def test_hmc():
# load test data
datadir = os.path.join(os.path.dirname(phasemodel.__file__), 'tests',
'testdata')
mdict = np.load(os.path.join(datadir, 'three_phases_v2.npz'))
for var in mdict.files:
globals()[var] = mdict[var]
sz = K_true.shape[0]
# convert coupling from complex 3x3 to real 6x6 matrix
M = kappa2m(K_true)
# some settings
opts = hmc2.opt(nsamples=10**3,
nomit=10**3,
steps=50,
stepadj=.15,
persistence=False)
# generate test data
utils.tic()
samps = hmc2.hmc2(en.f_phasedist, np.zeros(sz), opts, en.g_phasedist, M)
data = utils.smod(samps.T)
utils.toc()
# fit test data
K_fit = phasemodel.model.fit_model(data)
print K_true
print K_fit
K_error = (abs(K_true - K_fit)).mean()
print """
mean-absolute-difference = %6.8f; expect: 0.1
""" % (K_error)
def test_hmc_biased():
# generate random coupling
dim = 5
K_true = np.random.randn(dim + 1,
dim + 1) + 1j * np.random.randn(dim + 1, dim + 1)
K_true[np.diag(np.ones(dim + 1, bool))] = 0
K_true = .5 * (K_true + np.conj(K_true.T))
# convert coupling from complex 3x3 to real 6x6 matrix
M = kappa2m(K_true)
# some settings
opts = hmc2.opt(nsamples=10**3,
nomit=10**3,
steps=50,
stepadj=.15,
persistence=False)
# generate test data
utils.tic()
samps = hmc2.hmc2(en.f_phasedist_biased, np.zeros(dim), opts,
en.g_phasedist_biased, M)
data = utils.smod(samps.T)
utils.toc()
# fit test data
K_fit = phasemodel.model.fit_model_biased(data)
print K_true
print K_fit
K_error = (abs(K_true - K_fit)).mean()
print """
mean-absolute-difference = %6.8f; expect: 0.2
""" % (K_error)
def test_hmc():
# load test data
datadir = os.path.join(os.path.dirname(phasemodel.__file__),'tests','testdata')
mdict = np.load(os.path.join(datadir,'three_phases_v2.npz'))
for var in mdict.files:
globals()[var] = mdict[var]
sz = K_true.shape[0]
# convert coupling from complex 3x3 to real 6x6 matrix
M = kappa2m(K_true);
# some settings
opts = hmc2.opt(
nsamples = 10**3,
nomit = 10**3,
steps = 50,
stepadj = .15,
persistence = False)
# generate test data
utils.tic()
samps = hmc2.hmc2(en.f_phasedist,np.zeros(sz),opts,en.g_phasedist,M)
data = utils.smod(samps.T)
utils.toc()
# fit test data
K_fit = phasemodel.model.fit_model(data);
print K_true
print K_fit
K_error = (abs(K_true-K_fit)).mean()
print """
mean-absolute-difference = %6.8f; expect: 0.1
"""%(K_error)
def test_hmc_biased():
# generate random coupling
dim = 5
K_true = np.random.randn(dim+1,dim+1)+1j*np.random.randn(dim+1,dim+1)
K_true[np.diag(np.ones(dim+1,bool))] = 0
K_true = .5*(K_true+np.conj(K_true.T))
# convert coupling from complex 3x3 to real 6x6 matrix
M = kappa2m(K_true);
# some settings
opts = hmc2.opt(
nsamples = 10**3,
nomit = 10**3,
steps = 50,
stepadj = .15,
persistence = False)
# generate test data
utils.tic()
samps = hmc2.hmc2(en.f_phasedist_biased,np.zeros(dim),opts,en.g_phasedist_biased,M)
data = utils.smod(samps.T)
utils.toc()
# fit test data
K_fit = phasemodel.model.fit_model_biased(data);
print K_true
print K_fit
K_error = (abs(K_true-K_fit)).mean()
print """
mean-absolute-difference = %6.8f; expect: 0.2
"""%(K_error)