I = randi([1 50],10,5);
cnt = zeros (1,50);
for i=1:10
    for j=1:5
        temp=I(i,j);
        cnt(temp)=cnt(temp)+1;
    end
end

histogram(cnt);
---------------------


I=double(imread('cameraman.tif'));
Cnv_I=I;

[row,col]=size(I);
msk=[0 1 0;1 -4 1;0 1 0];

for i=2:row-1
    for j=2:col-1

        temp=I(i-1:i+1,j-1:j+1);
        cnv_sum=sum(sum(msk.*temp));

        Cnv_I(i,j)=cnv_sum;

    end
end

figure, imshow(uint8(I)),title('Original Image');

figure, imshow(Cnv_I),title('Convolved Image');

---------------------------------------------------



load fisheriris.mat;	
% Generate random data points for demonstration
rng(42); % Set random seed for reproducibility
num_points = 100;
num_clusters = 3;
data = rand(num_points, 2);
 
% Initialize cluster centroids randomly
initial_centroids = data(randperm(num_points, num_clusters), :);
 
% Number of iterations
max_iterations = 100;
tolerance = 1e-4;
 
% K-means algorithm
centroids = initial_centroids;
for iter = 1:max_iterations
    % Assign data points to nearest centroids
    distances = pdist2(data, centroids);
    [~, cluster_ids] = min(distances, [], 2);
 
    % Update centroids
    new_centroids = zeros(num_clusters, 2);
    for k = 1:num_clusters
        cluster_points = data(cluster_ids == k, :);
        new_centroids(k, :) = mean(cluster_points);
    end
 
    % Check for convergence
    if norm(new_centroids - centroids, 'fro') < tolerance
        break;
    end
 
    centroids = new_centroids;
end
 
% Plot the results
figure;
scatter(data(:, 1), data(:, 2), [], cluster_ids, 'filled');
hold on;
scatter(centroids(:, 1), centroids(:, 2), 100, 'k', 'filled', 'MarkerEdgeColor', 'w');
title('K-means Clustering');
legend('Data Points', 'Cluster Centroids');
hold off;