# Description of the foward finite-difference "algorithm". from opal.core.algorithm import Algorithm from opal.core.parameter import Parameter from opal.core.measure import Measure # Define Algorithm object. FD = Algorithm(name='FD', description='Forward Finite Differences') # Register executable for FD. FD.set_executable_command('python fd_run.py') # Register parameter file used by black-box solver to communicate with FD. #FD.set_parameter_file('fd.param') # Should be chosen automatically and hidden. # Define parameter and register it with algorithm. h = Parameter(kind='real', default=0.5, bound=(0, None), name='h', description='Step size') FD.add_param(h) # Define relevant measure and register with algorithm. error = Measure(kind='real', name='ERROR', description='Error in derivative') FD.add_measure(error)
IPOPT.add_parameter_constraint('s_theta > 1') IPOPT.add_parameter_constraint('s_phi > 1') IPOPT.add_parameter_constraint('delta > 0') IPOPT.add_parameter_constraint('eta_phi > 0') IPOPT.add_parameter_constraint('eta_phi < 0.5') IPOPT.add_parameter_constraint('theta_min_fact > 0') IPOPT.add_parameter_constraint('theta_max_fact > 0') IPOPT.add_parameter_constraint('gamma_theta > 0') IPOPT.add_parameter_constraint('gamma_theta < 1') IPOPT.add_parameter_constraint('gamma_phi > 0') IPOPT.add_parameter_constraint('gamma_phi < 1') IPOPT.add_parameter_constraint('kappa_soc > 0') IPOPT.add_parameter_constraint('kappa_soc < 1') # Define and register measures. IPOPT.add_measure(Measure(name='CPU', kind='real', description='Computing time')) IPOPT.add_measure(Measure(name='FEVAL', kind='integer', description='Number of evaluation of objective function')) IPOPT.add_measure(Measure(name='EQCVAL', kind='integer', description='Number of evaluation of equality constraints')) IPOPT.add_measure(Measure(name='INCVAL', kind='integer', description='Number of evaluation of inequality constraints')) IPOPT.add_measure(Measure(name='GEVAL', kind='integer', description='Number of evaluation of function objective gradient')) IPOPT.add_measure(Measure(name='EQJVAL', kind='integer',
# Description of ABySS. from opal.core.algorithm import Algorithm from opal.core.parameter import Parameter from opal.core.measure import Measure kd = int(raw_input("k-default: ")) kl = int(raw_input("k-lower: ")) ku = int(raw_input("k-upper: ")) # Define Algorithm object. AB = Algorithm(name='AB', description='ABySS') # Register executable command. AB.set_executable_command('python abyss_run.py') # Define parameter and register it with algorithm. #200k-test k = 30; 16, 48 k = Parameter(kind='integer', default=kd, bound=(kl, ku), name='k', description='Step size') AB.add_param(k) # Define relevant measure and register with algorithm. n50 = Measure(kind='integer', name='N50', description='N50 value') AB.add_measure(n50) #error = Measure(kind='real', name='ERROR', description='Error in derivative') #AB.add_measure(error)
bkthresh1 = Parameter(kind='integer', default=bkthresh1_def, bound=(bkthresh1_min, bkthresh1_max), name='bkthresh1', description='Level-2 bucket sieve bound') # OPAL begins by modifying the first parameters below, thus we should put # first the most important parameters. # Warning: if you change the order of parameters, please also change the # lines I_opt=`head -1 $f` and so on in optimize.sh LAS.add_param(I) LAS.add_param(qmin) LAS.add_param(lim0) LAS.add_param(lim1) LAS.add_param(bkthresh1) LAS.add_param(lpb0) LAS.add_param(lpb1) LAS.add_param(mfb0) LAS.add_param(mfb1) LAS.add_param(ncurves0) LAS.add_param(ncurves1) # Define relevant measure and register with algorithm. sievetime = Measure(kind='real', name='SIEVETIME', description='Time in the sieving') rels = Measure(kind='integer', name='RELATIONS', description='Relations found in the sieving') LAS.add_measure(sievetime) LAS.add_measure(rels)
