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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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Whale Optimization Algorithm Code Implementation


Whale Optimization Algorithm Code Files


function obj_fun(test_fun)
switch test_fun
    case 'F1'
        x = -100:2:100; y=x;
    case 'F2'
        x = -10:2:10; y=x;
end
end


function [LB,UB,D,FitFun]=test_fun_info(C)
switch C
    case 'F1'
        FitFun = @F1;
        LB = -100; 
        UB = 100;
        D = 30;
    case 'F2'
        FitFun = @F2;
        LB = -10;
        UB = 10;
        D = 30;
end
% F1 Test Function
    function r = F1(x)
        r = sum(x.^2);
    end
% F2 Test Function
    function r = F2(x)
        r = sum(abs(x))+prod(abs(x));
    end
end

function Position = initialize(Pop_Size,D,UB,LB)
SS_Bounds = size(UB,2);

if SS_Bounds == 1
    Position = rand(Pop_Size,D).*(UB-LB)+LB;
end

if SS_Bounds>1
    for i = 1:D
        UB_i = UB(i);
        LB_i = LB(i);
        Position(:,i) = rand(Pop_Size,1).*(UB_i-LB_i)+LB_i; 
    end
end
end

function [Best_Val,Best_Pos,Convergence_Curve]=WOA(Pop_Size,MaxT,LB,UB,D,FitFun)
Best_Pos = zeros(1,D);
Best_Val = inf;

Position = initialize(Pop_Size,D,UB,LB);
Convergence_Curve = zeros(1,MaxT);

T = 0;

while T<MaxT
    for i = 1:size(Position,1)
        CheckUB = Position(i,:)>UB;
        CheckLB = Position(i,:)<LB;
        Position(i,:) = (Position(i,:).*(~(CheckUB+CheckLB)))+UB.*CheckUB+LB.*CheckLB;
        %Calculate Fitness Values
        Fitness_Val = FitFun(Position(i,:));
        %Compare Fitness Values
        if Fitness_Val<Best_Val
            Best_Val = Fitness_Val;
            Best_Pos = Position(i,:);
        end
    end
    a = 2-T*((2)/MaxT);
    a2 = -1+T*((-1)/MaxT);
    
    %Agents Position Update (New Positions)
    for i=1:size(Position,1)
        r1=rand();
        r2=rand();
        A = 2*a*r1-a;
        C = 2 * r2;
        b = 1;
        l = (a2-1)*rand+1;
        p = rand();
        for j = 1:size(Position,2)
            if p<0.5
                if abs(A)>1
                    rand_best_index=floor(Pop_Size*rand()+1);
                    X_rand = Position(rand_best_index,:);
                    D_X_rand = abs(C*X_rand(j)-Position(i,j));
                    Position(i,j) = X_rand(j)-A*D_X_rand;
                elseif abs(A)<1
                    D_Best = abs(C*Best_Pos(j)-Position(i,j));
                    Position(i,j) = Best_Pos(j)-A*D_Best;
                end
            elseif p>=0.5
                distance2Best = abs(Best_Pos(j)-Position(i,j));
                Position(i,j) = distance2Best * exp(b.*1).*cos(1.*2*pi)+Best_Pos(j);    
            end
        end  
    end
    T = T + 1;  %Counter Increment
    Convergence_Curve(T) = Best_Val;
    [T Best_Val]
end
end


clc
clear all
Pop_Size = 100;
Objective_Fun = 'F2';
MaxT = 500;

[LB,UB,D,FitFun] = test_fun_info(Objective_Fun);
[Best_Val,Best_Pos,Sol_Convergence]=WOA(Pop_Size,MaxT,LB,UB,D,FitFun);


subplot(1,1,1);
semilogy(Sol_Convergence,'Color','r');
title('Convergence Curve');
xlabel('Iteration');
ylabel('Best Value');
axis tight
grid on
box on
legend ('WOA')

display(['Best Position',num2str(Best_Pos)]);
display(['Best_Value ',num2str(Best_Val)]);









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