Python pcmaes_pwqbの例

コード例 #1

ファイル: lcmaes_interface.py プロジェクト: IloneM/libcmaes

def pcmaes(fitfunc,p):
    has_bounds = isinstance(p,lcmaes.CMAParametersPB) or isinstance(p,lcmaes.CMAParametersPBS)
    has_scaling = isinstance(p,lcmaes.CMAParametersNBS) or isinstance(p,lcmaes.CMAParametersPBS)
    if not has_bounds:
        if not has_scaling:
            return lcmaes.pcmaes(fitfunc,p)
        else:
            return lcmaes.pcmaes_ls(fitfunc,p)
    else:
        if not has_scaling:
            return lcmaes.pcmaes_pwqb(fitfunc,p)
        else:
            return lcmaes.pcmaes_pwqb_ls(fitfunc,p)

コード例 #2

ファイル: lcmaes_interface.py プロジェクト: zxp-proteus/libcmaes

def pcmaes(fitfunc, p):
    has_bounds = isinstance(p, lcmaes.CMAParametersPB) or isinstance(
        p, lcmaes.CMAParametersPBS)
    has_scaling = isinstance(p, lcmaes.CMAParametersNBS) or isinstance(
        p, lcmaes.CMAParametersPBS)
    if not has_bounds:
        if not has_scaling:
            return lcmaes.pcmaes(fitfunc, p)
        else:
            return lcmaes.pcmaes_ls(fitfunc, p)
    else:
        if not has_scaling:
            return lcmaes.pcmaes_pwqb(fitfunc, p)
        else:
            return lcmaes.pcmaes_pwqb_ls(fitfunc, p)

コード例 #3

ファイル: ptest_bounds.py プロジェクト: scofield0li/libcmaes

x = [3]*10
olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
seed = 0 # 0 for seed auto-generated within the lib.
sigma = 0.1
lbounds = [-4]*10
ubounds = [4]*10
gp = lcmaes.make_genopheno_pwqb(lbounds,ubounds,10)
p = lcmaes.make_parameters_pwqb(x,sigma,olambda,seed,gp)

# objective function.
def nfitfunc(x,n):
    val = 0.0
    for i in range(0,n):
        val += x[i]*x[i]
    return val

# generate a function object
objfunc = lcmaes.fitfunc_pbf.from_callable(nfitfunc);

# pass the function and parameter to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes_pwqb(objfunc,p)

# collect and inspect results
bcand = cmasols.best_candidate()
bx = lcmaes.get_candidate_x(bcand)
print "best x=",bx
print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print "cov=",cov
print "elapsed time=",cmasols.elapsed_time(),"ms"

コード例 #4

ファイル: ptest_bounds.py プロジェクト: zxp-proteus/libcmaes

x = [3]*10
lambda_ = 10 # lambda is a reserved keyword in python, using lambda_ instead.
seed = 0 # 0 for seed auto-generated within the lib.
sigma = 0.1
lbounds = [-4]*10
ubounds = [4]*10
gp = lcmaes.make_genopheno_pwqb(lbounds,ubounds,10)
p = lcmaes.make_parameters_pwqb(x,sigma,gp,lambda_,seed)

# objective function.
def nfitfunc(x,n):
    val = 0.0
    for i in range(0,n):
        val += x[i]*x[i]
    return val

# generate a function object
objfunc = lcmaes.fitfunc_pbf.from_callable(nfitfunc);

# pass the function and parameter to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes_pwqb(objfunc,p)

# collect and inspect results
bcand = cmasols.best_candidate()
bx = lcmaes.get_candidate_x(bcand)
print("best x=",bx)
print("distribution mean=",lcmaes.get_solution_xmean(cmasols))
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print("cov=",cov)
print("elapsed time=",cmasols.elapsed_time(),"ms")