From aac1c1beb22827feb8eedb518533aee84699750d Mon Sep 17 00:00:00 2001 From: Calvin Date: Tue, 19 Feb 2013 08:15:55 -0500 Subject: saving changes to work remotely --- quikr.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) (limited to 'quikr.py') diff --git a/quikr.py b/quikr.py index 4b29cec..25eb75b 100755 --- a/quikr.py +++ b/quikr.py @@ -52,6 +52,7 @@ def main(): input_lambda = 10000 kmer = 6 xstar = quikr(args.fasta, trained_matrix_location, kmer, input_lambda) + np.savetxt("python.csv", xstar, delimiter=",") print xstar return 0 @@ -80,21 +81,27 @@ 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 = default_lambda * counts - trained_matrix = np.load(trained_matrix_location) - - # perform the non-negative least squares - # import pdb; pdb.set_trace() + + # loado our trained matrix + trained_matrix = np.load(trained_matrix_location) trained_matrix = np.rot90(trained_matrix) - xstar = scipy.optimize.nnls(trained_matrix, counts) - + + #form the k-mer sensing matrix + trained_matrix = trained_matrix * default_lambda; + + xstar, rnorm = scipy.optimize.nnls(trained_matrix, counts) + xstar = xstar / sum(xstar) + return xstar -- cgit v1.2.3