def get_F110_interp():
this_dir = os.path.split(__file__)[0]
xt, yt, xlimits = get_data()
interp = RMTB(
xlimits=xlimits,
num_ctrl_pts=15,
order=4,
approx_order=2,
nonlinear_maxiter=40,
solver_tolerance=1.e-20,
# solver='lu', derivative_solver='lu',
energy_weight=1.e-4,
regularization_weight=0.e-18,
extrapolate=False,
print_global=False,
data_dir=os.path.join(this_dir, '_smt_cache'),
)
# interp = KRG(theta0=[0.1]*3, data_dir='_smt_cache/')
interp.set_training_values(xt, yt)
interp.train()
return interp
def test_rmtb(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import RMTB
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])
xlimits = np.array([[0.0, 4.0]])
sm = RMTB(
xlimits=xlimits,
order=4,
num_ctrl_pts=20,
energy_weight=1e-15,
regularization_weight=0.0,
)
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 rMTBSimba(xt, yt, xtest, ytest, funXLimits):
t = RMTB(xlimits=funXLimits,
min_energy=True,
nonlinear_maxiter=20,
print_prediction=False)
t.set_training_values(xt, yt)
# Add the gradient information
# for i in range(ndim):
# t.set_training_derivatives(xt,yt[:, 1+i].reshape((yt.shape[0],1)),i)
t.train()
# Prediction of the validation points
print('RMTB, err: ' + str(compute_rms_error(t, xtest, ytest)))
# plot prediction/true values
title = 'RMTB'
return t, title, xtest, ytest
from smt.surrogate_models import RMTB
from smt.examples.one_D_step.one_D_step import get_one_d_step, plot_one_d_step
xt, yt, xlimits = get_one_d_step()
interp = RMTB(num_ctrl_pts=100,
xlimits=xlimits,
nonlinear_maxiter=20,
solver_tolerance=1e-16,
energy_weight=1e-14,
regularization_weight=0.)
interp.set_training_values(xt, yt)
interp.train()
plot_one_d_step(xt, yt, xlimits, interp)
]))
counter += 1
else:
skipcount += 1
a = np.array(krigedata)
xt = a[:, 0:3]
yt = a[:, 3:]
print('Data points: ' + str(counter) + ' Skipped: ' + str(skipcount))
# define a RMTS spline interpolant
# TODO replace with a different SMT surrogate
limits = np.array([[0.2, 0.8], [0.05, 1.0], [0.0, 3.5]])
sm = RMTB(order=3, xlimits=limits, nonlinear_maxiter=100)
sm.set_training_values(xt, yt)
sm.train()
# plot a grid of values at a slice of throttle = 0.5
machs = np.linspace(0.2, 0.8, 25)
alts = np.linspace(0.0, 35000., 25)
machs, alts = np.meshgrid(machs, alts)
pred = np.zeros((25, 25, 3))
pred2 = np.zeros((25, 25, 3))
# altitude is scaled by 1 / 10000 everywhere to make the indepdent variables of O(1)
# this is necessary for certain methods like IDW
# TODO examine other slices (for example holding Mach = 0.5 and varying other two parameters)
for i in range(25):
for j in range(25):