diff options
author | Calvin <calvin@EESI> | 2013-02-18 09:01:52 -0500 |
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committer | Calvin <calvin@EESI> | 2013-02-18 09:01:52 -0500 |
commit | c69b7e13895e8692afedb742bfdcc110bd982974 (patch) | |
tree | 10ea4f946411b0fea8c24f94bb5697d8f8808796 | |
parent | b8f5709b8bebb1ebe53505505b1360cc21e1691b (diff) |
set the input matrix properly
-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) |