default=0.1000, description='Level, used for interpolation')) # Register constraints on the parameters. trunk.add_parameter_constraint(ParameterConstraint('eta1 < eta2')) trunk.add_parameter_constraint(ParameterConstraint('eta1 > 0')) trunk.add_parameter_constraint(ParameterConstraint('eta2 < 1')) trunk.add_parameter_constraint(ParameterConstraint('gamma1 > 0')) trunk.add_parameter_constraint(ParameterConstraint('gamma1 <= gamma2')) #trunk.add_parameter_constraint(ParameterConstraint('gamma2 < 1')) trunk.add_parameter_constraint(ParameterConstraint('gamma3 > 1')) # Register atomic measures. trunk.add_measure(Measure(name='CPU', kind='real', description='Computing time')) trunk.add_measure(Measure(name='FEVAL', kind='integer', description='Number of function evaluations')) trunk.add_measure(Measure(name='GEVAL', kind='integer', description='Number of gradient evaluations')) trunk.add_measure(Measure(name='NITER', kind='integer', description='Number of iterations')) trunk.add_measure(Measure(name='CGITER', kind='integer', description='Number of CG iterations')) trunk.add_measure(Measure(name='RDGRAD', kind='real',
scale = Parameter(kind='integer', default=0, name='SCALE') iprint = Parameter(kind='integer', default=1, name='IPRINT') # Register parameters with algorithm. DFO.add_param(nx) DFO.add_param(maxit) DFO.add_param(maxef) DFO.add_param(stpcrtr) DFO.add_param(delmin) DFO.add_param(stpthr) DFO.add_param(cnstol) DFO.add_param(delta) DFO.add_param(pp) DFO.add_param(scale) DFO.add_param(iprint) # Define the feasible region. DFO.add_parameter_constraint('DELTA >= DELMIN') # Define and register measures. exitcode = Measure(kind='integer', name='EXITCODE', description='Exit code') fval = Measure(kind='real', name='FVAL', description='Function value') cpu = Measure(kind='real', name='CPU', description='CPU time usage') feval = Measure(kind='real', name='FEVAL', description='Number of function evaluations') DFO.add_measure(exitcode) DFO.add_measure(fval) DFO.add_measure(cpu) DFO.add_measure(feval)
NN.add_param(m) d = Parameter(kind="real", default=1e-6, bound=(0., .1), name="decay", description="Decay") NN.add_param(d) n1 = Parameter(kind="integer", default=200, bound=(0, 500), name="n1", description="Number of neurons in first layer") NN.add_param(n1) n2 = Parameter(kind="integer", default=200, bound=(0, 500), name="n2", description="Number of neurons in second layer") NN.add_param(n2) n3 = Parameter(kind="integer", default=0, bound=(0, 500), name="n3", description="Number of neurons in third layer") NN.add_param(n3) # Define relevant measure and register with algorithm. error = Measure(kind="real", name="acc", description="Accuracy of the model") NN.add_measure(error)
from opal.core.algorithm import Algorithm from opal.core.parameter import Parameter from opal.core.measure import Measure # Define new algorithm. coopsort = Algorithm(name='CoopSort', description='Sort Algorithm') # Register executable. coopsort.set_executable_command('python coopsort_run.py') # Define parameters. # The following coop tree amounts to 5522522 (in base 6.) coopsort.add_param(Parameter(name='coopTree', kind='categorical', default=275378, #default=284354431, description='Encoded cooperation tree')) # This dummy parameter is just there to circumvent a bug in NOMAD # that occurs when the problem has a single parameter and this parameter # is categorical. coopsort.add_param(Parameter(name='nothing', kind='integer', default=0, description='To avoid a bug in NOMAD')) coopsort.add_measure(Measure(name='TIME', kind='real', description='Computing time'))