Comments (3)
Hi, I tried to train KPLSK with a DOE (500, 100) and everything seams fine. Here is the few lines of code. Only the training time is quite long.
import numpy as np
from smt.surrogate_models import KPLSK
from smt.sampling_methods import LHS
from smt.problems import LpNorm
dim = 100
problem = LpNorm(ndim=dim)
xlimits = problem.xlimits
sampling = LHS(xlimits=xlimits)
num = 500
xt = sampling(num)
yt=problem(xt)
sm = KPLSK(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
print('training done')
#to have test data
num=1000
xtest = sampling(num)
ytest=problem(xtest)
ypred=sm.predict_values(xtest)
plt.plot(ytest, ypred , "o")
from smt.
To reduce training time, a possibility is to use GPX (with or without dimension reduction )
sm = GPX(theta0=[1e-2])
sm = GPX(theta0=[1e-2], kpls_dim=50)
from smt.
Hi, I am not sure to understand... Your training input data is an array of shape (4096, 100) ? Do you have an error by applying KPLSK ?
from smt.
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