def create_productions(self, terminals, formatter=None):
configs= combine(*[terminals for i in xrange(self.nvars)])
res= []
schema= self.schema
if formatter: schema= formatter.format(self)
for config in configs:
if not all((c(*config) for c in self.constraints)): continue
new_config= []
for e in config:
if e < 0:
new_config.append('m%s' % abs(e))
else:
new_config.append(e)
res.append(schema % tuple(new_config))
return res
def main():
usage= 'usage: %prog [options] infname1 [infname2 ...]'
parser= OptionParser(usage=usage)
parser.add_option('-o', '--output',
dest='output_fname', default='cluster.out',
help='output fname')
parser.add_option('-c', '--no-cache',
dest='cache', action='store_false', default=True,
help='discard cache')
parser.add_option('-k', '--resulting-dimensionality',
dest='k', default=150,
help='el numero de dimensiones resultantes')
options, args= parser.parse_args(argv[1:])
if len(args) < 1: parser.error('not enaught args')
parser= parserclass('models_cache')
infnames= args
outfname= options.output_fname
print 'parsing scores'
scores= [parser.parse(infname, cache=options.cache) for infname in infnames]
nclusterss= [2] # range(2,5)
npasss= [5,10, 15, 20, 25]
methods= ['a', 'm']
dists= ['e', 'b', 'c', 'a', 'u', 'x', 's', 'k']
configs= list(combine(nclusterss, npasss, methods, dists))
results= {}
for k in range(2,10,2):
print 'k=', k
concept_vectors= apply_lsa(scores, k)
step= len(configs)/10
for i, (nclusters, npass, method, dist) in enumerate(configs):
if (i+1)%step == 0: print '\t', ((i+1)*100)/len(configs)
r= Pycluster.kcluster(concept_vectors,
nclusters= nclusters,
method= method, dist= dist)
results[(k, nclusters, npass, method, dist)]= r
f= open('clusters_results.pickle', 'w')
pickle.dump(results, f, 2)
f.close()
