def cacheOrComputeKernel(self, options, filename, f):
if options.useCache == 1 and os.path.exists(filename):
print >> sys.stderr, 'Loading kernel from file:', filename
return IO.unpickle(filename)
else:
K = f(self)
print >> sys.stderr, 'Saving kernel to file:', filename
IO.pickle(filename, K)
return K
def saveWUZ(U, W, Z, options):
S = (W, U, Z)
IO.pickle('tmp/S_t=' + str(options.t) + '.txt', S)
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
graphFilename = filename_wordsX.replace(".", "_WG.")
if options.KNN > 0:
graphFilename = graphFilename.replace(".", "_KNN"+str(options.KNN)+".")
IO.pickle(graphFilename, msk)
