I'd like to assign a cumulative numerical value for sequential runs in a binary vector. What I have is
x = [0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0],
and what I would like is
y = [1 2 3 1 2 1 1 2 3 1 1 1 2 3 4 5 6].
The solution using sum/cumsum/unique/find range of functions alludes me. Any help would be greatly appreciated.
To find the number of runs, we can use rle function in R that stands for Run Length Encoding.
To number rows in a result set, you have to use an SQL window function called ROW_NUMBER() . This function assigns a sequential integer number to each result row.
The simplest way to create a sequence of numbers in R is by using the : operator. Type 1:20 to see how it works. That gave us every integer between (and including) 1 and 20 (an integer is a positive or negative counting number, including 0).
Here's a way:
a = arrayfun(@(x)(1:x), diff(find([1,diff(x),1])), 'uni', 0);
[a{:}]
The idea is to generate a list of the 'run lengths', i.e. [3,2,1,3,1,1,6]
in your case, then just concatenate a bunch of vectors that count to each value in that list, i.e. cat(2, 1:3, 1:2, 1:1, 1:3...
. I use arrayfun
as a shortcut for reapplying the :
operator and then use the comma separated list that {:}
returns as a shortcut for the concatenation.
(Not a one-liner, alas ...):
F = find(diff(x))+1;
y = ones(size(x));
y(F) = y(F)-diff([1,F]);
y = cumsum(y);
First, find all positions in x
where there is a change; then build a vector of 1 where you substract the length of each continuous segment. At the end, take the cumsum of it.
Create a sparse
matrix such that each run is on a different column, and then do the cumulative sum:
t = sparse(1:numel(x), cumsum([1 diff(x)~=0]), 1);
y = nonzeros(cumsum(t).*t).';
Use accumarray
with a custom function to generate each increasing pattern in a different cell, and then concatenate all cells:
y = accumarray(cumsum([1 diff(x)~=0]).', 1, [], @(x) {1:numel(x).'});
y = [y{:}];
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