def readInput(options, filename_wordsX, filename_wordsY, filename_seed):
# load data files
if options.pickled:
wordsX = IO.readPickledWords(filename_wordsX)
wordsY = IO.readPickledWords(filename_wordsY)
else:
wordsX = IO.readWords(filename_wordsX)
wordsY = IO.readWords(filename_wordsY)
if options.filename_graphX is not None:
print "loading graph -", options.filename_graphX
wordsX.G = IO.unpickle(options.filename_graphX)
print "loading graph -", options.filename_graphY
wordsY.G = IO.unpickle(options.filename_graphY)
seed_list = Struct()
seed_list.X, seed_list.Y = IO.readSeed(filename_seed) # read the seed list (X,Y)
wordsX.pushSeedToEnd(seed_list.X)
wordsY.pushSeedToEnd(seed_list.Y)
# assert sizes are correct
Nx = len(wordsX.words)
Ny = len(wordsY.words)
if Nx != Ny:
log(0, "Number of words must be the same", Nx, Ny)
else:
log(0, Nx, "words loaded.")
NSx = len(seed_list.X)
NSy = len(seed_list.Y)
if NSx != NSy:
log(0, "Number of seed words must be the same", NSx, NSy)
else:
log(0, NSx, "seed words loaded.")
assert NSx == NSy
if options.filename_graphX is not None:
(NGx0, NGx1) = wordsX.G.shape()
(NGy0, NGy1) = wordsY.G.shape()
assert NGx0 == NGx1, "GX is not a square adjacency matrix"
assert NGy0 == NGy1, "GY is not a square adjacency matrix"
# permute Y if rand_seed > 1, (this should only be used when testing on mock data)
# wordsY.permuteFirstWords(perm.randperm(perm.ID(Ny)))
# MU.printMatching(wordsX.words, wordsY.words, perm.ID(Ny))
return wordsX, wordsY, seed_list
# parse cmdline arguments
parser = OptionParser()
# general setting
parser.add_option('--sym', dest='sym', type="int", action='store', default=1)
parser.add_option('--stoc', dest='stochastic', type="int", action='store', default=1)
parser.add_option('--KNN', dest='KNN', type="int", action='store', default=10)
parser.add_option('--normalize', dest='normalize', type="int", action='store', default=1)
(options, args) = parser.parse_args()
return options
if __name__ == '__main__':
# parse arguments
filename_wordsX = (sys.argv[1])
# read input
wordsX = IO.readPickledWords(filename_wordsX)
options = parseOptions()
# make graph
G = makeGraph(wordsX, options)
G = G.todense()
if options.normalize == 1:
G = toSymmetricStochastic(G, sym=(options.sym == 1), stochastic=(options.stochastic == 1), norm='l1')
elif options.normalize == 2:
G = toSymmetricStochastic(G, sym=(options.sym == 1), stochastic=(options.stochastic == 1), norm='l2')
msk = MSK(None, wordsX.words, wordsX.words)
# save the matrix.
# This is hacky, since we're trusting that G is generated with rows/columns that match the order of wordsX.words
msk.M = G
