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)