def generate_krig(lb, ub, n_krigsamp, nvar, problem, n_cpu):
init_krigsamp = mcpopgen(lb=lb, ub=ub, ndim=2, n_order=1, n_coeff=3)
print("Evaluating Kriging Sample")
ykrig = problem(init_krigsamp)
print(np.count_nonzero(ykrig <= 0))
# Set Kriging Info
KrigInfo = initkriginfo(1)
KrigInfo["X"] = init_krigsamp
KrigInfo["y"] = ykrig
KrigInfo["nvar"] = nvar
KrigInfo["nsamp"] = n_krigsamp
KrigInfo["nrestart"] = 5
KrigInfo["ub"] = ub
KrigInfo["lb"] = lb
KrigInfo["nkernel"] = len(KrigInfo["kernel"])
KrigInfo["optimizer"] = "lbfgsb"
#trainkrig
drm = None
t = time.time()
krigobj = Kriging(KrigInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj.train(n_cpu=n_cpu)
loocverr, _ = krigobj.loocvcalc()
elapsed = time.time() - t
print("elapsed time to train Kriging model: ", elapsed, "s")
print("LOOCV error of Kriging model: ", loocverr, "%")
return krigobj, loocverr, drm
def generate_krig(init_samp, n_krigsamp, nvar, problem):
# Kriging Sample
t1 = time.time()
init_krigsamp = mcpopgen(type="lognormal",
ndim=nvar,
n_order=1,
n_coeff=5,
stddev=0.2,
mean=1)
ykrig = evaluate(init_krigsamp, type=problem)
t2 = time.time()
print("50 samp eval", t2 - t1)
# Evaluate Kriging Sample and calculate PoF real
init_samp_G = evaluate(init_samp, type=problem)
total_samp = np.hstack((init_samp, init_samp_G)).transpose()
positive_samp = total_samp[:, total_samp[nvar] >= 0]
positive_samp = positive_samp.transpose()
nsamp = np.size(init_samp, 0)
npos = np.size(positive_samp, 0)
Pfreal = 1 - npos / nsamp
lb = np.floor(np.min(init_samp)) * np.ones(shape=[nvar])
ub = np.ceil(np.max(init_samp)) * np.ones(shape=[nvar])
# Set Kriging Info
KrigInfo = initkriginfo("single")
KrigInfo["X"] = init_krigsamp
KrigInfo["y"] = ykrig
KrigInfo["nvar"] = nvar
KrigInfo["nsamp"] = n_krigsamp
KrigInfo["nrestart"] = 5
KrigInfo["ub"] = ub
KrigInfo["lb"] = lb
KrigInfo["nkernel"] = len(KrigInfo["kernel"])
KrigInfo["n_princomp"] = 4
KrigInfo["optimizer"] = "lbfgsb"
#trainkrig
t = time.time()
krigobj = KPLS(KrigInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj.train(parallel=False)
loocverr, _ = krigobj.loocvcalc()
elapsed = time.time() - t
print("elapsed time for train Kriging model: ", elapsed, "s")
print("LOOCV error of Kriging model: ", loocverr, "%")
return krigobj, Pfreal
def generate_krig(init_samp, n_krigsamp, nvar, problem, n_cpu):
init_krigsamp = mcpopgen(type="lognormal",
ndim=nvar,
n_order=1,
n_coeff=5,
stddev=0.2,
mean=1)
print("Evaluating Kriging Sample")
ykrig = evaluate(init_krigsamp, type=problem)
print(np.count_nonzero(ykrig <= 0))
lb = (np.min(init_samp, axis=0))
ub = (np.max(init_samp, axis=0))
Pfreal = None
# Set Kriging Info
KrigInfo = initkriginfo(1)
KrigInfo["X"] = init_krigsamp
KrigInfo["y"] = ykrig
KrigInfo["nvar"] = nvar
KrigInfo["nsamp"] = n_krigsamp
KrigInfo["nrestart"] = 5
KrigInfo["ub"] = ub
KrigInfo["lb"] = lb
KrigInfo["nkernel"] = len(KrigInfo["kernel"])
KrigInfo["n_princomp"] = 4
KrigInfo["optimizer"] = "lbfgsb"
#trainkrig
drm = None
t = time.time()
krigobj = KPLS(KrigInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj.train(n_cpu=n_cpu)
loocverr, _ = krigobj.loocvcalc()
elapsed = time.time() - t
print("elapsed time to train Kriging model: ", elapsed, "s")
print("LOOCV error of Kriging model: ", loocverr, "%")
return krigobj, loocverr, drm
ax2.grid(which='both', axis='both', linestyle='--')
ax2.set_title('Total Order ' + title, fontsize=15, fontweight='bold')
ax2.set_xlim(xlim)
ax2.xaxis.set_tick_params(labelsize=13, width=3)
ax1.set_yticks(y_pos)
ax1.set_yticklabels(vartot, fontsize=13, fontweight='bold')
plt.subplots_adjust(wspace=0.1, hspace=0)
plt.legend()
plt.show()
if __name__ == '__main__':
ub = np.array([5, 5])
lb = np.array([-5, -5])
init_samp = mcpopgen(lb=lb, ub=ub, ndim=2, n_order=6, n_coeff=1)
dic = dict()
nvar = 2
n_krigsamp = 50
problem = cust
n_cpu = 12
# Create Kriging model
t = time.time()
krigobj, loocverr, drm = generate_krig(lb, ub, n_krigsamp, nvar, problem,
n_cpu)
ktime = time.time() - t
# Predict and UQ
pred(krigobj, init_samp, problem, drmmodel=drm)
# Sensitivity Analysis
# Define AKMCS Information
akmcsInfo = dict()
akmcsInfo["init_samp"] = init_samp
akmcsInfo["maxupdate"] = 70
akmcsInfo["problem"] = problem
# Run AKMCS
t = time.time()
akmcsobj = AKMCS(krigobj, akmcsInfo)
akmcsobj.run(savedatato=filename)
elapsed = time.time() - t
print("elapsed time is : ", elapsed, "s")
if __name__ == '__main__':
init_samp = mcpopgen(type="lognormal",
ndim=40,
n_order=6,
n_coeff=1,
stddev=0.2,
mean=1)
nvar = 40
n_krigsamp = 50
problem = 'hidimenra'
filename = "akmcshidimen.csv"
krigobj, Pfreal = generate_krig(init_samp, n_krigsamp, nvar, problem)
run_akmcs(krigobj, init_samp, problem, filename)
print(Pfreal)
print("elapsed time is : ", elapsed, "s")
xtotal = akmcsobj.krigobj.KrigInfo['X']
return xtotal
if __name__ == '__main__':
nvar = 2
n_krigsamp = 25
problem = 'styblinski'
filename = "akmcs2d.csv"
figloc = 'akmcsupdate'
init_samp = mcpopgen(type='normal',
ndim=nvar,
n_order=6,
n_coeff=1,
stddev=1.5)
ysamp = evaluate(init_samp, problem)
krigsamp = mcpopgen(type='normal',
ndim=nvar,
n_order=1,
n_coeff=2.5,
stddev=1.5)
pos_MC = init_samp[ysamp.flatten() > 0, :]
neg_MC = init_samp[ysamp.flatten() <= 0, :]
krigobj, Pfreal = generate_krig(init_samp, krigsamp, nvar, problem)
xtotal = run_akmcs(krigobj, init_samp, problem, filename, figloc, pos_MC,
neg_MC)
print(Pfreal)
