diff options
| -rwxr-xr-x | quikr.py | 32 |
1 files changed, 19 insertions, 13 deletions
@@ -49,12 +49,12 @@ def main(): input_lambda = 10000 # If we aren't using a custom trained matrix, load in the defaults else: - trained_matrix_location = "output.npy" - input_lambda = 10000 - kmer = 6 + trained_matrix_location = "output.npy" + input_lambda = 10000 + kmer = 6 + xstar = quikr(args.fasta, trained_matrix_location, kmer, input_lambda) - np.savetxt("args.output, xstar, delimiter=",") - print xstar + np.savetxt(args.output, xstar, delimiter=",", fmt="%f") return 0 def quikr(input_fasta_location, trained_matrix_location, kmer, default_lambda): @@ -82,26 +82,32 @@ def quikr(input_fasta_location, trained_matrix_location, kmer, default_lambda): print "Detected Mac OS X" count_input = Popen(["count-osx", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE) - - - # load the output of our count program and form a probability vector from the counts counts = np.loadtxt(count_input.stdout) - counts = counts / np.sum(counts) + counts = counts / counts.sum(0) counts = default_lambda * counts + print counts.shape + counts = np.concatenate([np.zeros(1), counts]) - - # loado our trained matrix + # load our trained matrix trained_matrix = np.load(trained_matrix_location) - trained_matrix = np.rot90(trained_matrix) + + print counts.shape + print trained_matrix.shape #form the k-mer sensing matrix trained_matrix = trained_matrix * default_lambda; + + trained_matrix = np.transpose(trained_matrix); + trained_matrix = np.vstack((np.ones(trained_matrix.shape[1]), trained_matrix)) + # trained_matrix = np.transpose(trained_matrix); + print trained_matrix.shape + xstar, rnorm = scipy.optimize.nnls(trained_matrix, counts) - xstar = xstar / sum(xstar) + xstar = xstar / xstar.sum(0) return xstar |