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
else:
print 'file', filename, 'not found'
D = strings.pweditdist(X, Y)
IO.writeNumpyArray(filename, D)
(cost, pi, edge_cost) = MU.ApproxMatch(D)
# TODO:
# 3. set up an initial matching based on edit distance.
return cost, pi, edge_cost
if __name__ == '__main__':
# load data
fileX = (sys.argv[1])
fileY = (sys.argv[2])
#fileX = '../SCRIPTS/matlab/Jun10_en.txt'
#fileY = '../SCRIPTS/matlab/Jun10_es.txt'
X = IO.readWords(fileX)
Y = IO.readWords(fileY)
X.features = normalize_rows(X.features)
Y.features = normalize_rows(Y.features)
(cost, pi, edge_cost) = med(X.words, Y.words)
matching = MU.getMatching(X.words, Y.words, pi, edge_cost)
Y = MU.permuteFirstWords(Y, pi)
MU.printMatching(X, Y, edge_cost)
options = Options()
options.exp_id = -1
IO.writeMatching(options, X.words, Y.words, pi, edge_cost)
import IO
import BilexiconUtil
from common import *
if __name__ == '__main__':
filename_wordsX = sys.argv[1]
filename_wordsY = sys.argv[2]
filename_lexicon = sys.argv[3]
Nseed = int(sys.argv[4])
if 'pickle' in filename_wordsX:
wordsX = IO.readPickledWords(filename_wordsX)
wordsY = IO.readPickledWords(filename_wordsY)
else:
wordsX = IO.readWords(filename_wordsX)
wordsY = IO.readWords(filename_wordsY)
if filename_lexicon == 'None': # we don't have a lexicon. assume identity.
log(100, 'Using identity lexicon')
lex = None
gold_lex = dict() #
for w in wordsX.words:
gold_lex[w] = [w]
log(100, gold_lex)
else:
lex = BilexiconUtil.readLexicon(filename_lexicon)
(gold_lex, times) = BilexiconUtil.filterLexicon(lex, wordsX.words, wordsY.words)
log(100, 'Done filtering gold lexicon')
seed = []
