how to run function 1000 times to find minimum

i have a main function that eventually gives a score with randomized grouping of names. score is just one number calculated through function. I want to run this 1000 times to have 1000 scores and want to know minimum and know which grouping gave that score.

how can i do this?

[per isakson]

I restored the original subject line

[Stephen23]

Original question (from Google Cache):

i have a main function that eventually gives a score with randomized grouping of names. score is just one number calculated through function. I want to run this 1000 times to have 1000 scores and want to know minimum and know which grouping gave that score.

how can i do this?

[Rena Berman]

(Answers Dev) Restored edit

Answer 1

[per isakson]

Something like

S = struct( 'score', cell(1,1000), 'grouping', cell(1,1000) );
for jj = 1 :1000
    S(jj).grouping = randomized_grouping_of_names();
    S(jj).score = main_function( S(jj).grouping );
end
[~,ix_min] = min([S.score]);
S(ix_min)

In response to comment

What's in the file, FileName ?

FileName  = ???
groupsize = ???
data = readtable( FileName, 'ReadRowNames', true );
number_of_people = size(data, 2);
number_of_groups = ceil( number_of_people / groupsize );

S = struct( 'score', cell(1,N), 'grouping', cell(1,N) );
for jj = 1 : N
    S(jj).grouping = generate_random_grouping( data, number_of_people, number_of_groups );
    S(jj).score = score_grouping( data, S(jj).grouping, ~ );
end
[~,ix_min] = min([S.score]);
S(ix_min)

No good.

With substatial help from Walter

>> subject_scores = Jeon_clust( 'Biol_528_2019_sheet.xlsx', 3 )
subject_scores = 
  struct with fields:
       score: 0.66667
    grouping: {1×8 cell}

where

function    S = Jeon_clust( FileName, groupsize )
    N = 1000;
    data = readtable( FileName, 'ReadRowNames', true );
    number_of_people = size(data, 2);
    number_of_groups = ceil( number_of_people / groupsize );
    S = struct( 'score', cell(1,N), 'grouping', cell(1,N) );
    for jj = 1 : N
        S(jj).grouping = generate_random_grouping( data, number_of_people, number_of_groups );
        S(jj).score = sum_total_subject_score( data, S(jj).grouping, number_of_groups );
    end
    [~,ix_min] = min([S.score]);
    S = S(ix_min);
end

and

function    stss = sum_total_subject_score( data, grouping, number_of_groups )
    subject_scores = score_grouping(data, grouping,number_of_groups);
    group_ = [subject_scores{1:number_of_groups}];
    group_scores = group_.';
    diff_score = diff(group_scores);
    total_subject_score = abs(sum(diff_score));
    stss = sum(total_subject_score);
end

[Kangkeon Jeon]

function subject_scores = Jeon_clust(FileName, groupsize)
    data = readtable(FileName, 'ReadRowNames', true);
    number_of_people = size(data, 2);
    number_of_groups = ceil(number_of_people / groupsize);
    number_of_questions = size(data, 1);
    grouping = generate_random_grouping(data, number_of_people, number_of_groups);
    subject_scores = score_grouping(data, grouping,number_of_groups);
    group_ = [subject_scores{1:number_of_groups}];
    group_scores = group_.'
    diff_score = diff(group_scores)
    total_subject_score = abs(sum(diff_score))
    score = sum(total_subject_score)
end
function grouping =  generate_random_grouping(data, number_of_people, number_of_groups)
    % Create random order of names
    randOrder = randperm(number_of_people);
    namesPerm = data.Properties.VariableNames(randOrder);
    % Determine number of people per group
    groupNum = floor(number_of_people / number_of_groups) + [ones(1,rem(number_of_people,number_of_groups)), zeros(1,number_of_groups - rem(number_of_people,number_of_groups))];
    % Create groups
    groupIdx = [0,cumsum(groupNum)];
    grouping = arrayfun(@(i){namesPerm(groupIdx(i)+1:groupIdx(i+1))},1:numel(groupNum));
end
function score = score_grouping(data, grouping, ~)
    meanFun = @(x) squeeze(mean(mean(permute(reshape(data{:, x}', length(x), 3, 5), [2, 1, 3]))));
    score = cellfun(meanFun, grouping, 'UniformOutput', false);
end

so this is my whole code... could you help me how i can use your code with names i have T_T

[per isakson]

Functions shall have a help-text that at least describes input and output arguments!

[Kangkeon Jeon]

FileName is excel file that contains all numbers to generate scores and names to generate groups.

[per isakson]

What is the size of the table data ?

[Kangkeon Jeon]

i attached the file!

Answer 2

[Walter Roberson]

N = 1000;
all_scores = zeros(1,N);
all_groupings = cell(1,N);
for iter = 1 : N
    [all_scores(iter), all_groupings{N}] = generate_one_grouping_and_score_it();
end

[minscore, idx] = min(all_scores);
grouping_for_minscore = all_groupings{idx};
disp(minscore)
disp(grouping_for_minscore)

[Kangkeon Jeon]

would i type this in the command window?

[Walter Roberson]

Normally you would put it into a .m file and run the file.

Note that you will need to adjust the line

    [all_scores(iter), all_groupings{N}] = generate_one_grouping_and_score_it();

to reflect the real name of your function. Also, this assumes that the function returns the grouping as well as the score (you need it to return the grouping in order to be able to display the grouping later.)

[Kangkeon Jeon]

i tried this but it gave "Insufficient number of outputs from right hand side of equal sign to satisfy assignment" error. am i doing this wrong?

[Walter Roberson]

function [best_score, best_grouping] = Jeon_clust(FileName, groupsize)
data = readtable(FileName, 'ReadRowNames', true);
number_of_people = size(data, 2);
number_of_groups = ceil(number_of_people / groupsize);
number_of_questions = size(data, 1);
N = 1000;
all_scores = zeros(1,N);
all_groups = cell(1,N);

for iter = 1 : N
    grouping = generate_random_grouping(data, number_of_people, number_of_groups);
    subject_scores = score_grouping(data, grouping,number_of_groups);
    group_ = [subject_scores{1:number_of_groups}];
    group_scores = group_.';
    diff_score = diff(group_scores);
    total_subject_score = abs(sum(diff_score));
    score = sum(total_subject_score);
    all_scores(iter) = score;
    all_groups{iter} = grouping;
end
[best_score, idx] = min(all_scores);
best_grouping = all_groups{idx};