def _run_pure_online(self, d, k, mode, nframes):
#++++++++++++++++++++++++++++++++++++++++
# Approximate the models with online EM
#++++++++++++++++++++++++++++++++++++++++
ogm = GM(d, k, mode)
ogmm = OnGMM(ogm, 'kmean')
init_data = self.data[0:nframes / 20, :]
ogmm.init(init_data)
# Forgetting param
ku = 0.005
t0 = 200
lamb = 1 - 1 / (N.arange(-1, nframes - 1) * ku + t0)
nu0 = 0.2
nu = N.zeros((len(lamb), 1))
nu[0] = nu0
for i in range(1, len(lamb)):
nu[i] = 1. / (1 + lamb[i] / nu[i - 1])
# object version of online EM
for t in range(nframes):
# the assert are here to check we do not create copies
# unvoluntary for parameters
assert ogmm.pw is ogmm.cw
assert ogmm.pmu is ogmm.cmu
assert ogmm.pva is ogmm.cva
ogmm.compute_sufficient_statistics_frame(self.data[t], nu[t])
ogmm.update_em_frame()
ogmm.gm.set_param(ogmm.cw, ogmm.cmu, ogmm.cva)
return ogmm.gm
def _run_pure_online(self, d, k, mode, nframes):
# ++++++++++++++++++++++++++++++++++++++++
# Approximate the models with online EM
# ++++++++++++++++++++++++++++++++++++++++
ogm = GM(d, k, mode)
ogmm = OnGMM(ogm, "kmean")
init_data = self.data[0 : nframes / 20, :]
ogmm.init(init_data)
# Forgetting param
ku = 0.005
t0 = 200
lamb = 1 - 1 / (N.arange(-1, nframes - 1) * ku + t0)
nu0 = 0.2
nu = N.zeros((len(lamb), 1))
nu[0] = nu0
for i in range(1, len(lamb)):
nu[i] = 1.0 / (1 + lamb[i] / nu[i - 1])
# object version of online EM
for t in range(nframes):
# the assert are here to check we do not create copies
# unvoluntary for parameters
assert ogmm.pw is ogmm.cw
assert ogmm.pmu is ogmm.cmu
assert ogmm.pva is ogmm.cva
ogmm.compute_sufficient_statistics_frame(self.data[t], nu[t])
ogmm.update_em_frame()
ogmm.gm.set_param(ogmm.cw, ogmm.cmu, ogmm.cva)
return ogmm.gm
def _check(self, d, k, mode, nframes, emiter):
# ++++++++++++++++++++++++++++++++++++++++
# Approximate the models with online EM
# ++++++++++++++++++++++++++++++++++++++++
# Learn the model with Online EM
ogm = GM(d, k, mode)
ogmm = OnGMM(ogm, "kmean")
init_data = self.data
ogmm.init(init_data, niter=KM_ITER)
# Check that online kmean init is the same than kmean offline init
ogm0 = copy.copy(ogm)
assert_array_equal(ogm0.w, self.gm0.w)
assert_array_equal(ogm0.mu, self.gm0.mu)
assert_array_equal(ogm0.va, self.gm0.va)
# Forgetting param
lamb = N.ones((nframes, 1))
lamb[0] = 0
nu0 = 1.0
nu = N.zeros((len(lamb), 1))
nu[0] = nu0
for i in range(1, len(lamb)):
nu[i] = 1.0 / (1 + lamb[i] / nu[i - 1])
# object version of online EM: the p* arguments are updated only at each
# epoch, which is equivalent to on full EM iteration on the
# classic EM algorithm
ogmm.pw = ogmm.cw.copy()
ogmm.pmu = ogmm.cmu.copy()
ogmm.pva = ogmm.cva.copy()
for e in range(emiter):
for t in range(nframes):
ogmm.compute_sufficient_statistics_frame(self.data[t], nu[t])
ogmm.update_em_frame()
# Change pw args only a each epoch
ogmm.pw = ogmm.cw.copy()
ogmm.pmu = ogmm.cmu.copy()
ogmm.pva = ogmm.cva.copy()
# For equivalence between off and on, we allow a margin of error,
# because of round-off errors.
print " Checking precision of equivalence with offline EM trainer "
maxtestprec = 18
try:
for i in range(maxtestprec):
assert_array_almost_equal(self.gm.w, ogmm.pw, decimal=i)
assert_array_almost_equal(self.gm.mu, ogmm.pmu, decimal=i)
assert_array_almost_equal(self.gm.va, ogmm.pva, decimal=i)
print "\t !! Precision up to %d decimals !! " % i
except AssertionError:
if i < AR_AS_PREC:
print """\t !!NOT OK: Precision up to %d decimals only,
outside the allowed range (%d) !! """ % (
i,
AR_AS_PREC,
)
raise AssertionError
else:
print "\t !!OK: Precision up to %d decimals !! " % i
def _check(self, d, k, mode, nframes, emiter):
#++++++++++++++++++++++++++++++++++++++++
# Approximate the models with online EM
#++++++++++++++++++++++++++++++++++++++++
# Learn the model with Online EM
ogm = GM(d, k, mode)
ogmm = OnGMM(ogm, 'kmean')
init_data = self.data
ogmm.init(init_data, niter=KM_ITER)
# Check that online kmean init is the same than kmean offline init
ogm0 = copy.copy(ogm)
assert_array_equal(ogm0.w, self.gm0.w)
assert_array_equal(ogm0.mu, self.gm0.mu)
assert_array_equal(ogm0.va, self.gm0.va)
# Forgetting param
lamb = N.ones((nframes, 1))
lamb[0] = 0
nu0 = 1.0
nu = N.zeros((len(lamb), 1))
nu[0] = nu0
for i in range(1, len(lamb)):
nu[i] = 1. / (1 + lamb[i] / nu[i - 1])
# object version of online EM: the p* arguments are updated only at each
# epoch, which is equivalent to on full EM iteration on the
# classic EM algorithm
ogmm.pw = ogmm.cw.copy()
ogmm.pmu = ogmm.cmu.copy()
ogmm.pva = ogmm.cva.copy()
for e in range(emiter):
for t in range(nframes):
ogmm.compute_sufficient_statistics_frame(self.data[t], nu[t])
ogmm.update_em_frame()
# Change pw args only a each epoch
ogmm.pw = ogmm.cw.copy()
ogmm.pmu = ogmm.cmu.copy()
ogmm.pva = ogmm.cva.copy()
# For equivalence between off and on, we allow a margin of error,
# because of round-off errors.
print " Checking precision of equivalence with offline EM trainer "
maxtestprec = 18
try:
for i in range(maxtestprec):
assert_array_almost_equal(self.gm.w, ogmm.pw, decimal=i)
assert_array_almost_equal(self.gm.mu, ogmm.pmu, decimal=i)
assert_array_almost_equal(self.gm.va, ogmm.pva, decimal=i)
print "\t !! Precision up to %d decimals !! " % i
except AssertionError:
if i < AR_AS_PREC:
print """\t !!NOT OK: Precision up to %d decimals only,
outside the allowed range (%d) !! """ % (i, AR_AS_PREC)
raise AssertionError
else:
print "\t !!OK: Precision up to %d decimals !! " % i