def test_ls(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import LS
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.5, 1.0])
sm = LS()
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
def test_ls(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import LS
xt = np.array([0., 1., 2., 3., 4.])
yt = np.array([0., 1., 1.5, 0.5, 1.0])
sm = LS()
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0., 4., num)
y = sm.predict_values(x)
plt.plot(xt, yt, 'o')
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.legend(['Training data', 'Prediction'])
plt.show()
ydtest = np.zeros((ntest, ndim))
for i in range(ndim):
ydtest[:, i] = fun(xtest, kx=i).T
########### The LS model
# Initialization of the model
t = LS(print_prediction=False)
# Add the DOE
t.set_training_values(xt, yt[:, 0])
# Train the model
t.train()
# Prediction of the validation points
y = t.predict_values(xtest)
print("LS, err: " + str(compute_rms_error(t, xtest, ytest)))
if plot_status:
k, l = 0, 0
f, axarr = plt.subplots(4, 3)
axarr[k, l].plot(ytest, ytest, "-.")
axarr[k, l].plot(ytest, y, ".")
l += 1
axarr[3, 2].arrow(0.3, 0.3, 0.2, 0)
axarr[3, 2].arrow(0.3, 0.3, 0.0, 0.4)
axarr[3, 2].text(0.25, 0.4, r"$\hat{y}$")
axarr[3, 2].text(0.35, 0.15, r"$y_{true}$")
axarr[3, 2].axis("off")
# Fine-tune figure; hide x ticks for top plots and y ticks for right plots
plt.setp(axarr[3, 2].get_xticklabels(), visible=False)