How can I solve this error ... Error using * Inner matrix dimensions must agree. Error in Naivayes (line 74) 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);

I got Error Error using * Inner matrix dimensions must agree.

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 strcmp ( yy{j} , yu{i} ) %(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

[Chaya N]

Please state your question.

[honi heni]

I got error in the code, How can i rectify this.Thanks

 Error using * Inner matrix dimensions must agree. Error in Naivayes (line 74) 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);

Answer 1

[Chaya N]

Please check the values of the variables A1, A2, A3 and A4.

These should be scalar and greater than 1, otherwise they generate 0 or negative indices in line 74, which cannot be accessed.

Also, if any (or all) of these variables are vectors, then each of the multiplicands in the right hand side of line 74 should be either the same size or scalar. In this case, you would need to use element-wise multiplication. Replace the ' * ' operands by ' .* '