def test_MarginalHistogramEqualization(self):
print "Testing MarginalHistogramEqualization ..."
sys.stdout.flush()
psource = Distributions.ISA(
n=10,
P=[Distributions.MixtureOfGaussians(K=5) for k in range(10)],
S=[(k, ) for k in range(10)])
ptarget = Distributions.ISA(
n=10,
P=[Distributions.Gaussian(n=1) for k in range(10)],
S=[(k, ) for k in range(10)])
F = NonlinearTransformFactory.MarginalHistogramEqualization(
psource, ptarget)
dat = psource.sample(20000)
ld = F.logDetJacobian(dat)
ld = np.mean(np.abs(ld)) / dat.size(0) / np.log(2)
all_source = psource.all(dat)
all_target = ptarget.all(F * dat)
tol = 1e-2
prot = {}
prot['message'] = 'Difference in logdet correted ALL > ' + str(tol)
prot["1/n/log(2) * <|det J|> "] = ld
prot["ALL(TARGET)"] = all_target
prot["ALL(SOURCE)"] = all_source
prot[
"ALL(TARGET) + 1/n/log(2) * <|det J|> - ALL(SOURCE)"] = all_target + ld - all_source
def test_derivatives(self):
print "Testing derivatives w.r.t. data ... "
sys.stdout.flush()
p = Distributions.MixtureOfGaussians({'K': 5})
dat = p.sample(100)
h = 1e-7
tol = 1e-6
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 MixtureOfGaussians greater than ' + str(tol))