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

Question

Am totally new to Matlab I need some help, I got some error while running the code

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