# -*- coding: utf-8 -*-
"""
Example experiment. Train HMMs with PC 1 to 8, k=3..20.
"""
from judgmentHMM import Experiment
arg = {"ratedTurns":'../data/complete_april_2014.csv',
"validationMethod":"lolo",
"quantizationLevel":"parameters"}
models = {"hmm_multi":{}}
args = list()
for k in xrange(3,21):
for total in xrange(2,9):
newarg = arg.copy()
params = list()
for cur in xrange(1,total+1):
params.append("pc-"+str(total)+"-"+str(cur))
newarg["modelParams"] = params
newarg["k"] = k
args.append(newarg)
exp = Experiment()
exp.run(args,models,permute=False)
# -*- coding: utf-8 -*-
"""
Example experiment. Use exactly one set of parameters, no permutation. Quantize features into 8 clusters. Use only HMM.
"""
from judgmentHMM import Experiment
arg = {"ratedTurns":'../data/complete_april_2014.csv',
"validationMethod":"lolo",
"quantizationLevel":"parameters",
"k":8,
"modelParams",['asr-conf','words-user',"SSA-WELCOME","SSA-ASKCONFIRM","SSA-ASKFORINFO","SSA-SORRY","SSA-INFO","SSA-DETAILS","SSA-NAV"]}
models = {"hmm_multi":{}}
exp = Experiment()
exp.run([a],models,permute=False)
