def init_log_like(self):
# initialize log_like
if self.do_radial:
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v, self.model.w, self.pbc)
log_like = rad_log_like_lag(
self.model.dim_v,
self.model.dim_rad,
self.data.dim_lt,
self.model.rate,
self.model.wrad,
self.data.list_lt,
self.data.list_trans,
self.model.redges,
self.lmax,
self.model.bessel0_zeros,
self.model.bessels,
0.0,
)
else:
log_like = log_like_lag(
self.model.dim_v,
self.data.dim_lt,
self.model.v,
self.model.w,
self.model.list_lt,
self.data.list_trans,
self.pbc,
)
if log_like is None:
raise ValueError("Initial propagator has non-positive elements")
elif np.isnan(log_like):
raise ValueError("Initial likelihood diverges")
self.log_like = log_like
# add smoothing to diffusion profile
if self.k > 0.0:
E_w = string_energy(self.model.w, self.k, self.pbc)
self.string_vecs = string_vecs(len(self.model.w), self.pbc)
self.log_like = log_like - E_w # minus sign because surface=log_like
print "initial log-likelihood:", self.log_like
self.all_log_like = np.zeros(self.nmc, float)
# TODO make nicer
if self.model.ncosF > 0:
self.naccv_coeff = np.zeros(self.model.ncosF, int)
if self.model.ncosD > 0:
self.naccw_coeff = np.zeros(self.model.ncosD, int)
if self.do_radial:
if self.model.ncosDrad > 0:
self.naccwrad_coeff = np.zeros(self.model.ncosDrad, int)
else:
self.model.ncosDrad = -1
def mcmove_diffusion(self):
# propose temporary w vector: wt
if self.model.ncosD <= 0:
if self.k > 0:
index = np.random.randint(
0, self.model.dim_w
) # TODO what if string_vecs has different dimension???
wt = self.model.w + self.dw * (
np.random.random() - 0.5) * self.string_vecs[:, index]
else:
index = np.random.randint(0, self.model.dim_w)
wt = copy.deepcopy(self.model.w) # temporary w
wt[index] += self.dw * (np.random.random() - 0.5)
else:
index = np.random.randint(0, self.model.ncosD)
coefft = copy.deepcopy(self.model.w_coeff)
coefft[index] += self.dw * (np.random.random() - 0.5)
wt = self.model.calc_profile(coefft, self.model.w_basis)
log_like_try = log_like_lag(self.model.dim_v, self.data.dim_lt,
self.model.v, wt, self.model.list_lt,
self.data.list_trans, self.pbc)
if log_like_try is not None and not np.isnan(
log_like_try): # propagator is well behaved
# add restraints to smoothen
if self.k > 0.:
E_wt = string_energy(wt, self.k, self.pbc)
log_like_try -= E_wt # minus sign because surface=log_like
# Metropolis acceptance
dlog = log_like_try - self.log_like
r = np.random.random() #in [0,1[
if r < np.exp(
dlog / self.temp
): # accept if dlog increases, accept maybe if decreases
self.model.w[:] = wt[:]
if self.model.ncosD > 0:
self.model.w_coeff[:] = coefft[:]
self.naccw_coeff[index] += 1
self.naccw += 1
self.naccw_update += 1
self.log_like = log_like_try
if False:
self.check_propagator(self.model.list_lt[0])
print "loglike", self.log_like
def init_log_like(self):
# initialize log_like
if self.do_radial:
self.model.rate = init_rate_matrix(self.model.dim_v, self.model.v,
self.model.w, self.pbc)
log_like = rad_log_like_lag(
self.model.dim_v, self.model.dim_rad, self.data.dim_lt,
self.model.rate, self.model.wrad, self.data.list_lt,
self.data.list_trans, self.model.redges, self.lmax,
self.model.bessel0_zeros, self.model.bessels, 0.)
else:
log_like = log_like_lag(self.model.dim_v, self.data.dim_lt,
self.model.v, self.model.w,
self.model.list_lt, self.data.list_trans,
self.pbc)
if log_like is None:
raise ValueError("Initial propagator has non-positive elements")
elif np.isnan(log_like):
raise ValueError("Initial likelihood diverges")
self.log_like = log_like
# add smoothing to diffusion profile
if self.k > 0.:
E_w = string_energy(self.model.w, self.k, self.pbc)
self.string_vecs = string_vecs(len(self.model.w), self.pbc)
self.log_like = log_like - E_w # minus sign because surface=log_like
print "initial log-likelihood:", self.log_like
self.all_log_like = np.zeros(self.nmc, float)
# TODO make nicer
if self.model.ncosF > 0:
self.naccv_coeff = np.zeros(self.model.ncosF, int)
if self.model.ncosD > 0:
self.naccw_coeff = np.zeros(self.model.ncosD, int)
if self.do_radial:
if self.model.ncosDrad > 0:
self.naccwrad_coeff = np.zeros(self.model.ncosDrad, int)
else:
self.model.ncosDrad = -1
def mcmove_diffusion(self):
# propose temporary w vector: wt
if self.model.ncosD <= 0:
if self.k > 0:
index = np.random.randint(0, self.model.dim_w) # TODO what if string_vecs has different dimension???
wt = self.model.w + self.dw * (np.random.random() - 0.5) * self.string_vecs[:, index]
else:
index = np.random.randint(0, self.model.dim_w)
wt = copy.deepcopy(self.model.w) # temporary w
wt[index] += self.dw * (np.random.random() - 0.5)
else:
index = np.random.randint(0, self.model.ncosD)
coefft = copy.deepcopy(self.model.w_coeff)
coefft[index] += self.dw * (np.random.random() - 0.5)
wt = self.model.calc_profile(coefft, self.model.w_basis)
log_like_try = log_like_lag(
self.model.dim_v, self.data.dim_lt, self.model.v, wt, self.model.list_lt, self.data.list_trans, self.pbc
)
if log_like_try is not None and not np.isnan(log_like_try): # propagator is well behaved
# add restraints to smoothen
if self.k > 0.0:
E_wt = string_energy(wt, self.k, self.pbc)
log_like_try -= E_wt # minus sign because surface=log_like
# Metropolis acceptance
dlog = log_like_try - self.log_like
r = np.random.random() # in [0,1[
if r < np.exp(dlog / self.temp): # accept if dlog increases, accept maybe if decreases
self.model.w[:] = wt[:]
if self.model.ncosD > 0:
self.model.w_coeff[:] = coefft[:]
self.naccw_coeff[index] += 1
self.naccw += 1
self.naccw_update += 1
self.log_like = log_like_try
if False:
self.check_propagator(self.model.list_lt[0])
print "loglike", self.log_like