def test_rmtc(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import RMTC
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 = RMTC(
xlimits=xlimits,
num_elements=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 setUp(self):
ndim = 3
nt = 100
ne = 100
ncomp = 1
problems = OrderedDict()
problems['exp'] = TensorProduct(ndim=ndim, func='exp')
problems['tanh'] = TensorProduct(ndim=ndim, func='tanh')
problems['cos'] = TensorProduct(ndim=ndim, func='cos')
sms = OrderedDict()
sms['LS'] = LS()
sms['QP'] = QP()
sms['KRG'] = KRG(theta0=[1e-2] * ndim)
sms['KPLS'] = KPLS(theta0=[1e-2] * ncomp, n_comp=ncomp)
sms['KPLSK'] = KPLSK(theta0=[1] * ncomp, n_comp=ncomp)
sms['GEKPLS'] = GEKPLS(theta0=[1e-2] * ncomp,
n_comp=ncomp,
delta_x=1e-1)
if compiled_available:
sms['IDW'] = IDW()
sms['RBF'] = RBF()
sms['RMTC'] = RMTC()
sms['RMTB'] = RMTB()
t_errors = {}
t_errors['LS'] = 1.0
t_errors['QP'] = 1.0
t_errors['KRG'] = 1e-5
t_errors['KPLS'] = 1e-5
t_errors['KPLSK'] = 1e-5
t_errors['GEKPLS'] = 1e-5
if compiled_available:
t_errors['IDW'] = 1e-15
t_errors['RBF'] = 1e-2
t_errors['RMTC'] = 1e-1
t_errors['RMTB'] = 1e-1
e_errors = {}
e_errors['LS'] = 1.5
e_errors['QP'] = 1.5
e_errors['KRG'] = 1e-2
e_errors['KPLS'] = 1e-2
e_errors['KPLSK'] = 1e-2
e_errors['GEKPLS'] = 1e-2
if compiled_available:
e_errors['IDW'] = 1e0
e_errors['RBF'] = 1e0
e_errors['RMTC'] = 2e-1
e_errors['RMTB'] = 2e-1
self.nt = nt
self.ne = ne
self.ndim = ndim
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
def rMTCSimba(xt, yt, xtest, ytest, funXLimits):
t = RMTC(xlimits=funXLimits,
min_energy=True,
nonlinear_maxiter=20,
print_prediction=False)
t.set_training_values(xt, yt)
t.train()
# Prediction of the validation points
print('RMTC, err: ' + str(compute_rms_error(t, xtest, ytest)))
title = 'RMTC model'
return t, title, xtest, ytest
def setUp(self):
ndim = 3
nt = 500
ne = 100
problems = OrderedDict()
problems["sphere"] = Sphere(ndim=ndim)
sms = OrderedDict()
if compiled_available:
sms["RMTC"] = RMTC(num_elements=6, extrapolate=True)
sms["RMTB"] = RMTB(order=4, num_ctrl_pts=10, extrapolate=True)
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
def train(self, X_train, y_train):
if self.flavour == 'bspline':
self.smt_model = RMTB(xlimits=self.xlimits,
smoothness=self.smoothness,
approx_order=self.approx_order,
line_search=self.line_search,
order=self.order,
num_ctrl_pts=self.num_ctrl_pts)
if self.flavour == 'cubic':
self.smt_model = RMTC(xlimits=self.xlimits,
smoothness=self.smoothness,
approx_order=self.approx_order,
line_search=self.line_search,
order=self.order,
num_elements=self.num_elements)
super(RMTSModel, self).train(X_train, y_train)
def setUp(self):
ndim = 2
self.nt = 50
self.ne = 10
self.problem = Sphere(ndim=ndim)
self.sms = sms = OrderedDict()
if compiled_available:
sms['IDW'] = IDW()
sms['RBF'] = RBF()
sms['RMTB'] = RMTB(regularization_weight=1e-8,
nonlinear_maxiter=100,
solver_tolerance=1e-16)
sms['RMTC'] = RMTC(regularization_weight=1e-8,
