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
