Adaboost: Help on matlab code error

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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
Distr = weights;
trainpat = dataset;
traintarg = labels;
tr_size = size(trainpat, 2);
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;

I have dataset D x N and label 1 x N. And when I call this function by

load databaseAB
MaxIter = 100; % boosting iterations
% Step2: splitting data to training and control set
TrainData   = ALoad(:,1:2:end);
TrainLabels = BLoad(1:2:end);
ControlData   = ALoad(:,2:2:end);
ControlLabels = BLoad(2:2:end);
% Step3: constructing weak learner
weak_learner = tree_node_w(3); % pass the number of tree splits to the constructor
%
% Step4: training with Gentle AdaBoost
[RLearners RWeights] = RealAdaBoost(weak_learner, TrainData, TrainLabels, MaxIter);
%
% Step6: evaluating on control set
ResultR = sign(Classify(RLearners, RWeights, ControlData))
%
% Step7: calculating error
ErrorR  = sum(ControlLabels ~= ResultR) / length(ControlLabels);

gave me this error

Undefined function 'sort' for input arguments of type 'struct'.

Error in tree_node_w/do_learn_nu (line 65)

[DS, IX] = sort(trainpat(d,:));

Any one can help me for this error? Thank you.

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per isakson 2016-10-11

There is no function, sort, for structures.

Guesses:

trainpat is not intended to be a struct
the intention is to sort regarding a field of the structure, trainpat.
other mistake

Tuffahatul Ummah 2016-10-13

thank you for your answer.

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