Count sequence of zeros with For loop
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Let's say I have the following matrix:
a= 3 0 7 8 0 0 1 5
1 0 5 0 0 2 3 0
0 2 0 0 1 4 7 0
2 0 5 9 0 0 0 0
3 0 1 0 0 0 1 5
I wish to count the number of continuous 0s every time a 0 exists in each column. So for the above example, I want my output to be
zeros= 1 2 1 2 2 1 1 3
2 1 2 2
Is it possible to do this using a For loop? The output for each column can be made into a separate file if necessary.
Thank you!
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Answers (3)
Roman Müller-Hainbach
on 29 Aug 2017
It is not pretty, but it does what you want:
function zeros = contzeros(A)
zeros = cell( 1, size(A,2) );
for colind = 1:length(zeros)
column = A(:,colind)';
if column(1) == 0
zeros{colind} = max([0,find(column~=0,1)])-1;
end
[~,remains] = strtok(column,0);
while ~isempty(remains)
n = max([0,find(remains~=0,1)])-1;
if n == -1 && remains(end) == 0
zeros{colind}(end+1,1) = length(remains);
elseif n >= 1
zeros{colind}(end+1,1) = n;
end
[~,remains] = strtok(remains,0); %#ok<STTOK>
end
end
end
Notice that the output is a cell-array.
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Stephen23
on 29 Aug 2017
Edited: Stephen23
on 29 Aug 2017
Simply use diff and then run over the columns:
a = [...
3 0 7 8 0 0 1 5
1 0 5 0 0 2 3 0
0 2 0 0 1 4 7 0
2 0 5 9 0 0 0 0
3 0 1 0 0 0 1 5
];
idx = a([1,1:end,end],:)==0;
idx([1,end],:) = false;
tmp = num2cell(diff(idx,1,1),1);
fun = @(v)find(v<0)-find(v>0);
out = cellfun(fun,tmp,'uni',0);
giving the output in a cell array:
>> out{:}
ans = 1
ans =
2
2
ans = 1
ans =
2
1
ans =
2
2
ans =
1
2
ans = 1
ans = 3
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Jan
on 29 Aug 2017
Edited: Jan
on 29 Aug 2017
a = [3 0 7 8 0 0 1 5; ...
1 0 5 0 0 2 3 0; ...
0 2 0 0 1 4 7 0 ; ...
2 0 5 9 0 0 0 0 ; ...
3 0 1 0 0 0 1 5];
nCol = size(a, 2);
List = cell(1, nCol);
for iCol = 1:nCol
[B, N] = RunLength(a(:, iCol));
List{iCol} = N(B == 0);
end
You can create a cheap version of RunLength also:
function [b, n] = cheapRunLength(x)
d = [true; diff(x) ~= 0]; % TRUE if values change
b = x(d); % Elements without repetitions
k = find([d', true]); % Indices of changes
n = diff(k); % Number of repetitions
end
With the C-Mex this needs 40-50% of the runtime, but for small data, this might be not important.
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