def buildMixtureTreeMaxKL(data, K, iterations, maxKL, branchesPerNode = 2, multinomial = None):
logging.info("Dataset Size: " + str(len(data)))
if (multinomial):
(worstKL, worstN) = MME.worstFitForSingleMultinomial(data, multinomial)
logging.info("Worst KL found in branch: " + str(worstKL))
if (worstKL < maxKL): return None
if (len(data) < branchesPerNode): return None
# hyperparameters are fixed here:
hyperP = MME.MultinomialMixtureModelHyperparams(branchesPerNode, K, [1.0 / branchesPerNode]*branchesPerNode, [1.0 / K]*K)
mixtureModel = MME.computeDirichletMixture(data, hyperP, iterations)
smallerDatasets = []
for c in range(0, hyperP.C): smallerDatasets.append([])
for counts in data:
c = MME.assignComponentToCounts(counts, mixtureModel)
smallerDatasets[c].append(counts)
treeModel = MultinomialMixtureTree(mixtureModel)
for c in range(0, hyperP.C):
smallerDataset = smallerDatasets[c]
child = buildMixtureTreeMaxKL(smallerDataset, K, iterations, maxKL, branchesPerNode, mixtureModel.multinomials[c])
treeModel.mixtureNodes[c] = child
return treeModel
def buildSimpleMixtureTree(data, K, iterations, height, branchesPerNode = 2):
if (height == 0): return None
# hyperparameters are fixed here:
hyperP = MME.MultinomialMixtureModelHyperparams(branchesPerNode, K, [1.0 / branchesPerNode]*branchesPerNode, [1.0 / K]*K)
mixtureModel = MME.computeDirichletMixture(data, hyperP, iterations)
smallerDatasets = []
for c in range(0, hyperP.C): smallerDatasets.append([])
for counts in data:
c = MME.assignComponentToCounts(counts, mixtureModel)
smallerDatasets[c].append(counts)
treeModel = MultinomialMixtureTree(mixtureModel)
for c in range(0, hyperP.C):
smallerDataset = smallerDatasets[c]
child = buildSimpleMixtureTree(smallerDataset, K, iterations, height - 1, branchesPerNode)
treeModel.mixtureNodes[c] = child
return treeModel
#!/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
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level)
C = int(options.C)
iterations = int(options.I)
print "init dataset"
dataset = []
N = 0
for row in sys.stdin:
if (N % 100000 == 0): print "processed " + str(N) + " rows."
splitrow = row.split("\t")
dataset.append(map(int, splitrow))
N += 1
print "finished dataset"
hyperP = MME.MultinomialMixtureModelHyperparams(C, 168, [1] * C, [1] * 168)
finalModel = MME.computeDirichletMixture(dataset, hyperP, iterations)
logging.debug("Final Model:")
outputModel = sys.stdin
if (options.outputModel): outputModel = open(options.outputModel, 'w')
finalModel.outputToFile(outputModel)
finalModel.outputToTSV(sys.stdout)
(worseLogProb, worstN, worstC) = MME.worstFit(dataset, finalModel)
print "worstLogProb", worseLogProb
print "worst N", worstN
print "worst C", worstC
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level)
C = int(options.C)
iterations = int(options.I)
print "init dataset"
dataset = []
N = 0
for row in sys.stdin:
if (N % 100000 == 0): print "processed " + str(N) + " rows."
splitrow = row.split("\t")
dataset.append(map(int, splitrow))
N += 1
print "finished dataset"
hyperP = MME.MultinomialMixtureModelHyperparams(C, 168, [1]*C, [1]*168)
finalModel = MME.computeDirichletMixture(dataset, hyperP, iterations)
logging.debug("Final Model:")
outputModel = sys.stdin
if (options.outputModel): outputModel = open(options.outputModel, 'w')
finalModel.outputToFile(outputModel)
finalModel.outputToTSV(sys.stdout)
(worseLogProb, worstN, worstC) = MME.worstFit(dataset, finalModel)
print "worstLogProb", worseLogProb
print "worst N", worstN
print "worst C", worstC
#!/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
