def test_estimate(self):
print "Testing parameter estimation of Gamma distribution ..."
sys.stdout.flush()
myp = 2.0 * np.random.rand(1)[0] + .5
mys = 10.0 * np.random.rand(1)[0]
p1 = Distributions.ExponentialPower({'p': myp, 's': mys})
dat = p1.sample(50000)
myp = 2.0 * np.random.rand(1)[0] + .5
mys = 10.0 * np.random.rand(1)[0]
p2 = Distributions.ExponentialPower({'p': myp, 's': mys})
p2.estimate(dat)
prot = {}
prot[
'message'] = 'Difference in parameters for Exponential Power distribution greater than threshold'
prot['s-threshold'] = self.TolParamS
prot['p-threshold'] = self.TolParamP
prot['true model'] = p1
prot['estimated model'] = p2
self.assertTrue(np.abs(p2.param['p'] - p1.param['p']) < self.TolParamP or np.abs(p2.param['s'] - p1.param['s']) < self.TolParamS,\
Auxiliary.prettyPrintDict(prot))
def test_derivatives(self):
print "Testing derivatives w.r.t. data ... "
sys.stdout.flush()
P = []
for k in range(10):
myp = 2.0 * np.random.rand(1)[0] + .5
mys = 3.0 * np.random.rand(1)[0] + 1.0
p = Distributions.ExponentialPower({'p': myp, 's': mys})
P.append(p)
p = Distributions.ProductOfExponentialPowerDistributions({'P': P})
dat = p.sample(100)
h = 1e-7
tol = 1e-4
Y0 = dat.X.copy()
df = p.dldx(dat)
df2 = 0.0 * df
for i in xrange(dat.size(0)):
y = Y0.copy()
y[i, :] = y[i, :] + h
df2[i, :] = (p.loglik(Data(y)) - p.loglik(dat)) / h
prot = {}
prot[
'message'] = 'Difference in derivative of log-likelihood for PowerExponential greater than ' + str(
tol)
prot['max difference'] = np.max(np.abs((df - df2).flatten()))
prot['mean difference'] = np.mean(np.abs((df - df2).flatten()))
self.assertTrue(
np.max(np.abs(df - df2)) < tol, Auxiliary.prettyPrintDict(prot))
def test_derivatives(self):
print "Testing derivatives w.r.t. data ... "
sys.stdout.flush()
myp = 2.0 * np.random.rand(1)[0] + .5
mys = 3.0 * np.random.rand(1)[0] + 1.0
p = Distributions.ExponentialPower({'p': myp, 's': mys})
dat = p.sample(100)
h = 1e-7
tol = 1e-4
y = np.array(dat.X) + h
df = p.dldx(dat)
df2 = (p.loglik(Data(y)) - p.loglik(dat)) / h
prot = {}
prot[
'message'] = 'Difference in derivative of log-likelihood for PowerExponential greater than ' + str(
tol)
prot['max difference'] = np.max(np.abs(df - df2))
prot['mean difference'] = np.mean(np.abs(df - df2))
self.assertTrue(
np.max(np.abs(df - df2)) < tol, Auxiliary.prettyPrintDict(prot))
