I get this error Error using == Matrix dimensions must agree. Error in Naivayes (line 21) if (yy{j}==yu{i})

Question

honi heni 2016-12-8

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/316044-i-get-this-error-error-using-matrix-dimensions-must-agree-error-in-naivayes-line-21-if-yy

编辑： honi heni 2016-12-8

Am trying to debug and run this code in matlab , Am totally new to Matlab

fid = fopen('C:\Users\Oni\Documents\MATLAB\data.csv')';
out = textscan(fid,'%s%s%s%s%s','delimiter',',');
fclose(fid);
num_featureswithclass = size(out,2);
tot_rec = size(out{size(out,2)},1) -1;
for i = 1:tot_rec
    yy{i} = out{num_featureswithclass}{i+1};
end
for i = 1: num_featureswithclass
    xx{i} = out{i};
end
%Calculation of Prior Probability
yu = unique(yy)  %unique class label
nc = length(yu)   %number of classes
fy = zeros(nc,1);
num_of_rec_for_each_class = zeros(nc,1);
for i=1:nc
   for j =1:tot_rec
        if (yy{j}==yu{i})
            num_of_rec_for_each_class(i) = num_of_rec_for_each_class(i) +1;
        end 
   end
end
          %Likelihood Table
          prob_table = zeros(num_featureswithclass-1,10,nc);
          for col = 1:num_featureswithclass-1
              unique_value = unique(xx{col});
              rec_unique_value{col} = unique_value;
              for i = 2: length(unique_value)
                  for j = 2:tot_rec+1
                      if strcmp (xx{col}{j}, unique_value{i}) == 1 && strcmp(xx{num_featureswithclass}{j}, yu{1}) == 1
                          prob_table(col, i-1,1) = prob_table(col,i-1,1) +1;
                      end
                      if strcmp(xx{col}{j}, unique_value{i}) == 1 && strcmp (xx{num_featureswithclass}{j}, yu{2}) == 1
                          prob_table(col, i-1,2) = prob_table(col,i-1,2) + 1;
                      end
                  end
              end
          end
          prob_table(:,:,1) = prob_table(:,:,1)./num_of_rec_for_each_class(1);
          prob_table(:,:,2) = prob_table(:,:,2)./num_of_rec_for_each_class(2);
          %The matrix "prob_table" used in the above code is a matrix of 
          %4 * 10 * 2 deimension where "4" is the number of attributes in the
          % dataset.The number "10" is the possible number of unique value in
          % any attribute.In this example,the maximum number was "3". The
          % number "2" refer to the number of classes. If we see the values
          % present in theprob_table, the understanding will be further
          % enhanced
          %
          %Predicting for an unlabeled record:
          %Now that we have a naive Bayesian classifier in the form of
          %tables, we can use them to predict newly arriving unlabeled
          %records. The following code snippet describes the prediction
          %process in matlab
          A = {'Sunny', 'hot', 'high', 'false'};
          A1= find(ismember(rec_unique_value{1},A{1}));
          A11 = 1;
          A2 = find(ismember(rec_unique_value{2},A{2}));
          A21=2;
          A3 = find(ismember(rec_unique_value{3}, A{3}));
          A31 = 3;
          A4 = find(ismember(rec_unique_value{4},A{4}));
          A41 = 4;
          ProbN = prob_table(A11,A1 - 1,1) *prob_table(A21,A2 - 1,1) *prob_table(A31, A3 - 1,1) *prob_table(A41,A4 - 1,1) *fy(1);
          ProbP = prob_table(A11,A1 - 1,2) *prob_table(A21,A2 - 1,2) *prob_table(A31, A3 - 1,2) *prob_table(A41,A4 - 1,2) *fy(2);
          if ProbN > ProbP
              prediction = 'N'
          else
              prediction = 'P'
          end