knn code in matlab

main.m
% Load sample data
load fisheriris

% Define number of neighbors
k = 5;

% Define the number of samples
N = size(meas, 1);

% Define the indices of train and test samples
trainIdx = 1:2:N;
testIdx = 2:2:N;

% Split the data into train and test sets
trainData = meas(trainIdx, :);
trainLabels = species(trainIdx);
testData = meas(testIdx, :);
testLabels = species(testIdx);

% Classify the test samples using kNN
predictedLabels = zeros(size(testLabels));
for i = 1:size(testData, 1)
    % Compute the distances between the test sample and all train samples
    distances = sum((trainData - testData(i, :)).^2, 2);

    % Sort the distances in ascending order
    [sortedDistances, sortedIdx] = sort(distances);

    % Take the k nearest neighbors
    kNearestLabels = trainLabels(sortedIdx(1:k));

    % Count the number of samples in each class
    counts = histc(kNearestLabels, unique(kNearestLabels));

    % Find the class with the maximum number of samples
    [~, maxIdx] = max(counts);

    % Assign the predicted label
    predictedLabels(i) = unique(kNearestLabels(maxIdx));
end

% Compute the accuracy of the classification
accuracy = sum(predictedLabels == testLabels) / numel(testLabels)
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