def decode_sen_no(self, s):
self.current_list_cl = self.data.crowdlabs[s]
sentence = self.data.sentences[s]
ml_states = self.decode(util.get_obs(
sentence), include_crowd_obs=True)
return ml_states
def run_test(test, h):
cnt = 0
correct = 0
for s in test:
x = util.get_obs(s)
g = util.get_lab(s)
p = h.decode(x)
for i, j in zip(g, p):
cnt += 1
if i == j:
correct += 1
print correct * 1.0 / cnt
def mls(self):
"""
compute the most likely states seq for all sentences
:return:
"""
self.res = []
for s, sentence in enumerate(self.data.sentences):
if len(sentence) > 0:
self.current_list_cl = self.data.crowdlabs[s]
ml_states = self.decode(util.get_obs(
sentence), include_crowd_obs=True)
self.res.append(ml_states)
else:
self.res.append([])
}
freqs = {
_CONCEDER: [1, 1, 1, 1, 1, 1, 1],
_HARDHEADED: [1, 1, 1, 1, 1, 1, 1],
_TFT: [1, 1, 1, 1, 1, 1, 1],
_RANDOM: [1, 1, 1, 1, 1, 1, 1]
}
fileNames = os.listdir(training_folder)
testFileName = ""
for name in fileNames:
strategy_names = name.split('.')[0].split('_')
strat1 = u.get_strat_name(strategy_names[0])
strat2 = u.get_strat_name(strategy_names[1])
obs = u.get_obs(name)
for i in range(len(obs)):
a1 = obs[i][0]
a2 = obs[i][1]
a1 = u.movetype_to_number(a1)
a2 = u.movetype_to_number(a2)
freqs[strat1][a1] += 1
freqs[strat2][a2] += 1
print("Concede-selfish-fortune-unfortune-nice-silent-unchange")
for k in freqs.keys():
freqs[k] = u.normalize(freqs[k])
for k in freqs.keys():
def decode_all(self, sentences):
self.res = []
for s in sentences:
mls = self.decode(util.get_obs(s))
self.res.append(mls)
def get_tag_hc(hc, sen):
return hc.decode(util.get_obs(sen))
for name in fileNames:
# reset the frequencies count, except for the tit-for-tat strategy
freqs[_CONCEDER] = [0, 0, 0, 0, 0, 0, 0]
freqs[_HARDHEADED] = [0, 0, 0, 0, 0, 0, 0]
freqs[_RANDOM] = [0, 0, 0, 0, 0, 0, 0]
# store the previous evidence probabilities matrix
old_prob = prob
# extrapolate the strategy IDs from the file name
strategy_names = name.split('.')[0].split('_')
strat1 = u.get_strat_name(strategy_names[0])
strat2 = u.get_strat_name(strategy_names[1])
# define the observations vector. It would contain couple of moves: [agent1_move, agent2_move]
obs = u.get_obs(folderPath + name)
# extrapolate the moves of agent1 and store them
o1 = []
for o in obs:
o1.append(o[0])
observations[strat1].append(o1)
# extrapolate the moves of agent2 and store them
o2 = []
for o in obs:
o2.append(o[1])
observations[strat2].append(o2)
# count how many moves of each type the agents did
for i in range(len(obs)):