quikrCustomTranied was not using the specified kmer. Use the correct kmer and also rename rws and clmns to rows and columns respectively

:

1 files changed, 4 insertions, 4 deletions

diff --git a/src/matlab/quikrCustomTrained.m b/src/matlab/quikrCustomTrained.m
index c7961c7..e594951 100644
--- a/src/matlab/quikrCustomTrained.m
+++ b/src/matlab/quikrCustomTrained.m
@@ -10,12 +10,12 @@ if nargin~=4
     error('There must be exactly 4 input arguments: the training matrix, the /path/to/input/fastafile, the k-mer size, and lambda');

 end

 

-[rws, clumns]=size(trainingmatrix); %get the size of the training matrix

-if rws~=4^k

+[rows, columns]=size(trainingmatrix); %get the size of the training matrix

+if rows~=4^k

     error('Wrong k-mer size for input training matrix');

 end

 

-[status, counts]=unix(['count-kmers -r 6 -1 -u ' inputfasta]); %count the 6-mers in the fasta file, in the forward direction, return the counts without labels

+[status, counts]=unix([sprintf('count-kmers -r %d -1 -u ',k) ' ' inputfasta]); %count the k-mers in the fasta file, in the forward direction, return the counts without labels.

 if status ~= 0

   error('count-kmers failed: ensure count-kmers is in your path.');

 

@@ -25,7 +25,7 @@ counts=counts/sum(counts); %normalize the counts into a probability vector
 yaux=[0;lambda*counts]; %form the sample vector

 

 

-Aaux=[ones(1,clumns);lambda*trainingmatrix]; %form the k-mer sensing matrix

+Aaux=[ones(1,columns);lambda*trainingmatrix]; %form the k-mer sensing matrix

 warning off

 xstar=lsqnonneg(Aaux,yaux); %perform the non-negative lease squares

 warning on