def __init__(self,batchsize,d,k,tau,kappa):
self.__dp=dataParse.dataParse(os.path.abspath("./data/ideas.txt"))
self.__result=self.__dp.concatedField(os.path.abspath("./data/fieldList.txt"))
#doc
self.__doc=self.__result[0]
#fielddata
self.__fid=self.__result[1]
#dictionary
self.__vocab=file(os.path.abspath('./data/vocabulary.txt')).readlines()
# the number of words in the dictionary
self.__W=len(self.__vocab)
#the number of documents to analyze in each iteration
self.__batchsize=batchsize
# the total number of documents
self.__D=d
# the number of topics
self.__K=k
# the number of iterations
self.__documentstoanalyze=self.__D/self.__batchsize
# tau
self.__tau=tau
# kappa
self.__kappa=kappa
# lda instance (alpha=1/K, eta=1/K, tau_0=1024, kappa=0.1)
self.__ldaObj=onlineldavb.OnlineLDA(self.__vocab, self.__K, self.__D, 1./self.__K, 1./self.__K, self.__tau*1.0, self.__kappa)
def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
dp=dataParse.dataParse("../../data/ideas.txt")
des=dp.fieldParse("description")
# The number of documents to analyze each iteration
batchsize = 10
# The total number of documents in Wikipedia
D = 5000
# The number of topics
K = 100
# How many documents to look at
if (len(sys.argv) < 2):
documentstoanalyze = int(D/batchsize)
else:
documentstoanalyze = int(sys.argv[1])
# Our vocabulary
vocab = file('./dictnostops.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(0, D):
#Retrieve texts
docset=des
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
if (iteration % 10 == 0):
numpy.savetxt('lambda-%d.dat' % iteration, olda._lambda)
numpy.savetxt('gamma-%d.dat' % iteration, gamma)
def __init__(self,docNum):
self.__dp=dataParse.dataParse(os.path.abspath("./data/ideas.txt"))
self.__docNum=docNum
self.__tags=self.__dp.fieldParse("tags")
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import socket
import select
import Queue
import time
import os
import glVariable
import dataParse
dataParse = dataParse.dataParse()
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setblocking(False)
server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_address = ("0.0.0.0", 14567)
server.bind(server_address)
server.listen(10)
inputs = [server]
outputs = []
#message_queues = {}
print ("begining")
print (glVariable.seqNum)
glVariable.seqNum += 1
def __init__(self, docNum):
self.__dp = dataParse.dataParse(os.path.abspath("./data/ideas.txt"))
self.__docNum = docNum
self.__tags = self.__dp.fieldParse("tags")
