MATLAB: Find the first column in a matrix that contains at least 1 non zero..

matrix minimum column

Hi,im trying to find the first column in a matrix that contain at least 1 non zero. then the function Findfirst should return me with the column number that refers to the leftmost column in a matrix that contains at least a non zero element. below is my code but i dont seem to get the right results can anybody help me in identifying my error?

function [p] = Findfirst(A,currentRow) [m n]=size(A); p=1; for i=currentRow:m; for j=1:n; if A(i,j)~=0 p=i; if p<n p=n else p=n+1; end

end end end

Best Answer

Austin, here you go:

 function [p] = Findfirst(A,currentRow)
 [m n]=size(A);
 p=n;
 for i=currentRow:m
     for j=1:n
         if A(i,j)~=0
             p=j;       % you wanted to get the col index, right?
             break;     % break out once you have your solution
         end
     end
 end
 end

A bit more compact...

 function [p] = Findfirst(A)
 p = [];
 for ii = 1:length(A(1,:))
     if ~isempty(find(A(:,ii)))
         p = ii;
         break;
     end
 end
 end

For small-size matrices you could also

 [r c] = find(A);
 p = min(c);

Related Solutions

MATLAB: Efficiency fix

Some simplifications at first:

counter3 = 1;
len = size(mymatrix2, 2);
for n = 1:len
  a = mymatrix(1, n);
  for p = 1:len
    b = mymatrix2(1,p);
    if abs(a - b) == 1
      if mymatrix2(2,n) >= mymatrix2(2,p)
        if ~any(a == replacewith)
          replacewith(counter3) = a;
        end
      elseif mymatrix2(2,p) >= mymatrix2(2,n);
        if ~any(b == replacewith)
          replacewith(counter3) = b;
        end
      end
      counter3 = counter3 + 1;
    end
  end
end

But I'm sure this profits from vectorization. Please post meaningful test data - a RAND is preferred.

MATLAB: Scalar Levinson Algorithm and Autocorrelation in MATLAB

I was able to solve the indices problem and correctly solve the levinson recursion equations. Posting just in case someone needs this in the future!

% Initialization
P = zeros(1,3);
alpha = zeros(1,3);
a(1,1) = -autocorr(2)/autocorr(1);
P(1) = autocorr(1);
P(2) = autocorr(1) - ((autocorr(2)^2)/autocorr(1));
% Start Algorithm
for N = 1:numlag-2
    sum = 0;
    for j = 1:N
        sum = sum + a(N,j)*autocorr(N+2-j);
    end
    alpha(N,1) = autocorr(N+2) + sum;
for i = 1:N
    a(N+1,i) = a(N,i) - (alpha(N)/P(N+1))*a(N,N+1-i);
end
    a(N+1,i) = a(N,i) - (alpha(N)/P(N+1))*a(N,N+1-i);
    a(N+1,N+1) = - alpha(N)/P(N+1);
    P(N+2) = P(N+1) - (alpha(N))^2/P(N+1);
    
% Tolerance Checks    
    zerochecker = abs(P(2) - P(1));
    if zerochecker < 0.5 
        N = 0;
        break
    end
    check = abs(abs(P(N+2)) - abs(P(N+1)));
    if  check < 1
        break
    end
end

As a BONUS I am posting my autocorrelation sequence below:

function [autocorr] = corr(numlag,dataD,n)
% autocorrelation sequence
for ii = 1:numlag
    lag = ii - 1;
        for iii = 1:n-lag
            seq1(iii,ii) = dataD(iii).* dataD(iii+lag);         
            numer = sum(seq1);
        end
        autocorr_unrounded = round(numer,2)';
end
autocorr = autocorr_unrounded/n;
end

Best Answer

Related Solutions

MATLAB: Efficiency fix

MATLAB: Scalar Levinson Algorithm and Autocorrelation in MATLAB

Related Question