def __genFeature(self, num_of_homo_feats):
###################### TODO ######################
print "generate h features"
qry = self.qry
doc = self.doc
homo_feats = {}
df = ProcDoc.docFreq(doc)
for q_id, q_terms in qry.items():
npscq = np.asarray([self.scq(df, q_term) for q_term in q_terms])
homo_feats[q_id] = np.asarray([
np.sum(npscq),
np.amax(npscq),
np.amin(npscq),
np.mean(npscq)
])
# np.sum(a)
# np.amax(a)
# np.amin(a)
# np.mean(a)
# a.prod()**(1.0/len(a))
# len(a) / np.sum(1.0/a)
# var = variation(a, axis=0) idmax = np.argmax(var)
return homo_feats
def __genFeature(self, num_of_homo_feats):
print "generate h features"
qry = self.qry
doc = self.doc
homo_feats = {}
df = ProcDoc.docFreq(doc)
for q_id, q_terms in qry.items():
npscq = np.asarray([self.__scq(df, q_term) for q_term in q_terms])
harm_mean = self.__harm_mean(npscq)
geo_mean = self.__geo_mean(npscq)
homo_feats[q_id] = np.asarray([
np.std(npscq),
np.sum(npscq),
np.amax(npscq),
np.amin(npscq),
np.mean(npscq), harm_mean, geo_mean
])
return homo_feats
