def process_args():
opts, args = getopt.getopt(sys.argv[1:], "glnd")
for o, a in opts:
if o == "-g": interaction.enable_gpu()
if o == "-n": RUNCONFIG['update_networks'] = 1
if o == "-d": RUNCONFIG['run_developer'] = 1
if o == "-l": RUNCONFIG['lottery_choices'] = 1
return args
def process_args():
opts, args = getopt.getopt(sys.argv[1:], "glnd")
for o, a in opts:
if o == "-g": interaction.enable_gpu()
if o == "-n": RUNCONFIG['update_networks'] = 1
if o == "-d": RUNCONFIG['run_developer'] = 1
if o == "-l": RUNCONFIG['lottery_choices'] = 1
return args
from synthicity.urbansim import interaction, mnl
import numpy as np, pandas as pd
GPU = 0
if GPU: interaction.enable_gpu()
EMTOL = 1e-03
MAXITER = 10000
def lcmnl_estimate(cmdata,numclasses,csdata,numalts,chosen,maxiter=MAXITER,emtol=EMTOL):
loglik = -999999
beta = [np.ones(csdata.shape[1]) for i in range(numclasses)]
#cmbeta = np.ones((cmdata.shape[1]+1)*(numclasses-1)) # +1 if for asc, -1 is base alt
cmbeta = np.ones(cmdata.shape[1])
for i in range(maxiter):
print "Running iteration %d" % (i+1)
# EXPECTATION
print "Running class membership model"
print "cmbeta", cmbeta
cmprobs = mnl.mnl_simulate(cmdata,cmbeta,numclasses,GPU=GPU,returnprobs=1)
print "cmprobs", cmprobs
csprobs = []
for cno in range(numclasses):
print "Running class specific model for class %d" % (cno+1)
print "csbeta", beta[cno]
tmp = mnl.mnl_simulate(csdata,beta[cno],numalts,GPU=GPU,returnprobs=1)
print "csprobs", tmp