Mercurial > repos > bgruening > ctb_machine_learning
comparison mds_plot.py @ 0:fe542273784f draft default tip
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author | bgruening |
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date | Thu, 15 Aug 2013 03:39:14 -0400 |
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-1:000000000000 | 0:fe542273784f |
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1 #!/usr/bin/env python | |
2 | |
3 import argparse | |
4 import os | |
5 import sklearn.manifold | |
6 import numpy | |
7 import math | |
8 import pylab | |
9 | |
10 if __name__ == "__main__": | |
11 parser = argparse.ArgumentParser( | |
12 description="""2D multidimenisnal scaling of NxN matrices with scatter plot""" | |
13 ) | |
14 | |
15 parser.add_argument("-i", "--input", dest="sm", | |
16 required=True, | |
17 help="Path to the input file.") | |
18 parser.add_argument("--oformat", default='png', help="Output format (png, svg)") | |
19 parser.add_argument("-o", "--output", dest="output_path", | |
20 help="Path to the output file.") | |
21 | |
22 args = parser.parse_args() | |
23 mds = sklearn.manifold.MDS( n_components=2, max_iter=300, eps=1e-6, dissimilarity='precomputed' ) | |
24 data = numpy.fromfile( args.sm ) | |
25 d = math.sqrt( len(data) ) | |
26 sm = numpy.reshape( data, ( d,d )) | |
27 pos = mds.fit( sm ).embedding_ | |
28 pylab.scatter( pos[:,0],pos[:,1] ) | |
29 pylab.savefig( args.output_path, format=args.oformat ) |