def iDW(xt, yt, xtest, ytest):
########### The IDW model
t = IDW(print_prediction=False)
t.set_training_values(xt, yt)
t.train()
# Prediction of the validation points
y = t.predict_values(xtest)
print('IDW, err: ' + str(compute_rms_error(t, xtest, ytest)))
title = 'IDW'
return t, title, xtest, ytest
def test_idw(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import IDW
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 = IDW(p=2)
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_idw(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import IDW
xt = np.array([0., 1., 2., 3., 4.])
yt = np.array([0., 1., 1.5, 0.5, 1.0])
sm = IDW(p=2)
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()