diff options
| author | Calvin <calvin@EESI> | 2013-02-18 09:01:52 -0500 | 
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-02-18 09:01:52 -0500 | 
| commit | c69b7e13895e8692afedb742bfdcc110bd982974 (patch) | |
| tree | 10ea4f946411b0fea8c24f94bb5697d8f8808796 /quikr.py | |
| parent | b8f5709b8bebb1ebe53505505b1360cc21e1691b (diff) | |
set the input matrix properly
Diffstat (limited to 'quikr.py')
| -rwxr-xr-x | quikr.py | 7 | 
1 files changed, 4 insertions, 3 deletions
| @@ -33,6 +33,7 @@ def main():      # If we are using a custom trained matrix, we need to do some basic checks      if args.trained_matrix is not None:   +        trained_matrix_location = args.trained_matrix          if not os.path.isfile(args.trained_matrix):              parser.error("custom trained matrix not be found") @@ -50,8 +51,8 @@ def main():          trained_matrix_location = "output.npy"          input_lambda = 10000          kmer = 6 -        xstar = quikr(args.fasta, trained_matrix_location, kmer, input_lambda) -         +    xstar = quikr(args.fasta, trained_matrix_location, kmer, input_lambda) +    print xstar       return 0  def quikr(input_fasta_location, trained_matrix_location, kmer, default_lambda): @@ -90,7 +91,7 @@ def quikr(input_fasta_location, trained_matrix_location, kmer, default_lambda):    # perform the non-negative least squares    # import pdb; pdb.set_trace() -  counts = np.rot90(counts) +  trained_matrix = np.rot90(trained_matrix)    xstar = scipy.optimize.nnls(trained_matrix, counts)     xstar = xstar / sum(xstar)  | 
