def test_estimate(self):
print "Testing parameter estimation of LogNormal distribution ..."
sys.stdout.flush()
myu = 10 * np.random.rand(1)[0]
mys = 10 * np.random.rand(1)[0]
p = Distributions.LogNormal({'mu': myu, 's': mys})
dat = p.sample(1000000)
p = Distributions.LogNormal()
p.estimate(dat)
self.assertFalse( np.abs(p.param['mu'] - myu) > self.TolParam,\
'Difference in location parameter for LogNormal distribution greater than ' + str(self.TolParam))
self.assertFalse( np.abs(p.param['s'] - mys) > self.TolParam,\
'Difference in scale parameter for LogNormal distribution greater than ' + str(self.TolParam))
def test_derivatives(self):
print "Testing derivatives w.r.t. data ... "
sys.stdout.flush()
myu = 3.0 * np.random.rand(1)[0] + 1.0
mys = 3.0 * np.random.rand(1)[0] + 1.0
p = Distributions.LogNormal({'mu': myu, 's': mys})
dat = p.sample(100)
h = 1e-7
tol = 1e-2
y = np.array(dat.X) + h
df = p.dldx(dat)
df2 = (p.loglik(Data(y)) - p.loglik(dat)) / h
self.assertFalse( np.max(np.abs(df-df2)) > tol,\
'Difference ' + str(np.max(np.abs(df-df2))) + ' in derivative of log-likelihood for LogNormal greater than ' + str(tol))
