Hye, i got a problem when i want to run this code where it stated not enough input arguments. can anyone help me.

function [z, out]=FeatureSelectionCost(u,nf,data)

    % Read Data Elements
    x=data.x;
    t=data.t;

    % Create Permutation uinsg Random Keys
    [~, q]=sort(u);

    % Selected Features
    S=q(1:nf);

    % Ratio of Selected Features
    rf=nf/numel(q);

    % Selecting Features
    xs=x(S,:);

    % Weights of Train and Test Errors
    wTrain=0.8;
    wTest=1-wTrain;

    % Number of Runs
    nRun=3;
    EE=zeros(1,nRun);
    for r=1:nRun
        % Create and Train ANN
        results=CreateAndTrainANN(xs,t);

        % Calculate Overall Error
        EE(r) = wTrain*results.TrainData.E + wTest*results.TestData.E;
    end

    E=mean(EE);
    %if isinf(E)
    %    E=1e10;
    %end

    % Calculate Final Cost
    z=E;

    % Set Outputs
    out.S=S;
    out.nf=nf;
    out.rf=rf;
    out.E=E;
    out.z=z;
    %out.net=results.net;
    %out.Data=results.Data;
    %out.TrainData=results.TrainData;
    %out.TestData=results.TestData;

end

Answer 1

[KSSV]

You have not provided the values to the inputs. Provide the values of u,nf,data and then call the function.

[nurul liyana]

i do provide the values in another file. still i cant figure the problem.

%% Problem Definition

data=LoadData();

nf=4;

CostFunction=@(u) FeatureSelectionCost(u,nf,data); % Cost Function

nVar=data.nx; % Number of Decision Variables

VarSize=[1 nVar]; % Size of Decision Variables Matrix

VarMin=0; % Lower Bound of Variables VarMax=1; % Upper Bound of Variables

%% PSO Parameters

MaxIt=20; % Maximum Number of Iterations

nPop=20; % Population Size (Swarm Size)

% w=1; % Inertia Weight % wdamp=0.99; % Inertia Weight Damping Ratio % c1=2; % Personal Learning Coefficient % c2=2; % Global Learning Coefficient

% Constriction Coefficients phi1=2.05; phi2=2.05; phi=phi1+phi2; chi=2/(phi-2+sqrt(phi^2-4*phi)); w=chi; % Inertia Weight wdamp=1; % Inertia Weight Damping Ratio c1=chi*phi1; % Personal Learning Coefficient c2=chi*phi2; % Global Learning Coefficient

% Velocity Limits VelMax=0.1*(VarMax-VarMin); VelMin=-VelMax;

%% Initialization

empty_particle.Position=[]; empty_particle.Cost=[]; empty_particle.Out=[]; empty_particle.Velocity=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.Best.Out=[];

particle=repmat(empty_particle,nPop,1);

BestSol.Cost=inf;

for i=1:nPop

    % Initialize Position
    particle(i).Position=unifrnd(VarMin,VarMax,VarSize);

    % Initialize Velocity
    particle(i).Velocity=zeros(VarSize);

    % Evaluation
    [particle(i).Cost, particle(i).Out]=CostFunction(particle(i).Position);

    % Update Personal Best
    particle(i).Best.Position=particle(i).Position;
    particle(i).Best.Cost=particle(i).Cost;
    particle(i).Best.Out=particle(i).Out;

    % Update Global Best
    if particle(i).Best.Cost<BestSol.Cost

        BestSol=particle(i).Best;

    end

end

BestCost=zeros(MaxIt,1);

%% PSO Main Loop

for it=1:MaxIt

    for i=1:nPop

        % Update Velocity
        particle(i).Velocity = w*particle(i).Velocity ...
            +c1*rand(VarSize).*(particle(i).Best.Position-particle(i).Position) ...
            +c2*rand(VarSize).*(BestSol.Position-particle(i).Position);

        % Apply Velocity Limits
        particle(i).Velocity = max(particle(i).Velocity,VelMin);
        particle(i).Velocity = min(particle(i).Velocity,VelMax);

        % Update Position
        particle(i).Position = particle(i).Position + particle(i).Velocity;

        % Velocity Mirror Effect
        IsOutside=(particle(i).Position<VarMin | particle(i).Position>VarMax);
        particle(i).Velocity(IsOutside)=-particle(i).Velocity(IsOutside);

        % Apply Position Limits
        particle(i).Position = max(particle(i).Position,VarMin);
        particle(i).Position = min(particle(i).Position,VarMax);

        % Evaluation
        [particle(i).Cost, particle(i).Out] = CostFunction(particle(i).Position);

        % Update Personal Best
        if particle(i).Cost<particle(i).Best.Cost

            particle(i).Best.Position=particle(i).Position;
            particle(i).Best.Cost=particle(i).Cost;
            particle(i).Best.Out=particle(i).Out;

            % Update Global Best
            if particle(i).Best.Cost<BestSol.Cost

                BestSol=particle(i).Best;

            end

        end

    end

    BestCost(it)=BestSol.Cost;

    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);

    w=w*wdamp;

end

[KSSV]

Have you given u value?

[nurul liyana]

i just declare the value u, still didnot function. still not enough input argument

[KSSV]

You define u,nf,data and the call the function:

   [z, out]=FeatureSelectionCost(u,nf,data) ;