dest="modelFile",
default="",
help="the stored model file")
(options, args) = parser.parse_args()
#Set the log level
log_level = options.loglevel
numeric_level = getattr(logging, log_level, None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level)
C = int(options.C)
model = MME.importFile(options.modelFile)
dataset = []
for row in sys.stdin:
splitrow = row.split("\t")
dataset.append(map(int, splitrow))
#for n in range(0, len(dataset)):
# counts = dataset[n]
# print str(n) + "\t" + str(MME.assignComponentToCounts(counts, model))
# print file for google docs
#print "component\t",
#for i in range(0, C): print str(i) + "\t",
#print ""
#print "prior\t" + "\t".join(map(str, finalModel.mixture))
#!/usr/bin/python
import multinomialMixtureEstimation as MME
import logging
logging.basicConfig(level=logging.DEBUG)
model = MME.importFile("sampleModel.txt")
dataset = []
for i in range(0, 500): dataset.append(model.sampleRow(8))
hyperP = MME.MultinomialMixtureModelHyperparams(2, 3, [1, 1], [1, 1, 1])
finalModel = MME.computeDirichletMixture(dataset, hyperP, 10)
print "Final Model:"
print finalModel.mixture
print finalModel.multinomials
#!/usr/bin/python
import multinomialMixtureEstimation as MME
model = MME.importFile("multinomialMixtureExample.txt")
for i in range(0, 500):
sample = model.sampleRow(8)
print("\t".join(map(str, sample)))
parser.add_option("-L", '--loglevel', action="store", dest="loglevel", default='DEBUG', help="don't print status messages to stdout")
parser.add_option("-C", '--numComponents', action="store", dest="C", default="1", help="the number of components in the mixture model")
parser.add_option("-m", '--modelFile', action="store", dest="modelFile", default="", help="the stored model file")
(options, args) = parser.parse_args()
#Set the log level
log_level = options.loglevel
numeric_level = getattr(logging, log_level, None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level)
C = int(options.C)
model = MME.importFile(options.modelFile)
dataset = []
for row in sys.stdin:
splitrow = row.split("\t")
dataset.append(map(int, splitrow))
#for n in range(0, len(dataset)):
# counts = dataset[n]
# print str(n) + "\t" + str(MME.assignComponentToCounts(counts, model))
# print file for google docs
#print "component\t",
#for i in range(0, C): print str(i) + "\t",
#print ""
#print "prior\t" + "\t".join(map(str, finalModel.mixture))
#!/usr/bin/python
import multinomialMixtureEstimation as MME
import logging
logging.basicConfig(level=logging.DEBUG)
model = MME.importFile("sampleModel.txt")
dataset = []
for i in range(0, 500):
dataset.append(model.sampleRow(8))
hyperP = MME.MultinomialMixtureModelHyperparams(2, 3, [1, 1], [1, 1, 1])
finalModel = MME.computeDirichletMixture(dataset, hyperP, 10)
print "Final Model:"
print finalModel.mixture
print finalModel.multinomials
