def test_1d_kriging2(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([0.738513784857542,-0.210367746201974,-0.489015457891476,12.3033138316612])
krig1 = KrigingSurrogate()
krig1.train(x,y)
self.assertAlmostEqual(1.18375,krig1.thetas,places=7)
def test_get_uncertain_value(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([0.738513784857542,-0.210367746201974,-0.489015457891476,12.3033138316612])
krig1 = KrigingSurrogate()
krig1.train(x,y)
self.assertEqual(krig1.get_uncertain_value(1).mu,NormalDistribution(1.,0.).mu)
self.assertEqual(krig1.get_uncertain_value(1).sigma,NormalDistribution(1.,0.).sigma)
def test_1d_kriging3(self):
# Test for least squares solver utilization when ill-conditioned
x = [[case] for case in linspace(0., 1., 40)]
y = sin(x).flatten()
krig1 = KrigingSurrogate()
krig1.train(x, y)
new_x = array([0.5])
pred = krig1.predict(new_x)
self.assertAlmostEqual(8.7709e-09, pred.sigma, places=7)
self.assertAlmostEqual(0.479425538688, pred.mu, places=7)
def test_1d_kriging3(self):
# Test for least squares solver utilization when ill-conditioned
x = [[case] for case in linspace(0.,1.,40)]
y = sin(x).flatten()
krig1 = KrigingSurrogate()
krig1.train(x,y)
new_x = array([0.5])
pred = krig1.predict(new_x)
self.assertAlmostEqual(8.7709e-09,pred.sigma,places=7)
self.assertAlmostEqual(0.479425538688,pred.mu,places=7)
def test_1d_kriging_predictor(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([0.738513784857542,-0.210367746201974,-0.489015457891476,12.3033138316612])
krig1 = KrigingSurrogate()
krig1.train(x,y)
new_x = array([0.5])
pred = krig1.predict(new_x)
self.assertTrue(isinstance(pred,NormalDistribution))
self.assertAlmostEqual(2.5086,pred.sigma,places=3)
self.assertAlmostEqual(-1.37201,pred.mu,places=3)
def test_get_uncertain_value(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([
0.738513784857542, -0.210367746201974, -0.489015457891476,
12.3033138316612
])
krig1 = KrigingSurrogate()
krig1.train(x, y)
self.assertEqual(
krig1.get_uncertain_value(1).mu,
NormalDistribution(1., 0.).mu)
self.assertEqual(
krig1.get_uncertain_value(1).sigma,
NormalDistribution(1., 0.).sigma)
def test_1d_kriging_predictor(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([
0.738513784857542, -0.210367746201974, -0.489015457891476,
12.3033138316612
])
krig1 = KrigingSurrogate()
krig1.train(x, y)
new_x = array([0.5])
pred = krig1.predict(new_x)
self.assertTrue(isinstance(pred, NormalDistribution))
self.assertAlmostEqual(2.5086, pred.sigma, places=3)
self.assertAlmostEqual(-1.37201, pred.mu, places=3)
def test_2d_kriging(self):
def bran(x):
y = (x[1]-(5.1/(4.*pi**2.))*x[0]**2.+5.*x[0]/pi-6.)**2.+10.*(1.-1./(8.*pi))*cos(x[0])+10.
return y
x = array([[-2.,0.],[-0.5,1.5],[1.,3.],[8.5,4.5],[-3.5,6.],[4.,7.5],[-5.,9.],[5.5,10.5],
[10.,12.],[7.,13.5],[2.5,15.]])
y = array([bran(case) for case in x])
krig1 = KrigingSurrogate()
krig1.train(x,y)
pred = krig1.predict([-2.,0.])
self.assertAlmostEqual(bran(x[0]),pred.mu,places=5)
pred = krig1.predict([5.,5.])
self.assertAlmostEqual(14.513550,pred.sigma,places=2)
self.assertAlmostEqual(18.759264,pred.mu,places=2)
self.assertAlmostEqual(8.7709e-09, pred.sigma, places=7)
self.assertAlmostEqual(0.479425538688, pred.mu, places=7)
def test_2d_kriging(self):
def bran(x):
y = (x[1] - (5.1 / (4. * pi**2.)) * x[0]**2. + 5. * x[0] / pi -
6.)**2. + 10. * (1. - 1. / (8. * pi)) * cos(x[0]) + 10.
return y
x = array([[-2., 0.], [-0.5, 1.5], [1., 3.], [8.5, 4.5], [-3.5, 6.],
[4., 7.5], [-5., 9.], [5.5, 10.5], [10., 12.], [7., 13.5],
[2.5, 15.]])
y = array([bran(case) for case in x])
krig1 = KrigingSurrogate()
krig1.train(x, y)
pred = krig1.predict([-2., 0.])
self.assertAlmostEqual(bran(x[0]), pred.mu, places=5)
pred = krig1.predict([5., 5.])
self.assertAlmostEqual(14.513550, pred.sigma, places=2)
self.assertAlmostEqual(18.759264, pred.mu, places=2)
def test_get_uncertain_value(self):
x = array([[0.05], [.25], [0.61], [0.95]])
y = array([
0.738513784857542, -0.210367746201974, -0.489015457891476,
12.3033138316612
])
krig1 = KrigingSurrogate()
from time import time
import numpy as np
from numpy import pi, cos, sin
from openmdao.lib.surrogatemodels.kriging_surrogate import KrigingSurrogate
np.random.seed(12345)
N = 200
def bran(x):
y = (x[1]-(5.1/(4.*pi**2.))*x[0]**2.+5.*x[0]/pi-6.)**2.+10.*(1.-1./(8.*pi))*cos(x[0])+10.
return y
x = np.random.random((N, 2))*10.0
y = np.array([bran(case) for case in x])
krig1 = KrigingSurrogate()
t0 = time()
krig1.train(x, y)
print 'Training Time elapsed', time() - t0
xx = np.random.random((N, 2))*10.0
t0 = time()
for jj in range(len(xx)):
pred = krig1.predict(xx[jj, :])
print 'predicting Time elapsed', time() - t0
