operator = 'owa'
model = models['fusion']
devaluation_time = [2, 8, 16]
fig1 = figure()
ind = 1
for d in devaluation_time:
model.startExp()
for s in list(set(p_test[operator].keys()) - set(['S2'])):
# for s in p_test[operator].keys():
# for s in ['S9']:
model.setAllParameters(p_test[operator][s]['fusion'])
for i in xrange(nb_blocs):
cats.reinitialize()
cats.set_devaluation_interval(d)
model.startBloc()
for j in xrange(nb_trials):
# print cats.asso
state = cats.getStimulus(j)
# print state
action = model.chooseAction(state)
# print action, cats.actions.index(action)
reward = cats.getOutcome(state, action)
model.updateValue(reward)
# print reward
#sys.stdin.readline()
states = convertStimulus(np.array(model.state))
actions = np.array(model.action)
ax = {
k: subplot(3, len(devaluation_time), d + 1 + k * len(devaluation_time))
for k in xrange(3)
}
for s in xrange(len(s_to_plot)):
Hb = []
Hf = []
N = []
model.startExp()
model.setAllParameters(p_test[operator][s_to_plot[s]]['fusion'])
model.parameters['alpha'] = 1.0
model.parameters['beta'] = 4.38
model.parameters['gamma'] = 0.1
p = model.parameters
cats.reinitialize()
cats.set_devaluation_interval(devaluation_time[d])
model.startBloc()
for j in xrange(nb_trials):
state = cats.getStimulus(j)
action = model.chooseAction(state)
reward = cats.getOutcome(state, action)
model.updateValue(reward)
Hf.append(model.Hf)
N.append(model.nb_inferences)
# Hb.append(model.Hb)
#sys.stdin.readline()
states = convertStimulus(np.array(model.state))
actions = np.array(model.action)
responses = np.array(model.responses)
# Hbs = extractStimulusPresentation(np.array([Hb]),states,actions,responses)
for d in xrange(len(devaluation_time)):
ax = {k:subplot(3,len(devaluation_time),d+1+k*len(devaluation_time)) for k in xrange(3)}
for s in xrange(len(s_to_plot)):
Hb = []
Hf = []
N = []
model.startExp()
model.setAllParameters(p_test[operator][s_to_plot[s]]['fusion'])
model.parameters['alpha'] = 1.0
model.parameters['beta'] = 4.38
model.parameters['gamma'] = 0.1
p = model.parameters
cats.reinitialize()
cats.set_devaluation_interval(devaluation_time[d])
model.startBloc()
for j in xrange(nb_trials):
state = cats.getStimulus(j)
action = model.chooseAction(state)
reward = cats.getOutcome(state, action)
model.updateValue(reward)
Hf.append(model.Hf)
N.append(model.nb_inferences)
# Hb.append(model.Hb)
#sys.stdin.readline()
states = convertStimulus(np.array(model.state))
actions = np.array(model.action)
responses = np.array(model.responses)
# Hbs = extractStimulusPresentation(np.array([Hb]),states,actions,responses)
fig1 = figure() ind = 1 for d in devaluation_time: model.startExp() for s in list(set(p_test[operator].keys())-set(['S2'])): # for s in p_test[operator].keys(): # for s in ['S9']: model.setAllParameters(p_test[operator][s]['fusion']) for i in xrange(nb_blocs): cats.reinitialize() cats.set_devaluation_interval(d) model.startBloc() for j in xrange(nb_trials): # print cats.asso state = cats.getStimulus(j) # print state action = model.chooseAction(state) # print action, cats.actions.index(action) reward = cats.getOutcome(state, action) model.updateValue(reward) # print reward #sys.stdin.readline() states = convertStimulus(np.array(model.state)) actions = np.array(model.action)
