valMask{1};
testTargets = targets  .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)