def test_loglik(self):
p1 = Distributions.Kumaraswamy({'a': 2.0, 'b': 3.0})
p2 = Distributions.Kumaraswamy({'a': 1.0, 'b': 1.0})
nsamples = 1000000
data = p2.sample(nsamples)
logZ = logsumexp(p1.loglik(data) - p2.loglik(data) - np.log(nsamples))
print "Estimated partition function: ", np.exp(logZ)
self.assertTrue(
np.abs(np.exp(logZ) - 1.0) < 0.1 * self.TolParam,
'Difference in estimated partition function (1.0) greater than' +
str(0.1 * self.TolParam))
def test_estimate(self):
print "Testing parameter estimation of Kumaraswamy distribution ..."
sys.stdout.flush()
myu = 10 * rand()
mys = 10 * rand()
myB = 10 * rand()
p = Distributions.Kumaraswamy({'a': myu, 'b': mys, 'B': myB})
dat = p.sample(50000)
p = Distributions.Kumaraswamy(B=myB)
p.estimate(dat)
self.assertFalse(
np.abs(p.param['a'] - myu) > self.TolParam,
'Difference in Shape parameter for Kumaraswamy distribution greater than '
+ str(self.TolParam))
self.assertFalse(
np.abs(p.param['b'] - mys) > self.TolParam,
'Difference in Scale parameter for Kumaraswamy distribution greater than '
+ str(self.TolParam))
def test_cdf(self):
print "Testing consistency of cdf and ppf"
sys.stdout.flush()
myu = 10 * rand()
mys = 10 * rand()
myB = 10 * rand()
p = Distributions.Kumaraswamy({'a': myu, 'b': mys, 'B': myB})
u = rand(10)
u2 = p.cdf(p.ppf(u))
self.assertFalse(
sum(np.abs(u - u2)) > self.TolParam,
'Difference u - cdf(ppf(u)) greater than %.4g' % (self.Tol, ))
def test_dldtheta(self):
p = Distributions.Kumaraswamy({
'a': 5.0 * rand(),
'b': 5.0 * rand(),
'B': 10.0 * rand()
})
p.primary = ['a', 'b']
dat = p.sample(1000)
def f(arr):
p.array2primary(arr)
return np.sum(p.loglik(dat))
def df(arr):
p.array2primary(arr)
return np.sum(p.dldtheta(dat), axis=1)
arr0 = p.primary2array()
arr0 = abs(np.random.randn(len(arr0)))
err = optimize.check_grad(f, df, arr0)
print "Error in graident: ", err
self.assertTrue(
err < 1e-02,
'Gradient error %.4g is greater than %.4g' % (err, 1e-02))