MILP to select representative points in a set

Hello guyz, I'm trying to run this code using the intlinprog to select N rows of data from a matrix with 235 rows and 10 columns with the smaller difference between they boxplots measured by rsme. But I did a code to run one time each from 2 to 235 selected rowns. Someone could help me with the selecting part to calculate the boxplots measurements? Here is the code:

clear all

close all

clc

set(0,'DefaultAxesFontName', 'Times New Roman')

set(0,'DefaultAxesFontSize', 12)

set(0,'DefaultTextFontname', 'Times New Roman')

set(0,'DefaultTextFontSize', 12)

A = MTD.data(:,11:20); % A matriz with 235 rowns and 10 columns

AI = length(A); % All instances

Ipos = (1:1:AI)'; % Datasets positions

ALL = [quantile(A,[0.75 0.5 0.25]);max(A);min(A)]; %boxplot do todo

x_i = optimvar('x_i', AI, 'Type', 'integer', 'LowerBound', 0,'UpperBound',1); % Variables

Sim = 1;

for i = 2 : AI

Sim = Sim + 1

for j=1:AI

TS(j) = Ipos(j)*x_i(j);

end

TS(find(TS==0))=[];

AS = A(TS,:);

NS = length(SLCT);

f = sum(rmse(ALL,AS));

constraint = sum(x_i) == Ipos(i); % One constraint

problem = optimproblem('Objective', f, 'Constraints', constraint); % Problem definition

options = optimoptions('intlinprog', 'Display', 'off'); % solving

[solution,fvalM] = solve(problem, 'Options', options);

x(i-1,:) = solution.x_i;

fval(i-1,1:2) = [fvalM sum(solution.x_i)];

end