def optimize_PCR_parameters(self, seq_files, product_bounds,
parameters, seq_id=None):
self._optimized = False
if seq_id is None: seq_id = 0
#prepare parameters and matchers
if not (seq_files and product_bounds and parameters): return False
if not self._find_matches(seq_files, seq_id): return False
self._prepare_parameters(product_bounds, parameters)
#optimize parameters
print ''
optimum, _unused, info = fmin_l_bfgs_b(self._objective_function,
self._iparams,
self._objective_function_grad,
approx_grad=False,
bounds=self._ibounds, factr=1e12,
maxfun=self._max_steps, disp=1)
if self._last_mixture: cleanup_file(self._last_mixture)
#parse results
if info['warnflag'] == 2:
self._exit_status = info['task']
elif info['warnflag'] == 1:
self._exit_status = ('Number of simulations has '
'exceeded defined limit: %d') % info['funcalls']
else: self._optimized = True
#save results
self._optimum = optimum
self._named_optimum = self._name_parameters(self._optimum)
#simulate PCR at optimum
ovc, mixture = self._prepare_simulation(self._optimum)
if ovc is None: return False
self._PCR_Simulation = self._new_PCR_Simulation()
self._PCR_Simulation.add_mixture(self._seq_name, mixture)
self._PCR_Simulation.run(WorkCounter())
self._have_results = bool(self._PCR_Simulation)
return self._have_results
def _objective_function(self, parameters):
self._last_parameters = parameters
ovc, mixture = self._prepare_simulation(parameters)
if ovc is None: return -1e32 #FIXME: end search process if aborted
if self._last_mixture: cleanup_file(self._last_mixture)
self._last_mixture = mixture
self._last_OV = ovc(mixture)
return self._last_OV
def _objective_function(self, parameters):
self._last_parameters = parameters
ovc, mixture = self._prepare_simulation(parameters)
if ovc is None: return -1e32 #FIXME: end search process if aborted
if self._last_mixture: cleanup_file(self._last_mixture)
self._last_mixture = mixture
self._last_OV = ovc(mixture)
return self._last_OV
def optimize_PCR_parameters(self,
seq_files,
product_bounds,
parameters,
seq_id=None):
self._optimized = False
if seq_id is None: seq_id = 0
#prepare parameters and matchers
if not (seq_files and product_bounds and parameters): return False
if not self._find_matches(seq_files, seq_id): return False
self._prepare_parameters(product_bounds, parameters)
#optimize parameters
print ''
optimum, _unused, info = fmin_l_bfgs_b(self._objective_function,
self._iparams,
self._objective_function_grad,
approx_grad=False,
bounds=self._ibounds,
factr=1e12,
maxfun=self._max_steps,
disp=1)
if self._last_mixture: cleanup_file(self._last_mixture)
#parse results
if info['warnflag'] == 2:
self._exit_status = info['task']
elif info['warnflag'] == 1:
self._exit_status = (
'Number of simulations has '
'exceeded defined limit: %d') % info['funcalls']
else:
self._optimized = True
#save results
self._optimum = optimum
self._named_optimum = self._name_parameters(self._optimum)
#simulate PCR at optimum
ovc, mixture = self._prepare_simulation(self._optimum)
if ovc is None: return False
self._PCR_Simulation = self._new_PCR_Simulation()
self._PCR_Simulation.add_mixture(self._seq_name, mixture)
self._PCR_Simulation.run(WorkCounter())
self._have_results = bool(self._PCR_Simulation)
return self._have_results