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)