def getMixtures(dates, network,
max_n_links=None, return_tts=False, max_nb_mixture=4,num_threads=1):
tic("Running with {0} jobs.".format(num_threads),"getMixtures")
ttob_seqs = (ttob_seq for date in dates
for ttob_seq in getDayTTOBservations(date, network))
# Get travel times for each link
all_ttobs = (ttob for ttob_seq in ttob_seqs for ttob in ttob_seq)
tic("starting groupby...","getMixtures")
all_ttobs_by_lid = sorted([(lid, list(vals)) for (lid, vals) in groupby(all_ttobs, lambda ttob:ttob.linkId)], key=lambda z:-len(z[1]))
tic("groupby done, {0} links".format(len(all_ttobs_by_lid)),"getMixtures")
if max_n_links:
all_ttobs_by_lid = all_ttobs_by_lid[:max_n_links]
tts_by_link = [(lid, np.array([tto.tt for tto in vals])) for (lid, vals) in all_ttobs_by_lid]
tic("vectorization done","getMixtures")
if num_threads != 1:
tic("Running with {0} jobs.".format(num_threads),"getMixtures")
learned_mixtures = Parallel(n_jobs=num_threads,verbose=10)(delayed(getMixtures_inner)((lid, tts, max_nb_mixture)) for (lid, tts) in tts_by_link)
else:
learned_mixtures = [(lid, learnMixtureAuto(tts, max_nb_mixture)) for (lid, tts) in tts_by_link]
if return_tts:
return (dict(learned_mixtures), dict(tts_by_link))
else:
return dict(learned_mixtures)
def getMixtures_inner(args): (lid,tts,max_nb_mixture) = args return (lid, learnMixtureAuto(tts, max_nb_mixture))
