def main():
ndim = 2
np.random.seed(2)
tf = 25
nsteps = 1000
u_init = [0, 0]
noise = Noise([0, tf])
oscil = Oscillator(noise, tf, nsteps, u_init)
myMap = BlackBox(map_def, args=(oscil, ))
lam = noise.get_eigenvalues(ndim)
mean = np.zeros(ndim)
cov = np.diag(lam)
domain = [[-a, a] for a in 6.0 * np.sqrt(np.diag(cov))]
inputs = GaussianInputs(domain, mean, cov)
X = inputs.draw_samples(15, "lhs")
Y = myMap.evaluate(X)
o = OptimalDesign(X, Y, myMap, inputs, normalize_Y=True)
likelihood = Likelihood(o.model, o.inputs)
x_new = np.atleast_2d([1.0, 2.0])
qcrit = Q(o.model, o.inputs, likelihood=likelihood)
print(qcrit.evaluate(x_new))
print(qcrit.jacobian(x_new))
qcrit = QInt(o.model, o.inputs, ngrid=250, likelihood=likelihood)
print(qcrit.evaluate(x_new))
print(qcrit.jacobian(x_new))
def main():
ndim = 2
np.random.seed(3)
tf = 25
nsteps = 1000
u_init = [0, 0]
noise = Noise([0, tf])
oscil = Oscillator(noise, tf, nsteps, u_init)
myMap = BlackBox(map_def, args=(oscil,))
lam = noise.get_eigenvalues(ndim)
mean = np.zeros(ndim)
cov = np.diag(lam)
domain = [ [-a, a] for a in 6.0*np.sqrt(np.diag(cov)) ]
inputs = GaussianInputs(domain, mean, cov)
#inputs = UniformInputs(domain)
kwargs_gmm = dict(n_components=4, covariance_type="spherical")
X = inputs.draw_samples(100, "lhs")
Y = myMap.evaluate(X, parallel=True)
o = OptimalDesign(X, Y, myMap, inputs, normalize_Y=True)
likelihood = Likelihood(o.model, o.inputs, "nominal", kwargs_gmm=kwargs_gmm)
x_new = np.atleast_2d([1.0,2.0])
gmm_y = likelihood.evaluate(x_new)
print(jacobian_fdiff(likelihood, x_new))
print(likelihood.jacobian(x_new))
from GPy.models import GradientChecker
gm = GradientChecker(lambda x: likelihood.evaluate(x),
lambda x: likelihood.jacobian(x),
x_new, 'x')
assert(gm.checkgrad())
pts = inputs.draw_samples(n_samples=100, sample_method="grd")
gmm_y = likelihood.evaluate(pts).flatten()
pix = likelihood._evaluate_raw(pts).flatten()
fig = plt.figure(figsize=(12,6))
plt.subplot(1,2,1)
sc = plt.scatter(pts[:,0], pts[:,1], c=pix)
plt.colorbar(sc)
plt.title(r"$f_x/f_y$")
plt.subplot(1,2,2)
sc = plt.scatter(pts[:,0], pts[:,1], c=gmm_y)
plt.colorbar(sc)
plt.title("GMM fit")
plt.show()
def test_likelihood_gradients_importance():
X = inputs.draw_samples(100, "lhs")
Y = myMap.evaluate(X, parallel=True)
o = OptimalDesign(X, Y, myMap, inputs, normalize_Y=True)
x_new = np.atleast_2d([1.0, 2.0])
kwargs_gmm = dict(n_components=4, covariance_type="full")
likelihood = Likelihood(o.model,
o.inputs,
"importance",
kwargs_gmm=kwargs_gmm)
gm = GradientChecker(lambda x: likelihood.evaluate(x),
lambda x: likelihood.jacobian(x), x_new, 'x')
assert (gm.checkgrad())
def main():
ndim = 2
np.random.seed(2)
tf = 25
nsteps = 1000
u_init = [0, 0]
noise = Noise([0, tf])
oscil = Oscillator(noise, tf, nsteps, u_init)
myMap = BlackBox(map_def, args=(oscil, ))
lam = noise.get_eigenvalues(ndim)
mean = np.zeros(ndim)
cov = np.diag(lam)
acq_list = [(Q, QInt), (IVR_LW, IVR_LWInt), (IVR, IVRInt)]
for (Acq, AcqInt) in acq_list:
domain = [[-a, a] for a in 6.0 * np.sqrt(np.diag(cov))]
inputs = GaussianInputs(mean, cov, domain)
X = inputs.draw_samples(15, "lhs")
Y = myMap.evaluate(X)
o = OptimalDesign(X, Y, myMap, inputs, normalize_Y=True)
x_new = np.atleast_2d([1.0, 2.0])
likelihood = Likelihood(o.model, o.inputs)
if isinstance(Acq, type(AcquisitionWeighted)):
acq = Acq(o.model, o.inputs, likelihood=likelihood)
else:
acq = Acq(o.model, o.inputs)
print(acq.evaluate(x_new))
print(acq.jacobian(x_new))
domain = [[-a, a] for a in 20.0 * np.sqrt(np.diag(cov))]
inputs.set_domain(domain)
if isinstance(Acq, type(AcquisitionWeighted)):
acqint = AcqInt(o.model,
o.inputs,
ngrid=250,
likelihood=likelihood)
else:
acqint = AcqInt(o.model, o.inputs, ngrid=250)
print(acqint.evaluate(x_new))
print(acqint.jacobian(x_new))