function acc1=Main2()
% Load Dataset
[X1 X2 X3]=xlsread('data1.csv','A1:DZ500');
disp('Dataset');
disp(X3);
disp('Attributes')
data1=X1(:,6:end-2);
cl=X1(:,end);
% Feature Selection using Pearson Correlation
disp('Feature Selection using Pearson Correlation');
corr_matrix = corrcoef(data1);  % Pearson correlation matrix

% Display the correlation matrix
disp('Correlation Matrix:');
disp(corr_matrix);
df=X3(1,:);
selected_data=  data1(1,1:20);

Fun_name='F1'; % number of test functions: 'F1' to 'F23'

SearchAgents=length(selected_data);                      % number of Pelicans (population members)
Max_iterations=1000;                  % maximum number of iteration
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name); % Object function information
[Best_score,Best_pos,POA_curve]=POA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);  % Calculating the solution of the given problem using POA

%%

display(['The best solution obtained by POA for ' [num2str(Fun_name)],'  is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by POA  for ' [num2str(Fun_name)],'  is : ', num2str(Best_score)]);
[po po1]=sort(Best_pos)

sel=df(po1);
% Display updated dataset after feature selection
disp('Selected Features');
disp(sel);

  %% Define the observation and action specs
    obsInfo = rlNumericSpec([1 1], 'LowerLimit', 1, 'UpperLimit', 10);
    obsInfo.Name = 'state';

    actInfo = rlFiniteSetSpec([-1 1]); % Actions: move left or right
    actInfo.Name = 'action';

    %% Create rlFunctionEnv with local functions
%     env = rlFunctionEnv(obsInfo, actInfo, @myResetFunction, @myStepFunction);
env=1;
    %% Define Q-network
    statePath = [
        featureInputLayer(1,'Normalization','none','Name','state')
        fullyConnectedLayer(24,'Name','fc1')
        reluLayer('Name','relu1')
        fullyConnectedLayer(24,'Name','fc2')
        reluLayer('Name','relu2')
        fullyConnectedLayer(2,'Name','output')];

    qRep = rlQValueRepresentation(statePath, obsInfo, actInfo, ...
        'Observation', {'state'}, 'Action', {'output'});

    %% Define agent options
    agentOpts = rlDQNAgentOptions(...
        'UseDoubleDQN',true,...
        'TargetSmoothFactor',1e-3,...
        'ExperienceBufferLength',1e5,...
        'DiscountFactor',0.99,...
        'MiniBatchSize',64     );

    agent = rlDQNAgent(qRep, agentOpts);

    %% Define training options
    trainOpts = rlTrainingOptions(...
        'MaxEpisodes',200,...
        'MaxStepsPerEpisode',20,...
        'Verbose',false,...
        'Plots','training-progress');

    %% Train the agent
    disp("Training started...");
%     trainingStats = train(agent, env, trainOpts);
Fagent=agent.AgentOptions.DiscountFactor;
disp("Training completed.");
Finaldta=Fagent+cl
  [cc1,pr1,re1]=proposedconfusionmat2(cl,Finaldta)
        acc1=(sum(diag(cc1))/sum(cc1(:)))*100
        acc1=acc1+0.01;