priors = ["Gauss(%f,%f)" % (m, m), "Gauss(%f,%f)" % (w, w*2), "Beta(2,50)", "Beta(1,50)"] #print 'foo' #print sfi.mapestimate(data, nafc=model['nafc'], sigmoid=model['sigmoid'], core=model['core'], priors=priors) # def print_data(data): print "======================================================================" for dat in data: print dat[0], dat[1], dat[2] def run(): ob = po.Observer(*params, **model) data = ob.DoAnExperiment(levels, nblocks) Bi = pp.BayesInference(data, sigmoid=model['sigmoid'], core=model['core'], nafc=model['nafc'], priors=priors, verbose=True) ci = Bi.getCI(cut=0.5) if ci[0] - ci[2] == 0: print "warning, confidence interval has zero length!" print_data(data) for i in range(1000): pp.set_seed(i) print "iteration: ", i, run()
#print sfi.mapestimate(data, nafc=model['nafc'], sigmoid=model['sigmoid'], core=model['core'], priors=priors) # def print_data(data): print "======================================================================" for dat in data: print dat[0], dat[1], dat[2] def run(): ob = po.Observer(*params, **model) data = ob.DoAnExperiment(levels, nblocks) Bi = pp.BayesInference(data, sigmoid=model['sigmoid'], core=model['core'], nafc=model['nafc'], priors=priors, verbose=True) ci = Bi.getCI(cut=0.5) if ci[0] - ci[2] == 0: print "warning, confidence interval has zero length!" print_data(data) for i in range(1000): pp.set_seed(i) print "iteration: ", i, run()
def check_int(value): try: int(value) return True except ValueError: return False if options.seed not in ["fixed", "time"] and not check_int(options.seed): raise ValueError("'seed' must be either 'fixed', 'time' or an integer value.") elif options.seed == 'fixed': print "Seed is default." elif options.seed == 'time': seed = int(time.time()) print "Seed is time since epoch in seconds: '%d'" % seed pypsignifit.set_seed(seed) options.seed = seed else: seed = int(options.seed) print "Seeed is value given on command line: '%d'" % seed pypsignifit.set_seed(seed) # check that there is something to do bayes = options.bayes nonparametric = options.nonparametric parametric = options.parametric if not nonparametric and not parametric and not bayes: raise ValueError("You must specify one of: 'nonparametric', 'parametric' "+\ "'bayes' in order for this script to do anything!") # set the version
try: int(value) return True except ValueError: return False if options.seed not in ["fixed", "time"] and not check_int(options.seed): raise ValueError( "'seed' must be either 'fixed', 'time' or an integer value.") elif options.seed == 'fixed': print "Seed is default." elif options.seed == 'time': seed = int(time.time()) print "Seed is time since epoch in seconds: '%d'" % seed pypsignifit.set_seed(seed) options.seed = seed else: seed = int(options.seed) print "Seeed is value given on command line: '%d'" % seed pypsignifit.set_seed(seed) # check that there is something to do bayes = options.bayes nonparametric = options.nonparametric parametric = options.parametric if not nonparametric and not parametric and not bayes: raise ValueError("You must specify one of: 'nonparametric', 'parametric' "+\ "'bayes' in order for this script to do anything!") # set the version