nonlinear_maxiter=100,
solver_tolerance=1e-16)
def setUp(self):
ndim = 2
nt = 5000
ne = 100
problems = OrderedDict()
problems['sphere'] = Sphere(ndim=ndim)
sms = OrderedDict()
if compiled_available:
sms['RBF'] = RBF()
sms['RMTC'] = RMTC()
sms['RMTB'] = RMTB()
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
def setUp(self):
ndim = 2
nt = 5000
ne = 100
problems = OrderedDict()
problems['sphere'] = Sphere(ndim=ndim)
sms = OrderedDict()
if compiled_available:
sms['RBF'] = RBF()
sms['RMTC'] = RMTC()
sms['RMTB'] = RMTB()
sms['MFK'] = MFK(theta0=[1e-2] * ndim, eval_noise=True)
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
def setUp(self):
ndim = 2
nt = 5000
ne = 100
problems = OrderedDict()
problems["sphere"] = Sphere(ndim=ndim)
sms = OrderedDict()
if compiled_available:
sms["RBF"] = RBF()
sms["RMTC"] = RMTC()
sms["RMTB"] = RMTB()
sms["MFK"] = MFK(theta0=[1e-2] * ndim)
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
def setUp(self):
ndim = 3
nt = 5000
ne = 500
problems = OrderedDict()
problems["sphere"] = Sphere(ndim=ndim)
problems["exp"] = TensorProduct(ndim=ndim, func="exp")
problems["tanh"] = TensorProduct(ndim=ndim, func="tanh")
problems["cos"] = TensorProduct(ndim=ndim, func="cos")
sms = OrderedDict()
if compiled_available:
sms["RMTC"] = RMTC()
sms["RMTB"] = RMTB()
t_errors = {}
t_errors["RMTC"] = 1e-1
t_errors["RMTB"] = 1e-1
e_errors = {}
e_errors["RMTC"] = 1e-1
e_errors["RMTB"] = 1e-1
ge_t_errors = {}
ge_t_errors["RMTC"] = 1e-2
ge_t_errors["RMTB"] = 1e-2
ge_e_errors = {}
ge_e_errors["RMTC"] = 1e-2
ge_e_errors["RMTB"] = 1e-2
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
self.ge_t_errors = ge_t_errors
self.ge_e_errors = ge_e_errors
def setUp(self):
ndim = 3
nt = 5000
ne = 500
problems = OrderedDict()
problems['sphere'] = Sphere(ndim=ndim)
problems['exp'] = TensorProduct(ndim=ndim, func='exp')
problems['tanh'] = TensorProduct(ndim=ndim, func='tanh')
problems['cos'] = TensorProduct(ndim=ndim, func='cos')
sms = OrderedDict()
if compiled_available:
sms['RMTC'] = RMTC()
sms['RMTB'] = RMTB()
t_errors = {}
t_errors['RMTC'] = 1e-1
t_errors['RMTB'] = 1e-1
e_errors = {}
e_errors['RMTC'] = 1e-1
e_errors['RMTB'] = 1e-1
ge_t_errors = {}
ge_t_errors['RMTC'] = 1e-2
ge_t_errors['RMTB'] = 1e-2
ge_e_errors = {}
ge_e_errors['RMTC'] = 1e-2
ge_e_errors['RMTB'] = 1e-2
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
self.ge_t_errors = ge_t_errors
self.ge_e_errors = ge_e_errors
def get_prop_smt_model():
xt, yt, dyt_dxt, xlimits = get_b777_engine()
this_dir = os.path.split(__file__)[0]
interp = RMTC(
num_elements=6,
xlimits=xlimits,
nonlinear_maxiter=20,
approx_order=2,
energy_weight=0.,
regularization_weight=0.,
extrapolate=True,
print_global=False,
data_dir=os.path.join(this_dir, '_smt_cache'),
)
interp.set_training_values(xt, yt)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2)
interp.train()
return interp
def train(self, train_method, **kwargs):
"""Trains the surrogate model with given training data.
