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_nsamples(self):
interface.bootstrap(data, nsamples=666)
def test_start(self):
interface.bootstrap(data, nsamples=25, start=[0.1, 0.2, 0.3])
def test_basic(self):
interface.bootstrap(data)
def test_cuts(self):
interface.bootstrap(data, nsamples=25, cuts=[0.5,0.6,0.75])
def test_basic(self):
interface.bootstrap(data)
def test_core(self):
interface.bootstrap(data, nsamples=25, core='linear')
def test_core(self):
interface.bootstrap(data, nsamples=25, core='linear')
def test_nafc(self):
interface.bootstrap(data, nafc=23)
def test_sigmoid(self):
interface.bootstrap(data, nsamples=25, sigmoid='gumbel_l')
def test_nsamples(self):
interface.bootstrap(data, nsamples=666)
def test_start(self):
interface.bootstrap(data, nsamples=25, start=[0.1, 0.2, 0.3])
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_nafc(self):
interface.bootstrap(data, nafc=23)
def test_prior(self):
priors = ('Gauss(0,10)', 'Gamma(2,3)', 'Uniform(1,5)')
interface.bootstrap(data, nsamples=25, priors=priors)
def test_sigmoid(self):
interface.bootstrap(data, nsamples=25, sigmoid='gumbel_l')
def test_cuts(self):
interface.bootstrap(data, nsamples=25, cuts=[0.5, 0.6, 0.75])
def test_prior(self):
priors = ('Gauss(0,10)', 'Gamma(2,3)', 'Uniform(1,5)')
interface.bootstrap(data, nsamples=25, priors=priors)
def test_parameteric(self):
interface.bootstrap(data, nsamples=25, parametric=False)
def test_parameteric(self):
interface.bootstrap(data, nsamples=25, parametric=False)
def sample ():
boots = interface.bootstrap ( d, priors=priors, nsamples=1500-2*k )
mape = interface.mapestimate ( d, priors=priors )
mcmc = interface.mcmc ( d, start=(4,2,.02), priors=priors, nsamples = 1500-2*k )
diag = interface.diagnostics ( d, (4,1,.02) )
return float(os.popen ( "ps -C python -o rss" ).readlines()[1])/1024
