Selecting the same amount of data for all categories based on different labels

Hello. I am trying to edit the data in the input array above so that the new_data array has the same amount of bad,good and excellent quality trees for training of an AI. The bad, good and excellent are given by a label where [1; 0 ; 0] is bad, [0; 1 ; 0] is good and [0; 0 ; 1] is excellent. The bad trees have the least amount of data to train the AI so I am trying to reduce the good and and excellent data so they are all the same.

cvs_data =csvread('spruce tree timber quality.csv',1,0)
expert_ratings = cvs_data(:,12)' %inputs
inputs = cvs_data(:,1:11)
correct_labels = zeros(3,length(expert_ratings));
bad = 0;
good=0;
excellent=0;
histogram(expert_ratings)
Skew1 = skewness(expert_ratings)

for k =1 : length(expert_ratings)%length(expert_ratings)
    quality = cvs_data(k,12)';
  if quality >= 1 && quality <=4
      bad = bad+1;
        correct_labels(1:3, k) =[1; 0 ; 0];
  end
  if quality >= 5 && quality <=6
          good = good+1;
        correct_labels(1:3, k) =[0; 1 ; 0];
  end
  if quality >= 7 && quality <=10
      excellent = excellent+1;
        correct_labels(1:3, k) =[0; 0 ; 1];
  end
end



[good_inputs,rows_rem] = rmoutliers(inputs,"mean")
transposed_rows = rows_rem'
good_inputs_tp = good_inputs'
correct_labels(:,transposed_rows)=[];
correct_labels


%select the same amount of bad, good and excellent data 
correct_labels
good_inputs'
bad
good 
excellent

for column = 1:length(bad)    
    if correct_labels(:,column) == [0 1 0]
        new_data(column) = inputs(column)
    end
end
new_data

I tried to do this in the last part of the code but it doesn't seem to be working. Any help would be greatly appreciated.

Answer 1

[David Hill]

cvs_data =readmatrix('spruce tree timber quality.csv');
quality=cvs_data(:,12);
bad_data=cvs_data(quality<=4,1:11);
good_data=cvs_data(quality>=5&quality<=6,1:11);
excellent_data=cvs_data(quality>=7,1:11);
m=min([size(bad_data,1),size(good_data,1),size(excellent_data,1)]);
newdata=[bad_data(randperm(m),:);good_data(randperm(m),:);excellent_data(randperm(m),:)];