Parameters
----------
train_method : str
Training method among ``IDW``, ``KPLS``, ``KPLSK``, ``KRG``, ``LS``, ``QP``, ``RBF``, ``RMTB``, ``RMTC``
kwargs : dict
Additional keyword arguments supported by SMT objects
"""
if train_method == 'IDW':
self.trained = IDW(**kwargs)
elif train_method == 'KPLS':
self.trained = KPLS(**kwargs)
elif train_method == 'KPLSK':
self.trained = KPLSK(**kwargs)
elif train_method == 'KRG':
self.trained = KRG(**kwargs)
elif train_method == 'LS':
self.trained = LS(**kwargs)
elif train_method == 'QP':
self.trained = QP(**kwargs)
elif train_method == 'RBF':
self.trained = RBF(**kwargs)
elif train_method == 'RMTB':
self.trained = RMTB(xlimits=self.limits, **kwargs)
elif train_method == 'RMTC':
self.trained = RMTC(xlimits=self.limits, **kwargs)
else:
raise ValueError(
'train_method must be one between IDW, KPLS, KPLSK, KRG, LS, QP, RBF, RMTB, RMTC'
)
self.trained.set_training_values(self.x_samp, self.m_prop)
self.trained.train()
def setUp(self):
ndim = 2
nt = 10000
ne = 1000
problems = OrderedDict()
problems["sphere"] = Sphere(ndim=ndim)
problems["exp"] = TensorProduct(ndim=ndim, func="exp", width=5)
problems["tanh"] = TensorProduct(ndim=ndim, func="tanh", width=5)
problems["cos"] = TensorProduct(ndim=ndim, func="cos", width=5)
sms = OrderedDict()
sms["LS"] = LS()
sms["QP"] = QP()
if compiled_available:
sms["RMTC"] = RMTC(num_elements=20, energy_weight=1e-10)
sms["RMTB"] = RMTB(num_ctrl_pts=40, energy_weight=1e-10)
t_errors = {}
t_errors["LS"] = 1.0
t_errors["QP"] = 1.0
t_errors["RMTC"] = 1.0
t_errors["RMTB"] = 1.0
e_errors = {}
e_errors["LS"] = 1.5
e_errors["QP"] = 1.5
e_errors["RMTC"] = 1.0
e_errors["RMTB"] = 1.0
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
def setUp(self):
ndim = 2
nt = 10000
ne = 1000
problems = OrderedDict()
problems['sphere'] = Sphere(ndim=ndim)
problems['exp'] = TensorProduct(ndim=ndim, func='exp', width=5)
problems['tanh'] = TensorProduct(ndim=ndim, func='tanh', width=5)
problems['cos'] = TensorProduct(ndim=ndim, func='cos', width=5)
sms = OrderedDict()
sms['LS'] = LS()
sms['QP'] = QP()
if compiled_available:
sms['RMTC'] = RMTC(num_elements=20, energy_weight=1e-10)
sms['RMTB'] = RMTB(num_ctrl_pts=40, energy_weight=1e-10)
t_errors = {}
t_errors['LS'] = 1.0
t_errors['QP'] = 1.0
t_errors['RMTC'] = 1e-2
t_errors['RMTB'] = 1e-2
e_errors = {}
e_errors['LS'] = 1.5
e_errors['QP'] = 1.5
e_errors['RMTC'] = 1e-2
e_errors['RMTB'] = 1e-2
self.nt = nt
self.ne = ne
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
from smt.surrogate_models import RMTC
from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine
xt, yt, dyt_dxt, xlimits = get_b777_engine()
interp = RMTC(
num_elements=6,
xlimits=xlimits,
nonlinear_maxiter=20,
approx_order=2,
energy_weight=0.0,
regularization_weight=0.0,
extrapolate=True,
)
interp.set_training_values(xt, yt)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2)
interp.train()
plot_b777_engine(xt, yt, xlimits, interp)
def setUp(self):
ndim = 3
nt = 100
ne = 100
ncomp = 1
problems = OrderedDict()
problems["exp"] = TensorProduct(ndim=ndim, func="exp")
problems["tanh"] = TensorProduct(ndim=ndim, func="tanh")
