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clear all;close all;clc;
%% RNN for prediction
%% Data folders
Hd_folder = 'Hig-DR';
Ld_folder = 'Low-DR';
Md_folder = 'Med-DR';
Train_folder = 'Nodes';
%% Train+Test+validation Data Extraction
Train_data =[];
for i = 1:3
if i==1
Path = Hd_folder;
elseif i==2
Path = Ld_folder;
elseif i==3
Path = Md_folder;
end
complete_Train_Path = strcat('Datasets\',Path,'\',Train_folder);
files_list = ls(complete_Train_Path);
files_list(1,:) =[];
files_list(1,:) =[];
for j = 1:60
CSV_Node_file = strcat(complete_Train_Path,'\',files_list(i,:));
Data_Struct = importdata(CSV_Node_file); % import the content of CSV file into structure
Data_values=Data_Struct.data;
Train_data = [Train_data;Data_values];
end
end
net = fitnet(5);
view(net)
[net,tr] = train(net,[Train_data(2:end,:)]',[Train_data(1:end-1,:)]');
view(net)
y = net([Train_data(2:end,:)]');
errors = gsubtract(y,[Train_data(1:end-1,:)]');
%% Plot of the Results
perf = perform(net,y,[Train_data(1:end-1,:)]');
figure, plotregression(y,[Train_data(1:end-1,:)]')
figure, ploterrhist(errors)
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotfit(targets,outputs)
figure, plotperform(tr) #MatlabFullCourse