def extended_helper(wrapper):
stepwidths = (1.,1.,0.01)
sfr.setSeed(1)
priors = ('Gauss(0,10)', 'Gamma(2,3)', 'Uniform(1,5)')
return wrapper.mcmc(data, start=[0.1,0.2,0.3], nsamples=1000, nafc=4,
sigmoid="gumbel_r", core="ab", priors=priors,
stepwidths=stepwidths)
def setUp ( self ):
sft.setSeed(0)
nafc = 2
stimulus_intensities = [0.0,2.0,4.0,6.0,8.0,10.0]
number_of_correct = [34,32,40,48,50,48]
number_of_trials = [50]*len(stimulus_intensities)
data = zip(stimulus_intensities,number_of_correct,number_of_trials)
self.mcmc = pd.BayesInference ( data, priors=("Gauss(0,100)","Gamma(1.01,200)","Beta(2,30)") )
def setUp ( self ):
sft.setSeed(0)
nafc = 2
stimulus_intensities = [0.0,2.0,4.0,6.0,8.0,10.0]
number_of_correct = [34,32,40,48,50,48]
number_of_trials = [50]*len(stimulus_intensities)
data = zip(stimulus_intensities,number_of_correct,number_of_trials)
self.parametric = pd.BootstrapInference ( data, priors=("","","Beta(2,30)"), parametric=True )
self.nonparametric = pd.BootstrapInference ( data, priors=("","","Beta(2,30)"), parametric=False )
def test_old_doctest(self):
x = [float(2*k) for k in xrange(6)]
k = [34,32,40,48,50,48]
n = [50]*6
d = [[xx,kk,nn] for xx,kk,nn in zip(x,k,n)]
priors = ('flat','flat','Uniform(0,0.1)')
sfr.setSeed(1)
samples,est,D,thres,thbias,thacc,slope,slbias,slacc,Rkd,Rpd,out,influ = interface.bootstrap(d,nsamples=2000,priors=priors)
self.assertAlmostEqual( np.mean(est[:,0]), 2.7537742610139397, places=2)
self.assertAlmostEqual( np.mean(est[:,1]), 1.4072288392075412, places=2)
def test_old_doctest(self):
x = [float(2*k) for k in xrange(6)]
k = [34,32,40,48,50,48]
n = [50]*6
d = [[xx,kk,nn] for xx,kk,nn in zip(x,k,n)]
priors = ('Gauss(0,1000)','Gauss(0,1000)','Beta(3,100)')
stepwidths = (1.,1.,0.01)
sfr.setSeed(1)
(estimates, deviance, posterior_predictive_data,
posterior_predictive_deviances, posterior_predictive_Rpd,
posterior_predictive_Rkd, logposterior_ratios, accept_rate) = interface.mcmc(d,nsamples=10000,priors=priors,stepwidths=stepwidths)
self.assertAlmostEqual( np.mean(estimates[:,0]), 2.52579214385 )
self.assertAlmostEqual( np.mean(estimates[:,1]), 7.30680631915 )
def test_old_doctest(self):
x = [float(2 * k) for k in xrange(6)]
k = [34, 32, 40, 48, 50, 48]
n = [50] * 6
d = [[xx, kk, nn] for xx, kk, nn in zip(x, k, n)]
priors = ('flat', 'flat', 'Uniform(0,0.1)')
sfr.setSeed(1)
samples, est, D, thres, thbias, thacc, slope, slbias, slacc, Rkd, Rpd, out, influ = interface.bootstrap(
d, nsamples=2000, priors=priors)
self.assertAlmostEqual(np.mean(est[:, 0]),
2.7537742610139397,
places=2)
self.assertAlmostEqual(np.mean(est[:, 1]),
1.4072288392075412,
places=2)
def test_old_doctest(self):
x = [float(2 * k) for k in xrange(6)]
k = [34, 32, 40, 48, 50, 48]
n = [50] * 6
d = [[xx, kk, nn] for xx, kk, nn in zip(x, k, n)]
priors = ('Gauss(0,1000)', 'Gauss(0,1000)', 'Beta(3,100)')
stepwidths = (1., 1., 0.01)
sfr.setSeed(1)
(estimates, deviance, posterior_predictive_data,
posterior_predictive_deviances, posterior_predictive_Rpd,
posterior_predictive_Rkd, logposterior_ratios,
accept_rate) = interface.mcmc(d,
nsamples=10000,
priors=priors,
stepwidths=stepwidths)
self.assertAlmostEqual(np.mean(estimates[:, 0]), 2.5463976926832483)
self.assertAlmostEqual(np.mean(estimates[:, 1]),
7.335732619111738,
places=3)
def test_set_seed(self):
sfr.setSeed(1)
def test_set_seed(self):
sfr.setSeed(1)
def basic_helper(wrapper):
priors = ('Gauss(0,1000)','Gauss(0,1000)','Beta(3,100)')
stepwidths = (1.,1.,0.01)
sfr.setSeed(1)
return wrapper.mcmc(data, nsamples=1000, priors=priors, stepwidths=stepwidths)
def extended_helper(wrapper):
priors = ('Gauss(0,10)', 'Gamma(2,3)', 'Uniform(1,5)')
sfr.setSeed(1)
return wrapper.bootstrap(data, start=[0.1, 0.2, 0.3], nsamples=100, nafc=4,
sigmoid="gumbel_l", core="linear", priors=priors,
cuts=[0.5,0.6,0.75])
def basic_helper(wrapper):
priors = ('flat','flat','Uniform(0,0.1)')
sfr.setSeed(1)
return wrapper.bootstrap(data,nsamples=2000,priors=priors)
def helper(wrapper):
sfr.setSeed(6)
return wrapper.mcmc(data, nsamples=20)
