%Lab 6



%

% 1 sample solution

%

function loc = findPattern(dna, pat)

% loc is a vector of locations where string pat occurs in string dna



    pat_len = length(pat);

    dna_len = length(dna);

    index = 1;

    

    for(i = 1 : dna_len - pat_len + 1)

    

        % verifying a pattern matches

        found = 1;

        for(j = 1 : pat_len)

            if(dna(i+j-1) ~= pat(j))

                found = 0;

            end

        end

        

        % if pattern matches we store location

        if(found == 1)

            loc(index) = i;

            index = index + 1;

        end

    end

end





%

% 2 sample solution

%

function result = myDeterminant(x)

% result is the determinant of matrix x.  a1, a2, a3 are the minor matrices.



    a1(1,1) = x(2,2);

    a1(1,2) = x(2,3);

    a1(2,1) = x(3,2);

    a1(2,2) = x(3,3);

    

    a2(1,1) = x(2,1);

    a2(1,2) = x(2,3);

    a2(2,1) = x(3,1);

    a2(2,2) = x(3,3);

    

    a3(1,1) = x(2,1);

    a3(1,2) = x(2,2);

    a3(2,1) = x(3,1);

    a3(2,2) = x(3,2);

    

    result = x(1,1)*det(a1) - x(2,2)*det(a2) + x(3,3)*det(a3);

end





%

% Alternate 2 sample solution

%

function output=myDeterminant(x)

% output is the determinant of matrix x.



output= x(1,1)*det(x(2:3,2:3)) ...

        - x(1,2)*det(x(2:3,[1 3])) ...

        + x(1,3)*det(x(2:3,1:2));









%

% 3 sample solution

%

function minVal = minInNeighborhood(M, loc)

% minVal is the lowest value in the neighborhood of position 

% (loc(1),loc(2)), i.e., M(loc(1)-1:loc(1)+1, loc(2)-1:loc(2)+1).

% M is a matrix of numbers.  loc is a vector of length 2.



    [nr, nc]= size(M);  % dimensions of M



    % creating a bigger matrix which pads the borders

    for(i = 1:nr+2)

        for(j = 1:nc+2)

            if(i == 1 || j == 1 || i == nr+2 || j == nc+2)

                bigM(i,j) = realmax;

            else

                bigM(i,j) = M(i-1,j-1);

            end

        end

    end



    nr= nr+2;  nc= nc+2;        % dimensions of bigM

    r= loc(1)+1;  c= loc(2)+1;  % center of neighborhood in bigM

    

    % searching for the min value

    % minVal= minInMatrix( bigM(r-1:r+1, c-1:c+1) );

    minVal = realmax;

    

    for(i = r-1: r+1)

        for(j = c-1 : c+1)

            if(minVal > bigM(i,j))

                minVal = bigM(i,j);

            end

        end

    end

end