problems["cos"] = TensorProduct(ndim=ndim, func="cos")
sms = OrderedDict()
sms["LS"] = LS()
sms["QP"] = QP()
sms["KRG"] = KRG(theta0=[1e-2] * ndim)
sms["MFK"] = MFK(theta0=[1e-2] * ndim)
sms["KPLS"] = KPLS(theta0=[1e-2] * ncomp, n_comp=ncomp)
sms["KPLSK"] = KPLSK(theta0=[1] * ncomp, n_comp=ncomp)
sms["GEKPLS"] = GEKPLS(theta0=[1e-2] * ncomp,
n_comp=ncomp,
delta_x=1e-1)
sms["GENN"] = genn()
if compiled_available:
sms["IDW"] = IDW()
sms["RBF"] = RBF()
sms["RMTC"] = RMTC()
sms["RMTB"] = RMTB()
t_errors = {}
t_errors["LS"] = 1.0
t_errors["QP"] = 1.0
t_errors["KRG"] = 1e0
t_errors["MFK"] = 1e0
t_errors["KPLS"] = 1e0
t_errors["KPLSK"] = 1e0
t_errors["GEKPLS"] = 1e0
t_errors["GENN"] = 1e0
if compiled_available:
t_errors["IDW"] = 1e0
t_errors["RBF"] = 1e-2
t_errors["RMTC"] = 1e-1
t_errors["RMTB"] = 1e-1
e_errors = {}
e_errors["LS"] = 1.5
e_errors["QP"] = 1.5
e_errors["KRG"] = 1e-2
e_errors["MFK"] = 1e-2
e_errors["KPLS"] = 1e-2
e_errors["KPLSK"] = 1e-2
e_errors["GEKPLS"] = 1e-2
e_errors["GENN"] = 1e-2
if compiled_available:
e_errors["IDW"] = 1e0
e_errors["RBF"] = 1e0
e_errors["RMTC"] = 2e-1
e_errors["RMTB"] = 2e-1
self.nt = nt
self.ne = ne
self.ndim = ndim
self.problems = problems
self.sms = sms
self.t_errors = t_errors
self.e_errors = e_errors
from smt.surrogate_models import RMTC
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 = RMTC(
num_elements=40,
xlimits=xlimits,
nonlinear_maxiter=20,
solver_tolerance=1e-16,
energy_weight=1e-14,
regularization_weight=0.0,
)
interp.set_training_values(xt, yt)
interp.train()
plot_one_d_step(xt, yt, xlimits, interp)
ax = plt.subplot(nrow, ncol, 1)
ax.legend(["α = 3,", "α = 2", "α = 1", "α = 0", "α = -1", "α = -2", "α = -3"], title = "Blade Angle of Attack (°)")
plt.tight_layout(rect = [0,0.03,1,0.95])
plt.savefig('smt_slice.pdf')
plt.show()
if __name__ == '__main__':
xt, yt, xlimits = get_propeller_smt()
interp = RMTC(
num_elements=50,
xlimits = xlimits, nonlinear_maxiter =0, min_energy = True,
regularization_weight = 0e-10,
smoothness=[1e1,1e3],
energy_weight=1e3,
# data_dir = "work",
print_global=False,
approx_order = 2,
extrapolate = True,
)
interp.set_training_values(xt, yt)
interp.train()
# angle of attack of the blade , pitch angle
x = np.array([
# 3., 14.28
[-3., 2],
])
y = interp.predict_values(x)
print('C_T:', y[:, 0])
print('C_Q:', y[:, 1])
axarr[k, l].plot(ydtest[:, i], ydtest[:, i], "-.")
axarr[k, l].plot(ydtest[:, i], yd_prediction[:, i], ".")
if l == 1:
l = 0
k += 1
else:
l += 1
if plot_status:
plt.show()
########### The RMTC model
t = RMTC(
xlimits=fun.xlimits,
min_energy=True,
nonlinear_maxiter=20,
print_prediction=False,
)
t.set_training_values(xt, yt[:, 0])
# 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
y = t.predict_values(xtest)
print("RMTC, err: " + str(compute_rms_error(t, xtest, ytest)))
if plot_status:
k, l = 0, 0
from smt.surrogate_models import RMTC
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
xt, yt, xlimits = get_rans_crm_wing()
interp = RMTC(
num_elements=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-10
)
interp.set_training_values(xt, yt)
interp.train()
plot_rans_crm_wing(xt, yt, xlimits, interp)