how to calculate bmu in matlab

main.m
% assume you have a dataset X and an input data point x
% X is a n-by-d matrix, where n is the number of data points and d is the dimensionality
% x is a 1-by-d vector

% preprocessing the data
X_norm = normalize(X); % normalize the data
X_pca = pca(X_norm); % apply PCA to reduce the dimensionality

% clustering the data
k = 10; % define the number of clusters
[idx, C] = kmeans(X_pca, k); % apply k-means clustering to the data

% find the BMU
distances = pdist2(C, x); % calculate the Euclidean distance between each centroid and the input data point
[~, bmu_idx] = min(distances); % find the index of the closest centroid
bmu = X(bmu_idx, :); % get the corresponding data point
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