for _ in trange(iterations_num):
# get stimulus from psi object
rod, frame = psi.stim
# get response from the generative model
response = genAgent.getResponses(rod, frame, 1)
# plot selected stimuli
plotter.plotStimuli()
# M: Plot RMSE for thresholds; first argument is the predicted treshold,
# second argument is the target threshold.
a=np.asarray(psi.CDFTable)
b= np.asarray(genAgent.PSETable)
# a is basically nothing no since, it is flattened and meaned
plotter.plotRSME(a.flatten().mean(), b)
# plot updated parameter values based on mean and MAP
plotter.plotParameterValues()
# the parameter distributions may be plotted at most once (so comment out at least one)
# plot parameter distributions of current trial
# plotter.plotParameterDistributions()
# plot parameter distributions of each trial as surfaces
plotter.plotParameterDistributions(projection='3d')
# the negative log likelihood may be plotted at most once (so comment out at least one)
# plot negative log likelihood of responses thus far as a contour plot
# 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
# get response from the generative model
response = genAgent.getResponses(rod, frame, 1)
# plot selected stimuli
plotter.plotStimuli()
# M: Plot RMSE for thresholds; first argument is the predicted treshold,
# second argument is the target threshold.
plotter.plotRSME(psi.response_array, genAgent.PSETable)
# plot updated parameter values based on mean and MAP
plotter.plotParameterValues()
# the parameter distributions may be plotted at most once (so comment out at least one)
# plot parameter distributions of current trial
# plotter.plotParameterDistributions()
# plot parameter distributions of each trial as surfaces
plotter.plotParameterDistributions(projection='3d')
# the negative log likelihood may be plotted at most once (so comment out at least one)
# plot negative log likelihood of responses thus far as a contour plot
# get response from the generative model
response = genAgent.getResponses(rod, frame, 1)
# plot selected stimuli
plotter.plotStimuli()
# M: Plot RMSE for thresholds; first argument is the predicted threshold,
# second argument is the target threshold.
# M: PSE from GenAg is the target threshold, the CDF PSE is the predicted threshold.
CDFArray = np.asarray(psi.CDFTable)
# Reduce dimensionality by summing over all columns.
CDFArrayMean = CDFArray.mean(axis=0)
PSEArray = np.asarray(genAgent.PSETable)
# PSEArray2= PSEArray.mean(axis=0)
plotter.plotRSME(CDFArray, PSEArray)
#print(CDFArray)
print(CDFArray)
# print(psi.CDFTable)
# print(genAgent.PSETable)
#plotter.CDFPlotter(CDFArrayMean)
# print('cdf', psi.CDFTable)
# plot updated parameter values based on mean and MAP
plotter.plotParameterValues()
# the parameter distributions may be plotted at most once (so comment out at least one)
# plot parameter distributions of current trial
# plotter.plotParameterDistributions()
