def test_qp(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import QP
xt = np.array([[0.0, 1.0, 2.0, 3.0, 4.0]]).T
yt = np.array([[0.2, 1.4, 1.5, 0.5, 1.0], [0.0, 1.0, 2.0, 4, 3]]).T
sm = QP()
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
t1, _ = plt.plot(xt, yt[:, 0], "o", "C0")
p1 = plt.plot(x, y[:, 0], "C0", label="Prediction 1")
t2, _ = plt.plot(xt, yt[:, 1], "o", "C1")
p2 = plt.plot(x, y[:, 1], "C1", label="Prediction 2")
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.show()
def quadraticModel(xt, yt, xtest, ytest):
########### The QP model
t = QP(print_prediction=False)
t.set_training_values(xt, yt)
t.train()
title = 'QP model: validation of the prediction model'
print('QP, err: ' + str(compute_rms_error(t, xtest, ytest)))
return t, title, xtest, ytest
def test_ny(self):
xt, yt, _ = get_rans_crm_wing()
interp = QP()
interp.set_training_values(xt, yt)
interp.train()
v0 = np.zeros((4, 2))
for ix, i in enumerate([10, 11, 12, 13]):
v0[ix, :] = interp.predict_values(np.atleast_2d(xt[i, :]))
v1 = interp.predict_values(np.atleast_2d(xt[10:14, :]))
expected_diff = np.zeros((4, 2))
np.testing.assert_allclose(v1 - v0, expected_diff, atol=1e-15)
class SS_model_QP(SS_model_base):
def __init__(self, data, x_headers, y_header):
super().__init__(data, x_headers, y_header)
self.model = QP(print_training=False, print_global=False)
def get_hyperparameters(self):
return "quadratic model"
def fit(self):
self.model.set_training_values(self.X_train, self.y_train)
self.model.train()
def model_predict(self):
X = np.array(self.X_pred)
self.y_pred = self.model.predict_values(X)
self.y_std = None
def test_qp(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import QP
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 = QP()
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_qp(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import QP
xt = np.array([0., 1., 2., 3., 4.])
yt = np.array([0., 1., 1.5, 0.5, 1.0])
sm = QP()
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()