def parseCommandLine (D,argv):
if len(argv) != 2 and len(argv) != 3:
print("Error - usage is " + argv[0] + " <data_file> <startingdatapoints> ")
sys.exit(1)
RP = RepPoints.RepPoints()
EmAlg = cEM(D)
if len(argv)>2:
f = open(argv[2],"r")
EmAlg = pickle.load(f)
EmAlg.EM(len(D.classlist))
EmAlg.bPPC = True
emclusts = RP.createClusters(EmAlg)
RP.repPoints(EmAlg, emclusts)
else:
EmAlg.EM(len(D.classlist))
EmAlg.bPPC = True
# Creates clusters depending on what EM guessed.
emclusts = RP.createClusters(EmAlg)
# Finds the outerpoints and the midpoints and assigns them in emclusters.
RP.repPoints(EmAlg, emclusts)
# This makes the algorithm start with good initial points.
starter = Starts.starts()
EmAlg = starter.goodInitial(D, EmAlg, emclusts, RP, 0)
f = open("pickles/"+argv[1].split('/')[-1]+"pickle","w")
pickle.dump(EmAlg,f)
EmAlg.EM(len(D.classlist))
f.close()
return EmAlg
def scenario2(D, EMStarts, sFileStub):
f = open(sFileStub + ".scen2.results", "w")
dEMResults = {} # from option string to list of EMs
f.write("queries,cons,likelihood,NMI\n")
for option in StarterOptions.lOptions:
EMOptionResults = []
f.write(StarterOptions.lOptionNames[option] + "\n")
for iOutertrial in range(numOuterTrials):
f.write("outertrial: %d\n" % iOutertrial)
f.flush()
# run the initial points algorithm several
# times for this set of options and this initial point
#for iRestart in range(numInnerTrials):
# get starting EM
em = copy.deepcopy(EMStarts[iOutertrial])
em.bPPC = True
# setup and run goodInitial
RP = RepPoints.RepPoints()
emclusts = RP.createClusters(em)
RP.repPoints(em, emclusts)
starter = Starts.starts()
newEM = starter.goodInitial(D, em, emclusts, RP, f)
EMOptionResults.append(newEM)
dEMResults[StarterOptions.lOptionNames[option]] = EMOptionResults
f.close()
return dEMResults
