class NestedSamplingSA(NestedSampling):
"""overload get_starting_configuration() in order to introduce sampling from known minima
Parameters
----------
system : pele system object
nreplicas : int
number of replicas
takestep : callable
object to do the step taking. must be callable and have attribute takestep.stepsize
minima : list of Minimum objects
"""
def __init__(self,
system,
nreplicas,
mc_runner,
minima,
minprob=None,
energy_offset=None,
**kwargs):
super(NestedSamplingSA, self).__init__(system, nreplicas, mc_runner,
**kwargs)
self.minima = minima
self.bh_sampler = SASampler(self.minima, self.system.k)
if minprob is None:
raise ValueError("minprob cannot be None")
self.minprob = minprob
if energy_offset is None:
self.energy_offset = 2.5
else:
self.energy_offset = energy_offset
self.count_sampled_minima = 0
def get_starting_configuration_minima_HA(self, Emax):
"""using the Harmonic Approximation sample a new configuration starting from a minimum sampled uniformly according to phase space volume
Notes
-----
displace minimum configuration along its eigenvectors
"""
m = self.bh_sampler.sample_minimum(Emax)
x = self.bh_sampler.sample_coords_from_basin(m, Emax)
pot = self.system.get_potential()
e = pot.getEnergy(x)
return x, e
def get_starting_configuration_minima_single(self, Emax):
"""return a minimum sampled uniformly according to phase space volume"""
m = self.bh_sampler.sample_minimum(Emax)
x, e = m.coords, m.energy
self.system.center_coords(x)
if True:
accept_tests = self.system.get_config_tests()
for test in accept_tests:
t = test(coords=x)
if not t:
print "warning: minimum from database failed configuration test", m._id, m.energy
raise ConfigTestError()
return x, e
def get_starting_configuration_minima(self, Emax):
"""return a minimum sampled uniformly according to phase space volume
Notes
-----
some of the minima in the database don't satisfy the configuration
checks. So we sample over and over again until we get one that
passes
"""
self.count_sampled_minima += 1
while True:
try:
return self.get_starting_configuration_minima_single(Emax)
except ConfigTestError:
pass
def onset_prob_func(self, Emax):
"""return the probability of sampling from a minimum
The probability depends on Emax. For high Emax the probability is 0. the probability
increases as Emax get's lower, reaching a maximum of self.minprob.
value of Emax where the probabilty starts to get large is energy_max_database + energy_offset where
energy_max_database is the maximum energy minimum in the database. This behavior
can be adjusted with parameter energy_offset.
parameter b determines the speed at which it turns on.
"""
if not hasattr(self, "_energy_max_database"):
self._energy_max_database = float(
max([m.energy for m in self.minima]))
max_prob = float(self.minprob)
energy_onset_width = 1.
dE = self._energy_max_database + self.energy_offset - Emax
f = dE / energy_onset_width
if f > 100:
onset_prob = 0.
else:
onset_prob = max_prob / (1. + np.exp(-f))
return onset_prob
def get_starting_configurations(self, Emax):
"""this function overloads the function in NestedSampling"""
# choose a replica randomly
configs = self.get_starting_configurations_from_replicas()
# replace each starting configuration with a one chosen
# from the minima with probability prob
onset_prob = self.onset_prob_func(Emax)
prob = onset_prob / float(self.nreplicas)
for i in range(len(configs)):
if np.random.uniform(0, 1) < prob:
x, energy = self.get_starting_configuration_minima(Emax)
configs[i] = Replica(x, energy, from_random=False)
if self.verbose:
print "sampling from minima, E minimum:", energy, "with probability:", prob
return configs
class NestedSamplingSA(NestedSampling):
"""overload get_starting_configuration() in order to introduce sampling from known minima
Parameters
----------
system : pele system object
nreplicas : int
number of replicas
takestep : callable
object to do the step taking. must be callable and have attribute takestep.stepsize
minima : list of Minimum objects
"""
def __init__(self, system, nreplicas, mc_runner, minima,
minprob=None, energy_offset=None,
**kwargs):
super(NestedSamplingSA, self).__init__(system, nreplicas, mc_runner, **kwargs)
self.minima = minima
self.bh_sampler = SASampler(self.minima, self.system.k)
if minprob is None:
raise ValueError("minprob cannot be None")
self.minprob = minprob
if energy_offset is None:
self.energy_offset = 2.5
else:
self.energy_offset = energy_offset
self.count_sampled_minima = 0
def get_starting_configuration_minima_HA(self, Emax):
"""using the Harmonic Approximation sample a new configuration starting from a minimum sampled uniformly according to phase space volume
Notes
-----
displace minimum configuration along its eigenvectors
"""
m = self.bh_sampler.sample_minimum(Emax)
x = self.bh_sampler.sample_coords_from_basin(m, Emax)
pot = self.system.get_potential()
e = pot.getEnergy(x)
return x, e
def get_starting_configuration_minima_single(self, Emax):
"""return a minimum sampled uniformly according to phase space volume"""
m = self.bh_sampler.sample_minimum(Emax)
x, e = m.coords, m.energy
self.system.center_coords(x)
if True:
accept_tests = self.system.get_config_tests()
for test in accept_tests:
t = test(coords=x)
if not t:
print "warning: minimum from database failed configuration test", m._id, m.energy
raise ConfigTestError()
return x, e
def get_starting_configuration_minima(self, Emax):
"""return a minimum sampled uniformly according to phase space volume
Notes
-----
some of the minima in the database don't satisfy the configuration
checks. So we sample over and over again until we get one that
passes
"""
self.count_sampled_minima += 1
while True:
try:
return self.get_starting_configuration_minima_single(Emax)
except ConfigTestError:
pass
def onset_prob_func(self, Emax):
"""return the probability of sampling from a minimum
The probability depends on Emax. For high Emax the probability is 0. the probability
increases as Emax get's lower, reaching a maximum of self.minprob.
value of Emax where the probabilty starts to get large is energy_max_database + energy_offset where
energy_max_database is the maximum energy minimum in the database. This behavior
can be adjusted with parameter energy_offset.
parameter b determines the speed at which it turns on.
"""
if not hasattr(self, "_energy_max_database"):
self._energy_max_database = float(max([m.energy for m in self.minima]))
max_prob = float(self.minprob)
energy_onset_width = 1.
dE = self._energy_max_database + self.energy_offset - Emax
f = dE / energy_onset_width
if f > 100:
onset_prob = 0.
else:
onset_prob = max_prob / ( 1. + np.exp(-f))
return onset_prob
def get_starting_configurations(self, Emax):
"""this function overloads the function in NestedSampling"""
# choose a replica randomly
configs = self.get_starting_configurations_from_replicas()
# replace each starting configuration with a one chosen
# from the minima with probability prob
onset_prob = self.onset_prob_func(Emax)
prob = onset_prob / float(self.nreplicas)
for i in range(len(configs)):
if np.random.uniform(0,1) < prob:
x, energy = self.get_starting_configuration_minima(Emax)
configs[i] = Replica(x, energy, from_random=False)
if self.verbose:
print "sampling from minima, E minimum:", energy, "with probability:", prob
return configs
