# initialize generative agent
genAgent = GenerativeAgent(params_gen, stimuli)
# initialize psi object
psi = PSI_RiF(params, stimuli)
# number of iterations of the experiment
iterations_num = 500
# initialize plotter and plot generative distribution, generative weights and the negative log likelihood
plotter = Plotter(params, params_gen, stimuli, genAgent, psi, iterations_num)
plotter.plotGenProbTable()
plotter.plotGenVariances()
plotter.plotGenWeights()
plotter.plotGenPSE()
plotter.plotNegLogLikelihood(responses_num=500)
plotter.plot()
for stim_selection in ['adaptive', 'random']:
# set stimulus selection mode and reset psi object to initial values
psi.reset(stim_selection)
# reset plotter to plot new figures
plotter.reset()
# run model for given number of iterations
print 'inferring model ' + stim_selection + 'ly'
for _ in trange(iterations_num):
# get stimulus from psi object
rod, frame = psi.stim
genAgent = GenerativeAgent(params_gen, stimuli)
# number of iterations of the experiment
iterations_num = 100
# initialize psi object
# M: Add iterations for prior tracker
psi = PSI_RiF(params, stimuli, iterations_num)
# initialize plotter and plot generative distribution, generative weights and the negative log likelihood
plotter = Plotter(params, params_gen, stimuli, genAgent, psi, iterations_num)
plotter.plotGenProbTable()
plotter.plotGenVariances()
plotter.plotGenWeights()
plotter.plotGenPSE()
plotter.plotNegLogLikelihood(responses_num=500)
plotter.plot()
# M: Normal adaptive use 'adaptive'
# M: Product entropy use 'adaptive2'
# M: Backward induction use 'backward'
# for stim_selection in [ 'adaptive', 'adaptive2', 'backward']:
for stim_selection in ['adaptive2', 'backward']:
# set stimulus selection mode and reset psi object to initial values
psi.reset(stim_selection)
# reset plotter to plot new figures
plotter.reset()
# run model for given number of iterations
