MATLAB: Attempted to access best_dim(1); index out of bounds because numel(best_dim)=0.

attempt to access

Here is a code. I can not understand this error and how to resolve.
function [tree_node_left, tree_node_right, split_error] = do_learn_nu(tree_node, dataset, labels, weights, papa)
tree_node_left = tree_node; tree_node_right = tree_node;
if(nargin > 4) tree_node_left.parent = papa; tree_node_right.parent = papa; end
% [i,t] = weakLearner(weights,dataset', labels'); % % tree_node_left.right_constrain = t; % tree_node_right.left_constrain = t; % % tree_node_left.dim = i; % tree_node_right.dim = i; % % return;
%dataset=data_w(dataset) ;
%stump = set_distr(stump, get_sampl_weights(dataset)) ;
Distr = weights;
%[trainpat, traintarg] = get_train( dataset); trainpat = dataset; traintarg = labels; % traintarg = traintarg'; tr_size = size(trainpat, 1);
T_MIN = zeros(3,size(trainpat,1)); d_min = 1; d_max = size(trainpat,1);
for d = d_min : d_max;
[DS, IX] = sort(trainpat(d,:));
TS = traintarg(IX);
DiS = Distr(IX);
lDS = length(DS);
vPos = 0 * TS;
vNeg = vPos;
i = 1;
j = 1;
while i <= lDS
  k = 0;
  while i + k <= lDS && DS(i) == DS(i+k)
    if(TS(i+k) > 0)
      vPos(j) = vPos(j) + DiS(i+k);
    else
      vNeg(j) = vNeg(j) + DiS(i+k);
    end
    k = k + 1;
  end
  i = i + k;
  j = j + 1;
end
vNeg = vNeg(1:j-1);
vPos = vPos(1:j-1);
Error = zeros(1, j - 1);
InvError = Error;
IPos = vPos;
INeg = vNeg;
for i = 2 : length(IPos)
  IPos(i) = IPos(i-1) + vPos(i);
  INeg(i) = INeg(i-1) + vNeg(i);
end
Ntot = INeg(end);
Ptot = IPos(end);
for i = 1 : j - 1
  Error(i) = IPos(i) + Ntot - INeg(i);
  InvError(i) = INeg(i) + Ptot - IPos(i);
end
idx_of_err_min = find(Error == min(Error));
if(length(idx_of_err_min) < 1)
    idx_of_err_min = 1;  
end
if(length(idx_of_err_min) <1)
  idx_of_err_min = idx_of_err_min;
end
idx_of_err_min = idx_of_err_min(1);
idx_of_inv_err_min = find(InvError == min(InvError));
if(length(idx_of_inv_err_min) < 1)
    idx_of_inv_err_min = 1;  
end
idx_of_inv_err_min = idx_of_inv_err_min(1);
if(Error(idx_of_err_min) < InvError(idx_of_inv_err_min))
  T_MIN(1,d) = Error(idx_of_err_min);
  T_MIN(2,d) = idx_of_err_min;
  T_MIN(3,d) = -1;
else
  T_MIN(1,d) = InvError(idx_of_inv_err_min);
  T_MIN(2,d) = idx_of_inv_err_min;
  T_MIN(3,d) = 1;
end
end
dim = [];
best_dim = find(T_MIN(1,:) == min(T_MIN(1,:)));
dim = best_dim(1);
tree_node_left.dim = dim; tree_node_right.dim = dim;
TDS = sort(trainpat(dim,:));
lDS = length(TDS);
DS = TDS * 0;
i = 1; j = 1;
while i <= lDS k = 0; while i + k <= lDS && TDS(i) == TDS(i+k) DS(j) = TDS(i); k = k + 1; end i = i + k; j = j + 1; end
DS = DS(1:j-1);
split = (DS(T_MIN(2,dim)) + DS(min(T_MIN(2,dim) + 1, length(DS)))) / 2;
split_error = T_MIN(1,dim);
tree_node_left.right_constrain = split; tree_node_right.left_constrain = split;
function [i,t] = weakLearner(distribution,train,label) %disp('run weakLearner'); for tt = unique(train)%1:(16*256-1) error(tt)=(label .* distribution) * ((train(:,floor(tt/16)+1)>=16*(mod(tt,16)+1))); end [val,tt]=max(abs(error-0.5));
    i=floor(tt/16)+1;
    t=16*(mod(tt,16)+1);
    return;

Best Answer

best_dim = find(T_MIN(1,:) == min(T_MIN(1,:)));
dim = best_dim(1);
best_dim is empty, therefore there is no element 1. best_dim is empty because on the previous line find did not have any non-zero input values. That occurred because no values in T_MIN satisfied the equality operator, or possible T_MIN was empty. You can check that yourself.
Related Question