def gp_interpolator(x, y, res=1000, Nparam=3, decouple_sfr=False):
yerr = np.zeros_like(y)
yerr[2:(2 + Nparam)] = 0.001 / np.sqrt(Nparam)
if len(yerr) > 26:
yerr[2:(2 + Nparam)] = 0.1 / np.sqrt(Nparam)
if decouple_sfr == True:
yerr[(2 + Nparam):] = 0.1
#if decouple_sfr == True:
# yerr[-2:] = 0.1/np.sqrt(Nparam)
#else:
# yerr[-2:] = 0.01/np.sqrt(Nparam)
#kernel = np.var(yax) * kernels.ExpSquaredKernel(np.median(yax)+np.std(yax))
#k2 = np.var(yax) * kernels.LinearKernel(np.median(yax),order=1)
#kernel = np.var(y) * kernels.Matern32Kernel(np.median(y)) #+ k2
kernel = np.var(y) * (kernels.Matern32Kernel(np.median(y)) +
kernels.LinearKernel(np.median(y), order=2))
gp = george.GP(kernel, solver=george.HODLRSolver)
#print(xax.shape, yerr.shape)
gp.compute(x.ravel(), yerr.ravel())
# optimize kernel parameters
# p0 = gp.get_parameter_vector()
# results = minimize(nll, p0, jac=grad_nll, method="L-BFGS-B", args = (gp, y))
# gp.set_parameter_vector(results.x)
x_pred = np.linspace(np.amin(x), np.amax(x), res)
y_pred, pred_var = gp.predict(y.ravel(), x_pred, return_var=True)
return x_pred, y_pred
def generate_data(kernel, N=50, rng=(-5, 5)):
var = np.random.randn() * 0.5
if kernel == 'SE':
gp = george.GP(0.1 * kernels.ExpSquaredKernel(5 + var))
elif kernel == 'M32':
gp = george.GP(0.1 * kernels.Matern32Kernel(5 + var))
elif kernel == 'PER':
gp = george.GP(0.1 * kernels.CosineKernel(log_period=0.25 + var / 8))
elif kernel == 'LIN':
gp = george.GP(0.1 *
kernels.LinearKernel(order=1, log_gamma2=1.25 + var))
else:
gp = None
x = rng[0] + np.diff(rng) * np.sort(np.random.rand(N))
y = gp.sample(x)
return x, y
def gp_interpolator(x,y,res = 1000, Nparam = 3):
yerr = np.zeros_like(y)
yerr[2:(2+Nparam)] = 0.001/np.sqrt(Nparam)
if len(yerr) > 26:
yerr[2:(2+Nparam)] = 0.1/np.sqrt(Nparam)
#kernel = np.var(yax) * kernels.ExpSquaredKernel(np.median(yax)+np.std(yax))
#k2 = np.var(yax) * kernels.LinearKernel(np.median(yax),order=1)
#kernel = np.var(y) * kernels.Matern32Kernel(np.median(y)) #+ k2
kernel = np.var(y) * (kernels.Matern32Kernel(np.median(y)) + kernels.LinearKernel(np.median(y), order=2))
gp = george.GP(kernel)
#print(xax.shape, yerr.shape)
gp.compute(x.ravel(), yerr.ravel())
x_pred = np.linspace(np.amin(x), np.amax(x), res)
y_pred, pred_var = gp.predict(y.ravel(), x_pred, return_var=True)
return x_pred, y_pred
def get_kernel(architecture, kernel_name, domain_name_lst, n_dims):
if architecture == "trans":
mapping = trans_domain_kernel_mapping
else:
mapping = None
kernel = None
initial_ls = np.ones([n_dims])
if kernel_name == "constant":
kernel = kernels.ConstantKernel(1, ndim=n_dims)
elif kernel_name == "polynomial":
kernel = kernels.PolynomialKernel(log_sigma2=1, order=3, ndim=n_dims)
elif kernel_name == "linear":
kernel = kernels.LinearKernel(log_gamma2=1, order=3, ndim=n_dims)
elif kernel_name == "dotproduct":
kernel = kernels.DotProductKernel(ndim=n_dims)
elif kernel_name == "exp":
kernel = kernels.ExpKernel(initial_ls, ndim=n_dims)
elif kernel_name == "expsquared":
kernel = kernels.ExpSquaredKernel(initial_ls, ndim=n_dims)
elif kernel_name == "matern32":
kernel = kernels.Matern32Kernel(initial_ls, ndim=n_dims)
elif kernel_name == "matern52":
kernel = kernels.Matern52Kernel(initial_ls, ndim=n_dims)
elif kernel_name == "rationalquadratic":
kernel = kernels.RationalQuadraticKernel(log_alpha=1,
metric=initial_ls,
ndim=n_dims)
elif kernel_name == "expsine2":
kernel = kernels.ExpSine2Kernel(1, 2, ndim=n_dims)
elif kernel_name == "heuristic":
kernel = mapping[domain_name_lst[0]](ndim=n_dims, axes=0)
for i in range(len(domain_name_lst[1:])):
d = domain_name_lst[1:][i]
kernel += mapping[d](ndim=n_dims, axes=i)
elif kernel_name == "logsquared":
kernel = kernels.LogSquaredKernel(initial_ls, ndim=n_dims)
return kernel
kernels.CosineKernel(log_period=0.5, ndim=2, axes=1),
kernels.CosineKernel(log_period=0.75, ndim=5, axes=[2, 3]),
kernels.ExpSine2Kernel(gamma=0.4, log_period=1.0),
kernels.ExpSine2Kernel(gamma=12., log_period=0.5, ndim=2),
kernels.ExpSine2Kernel(gamma=17., log_period=0.5, ndim=2, axes=1),
kernels.ExpSine2Kernel(gamma=13.7, log_period=-0.75, ndim=5, axes=[2, 3]),
kernels.ExpSine2Kernel(gamma=-0.7, log_period=0.75, ndim=5, axes=[2, 3]),
kernels.ExpSine2Kernel(gamma=-10, log_period=0.75),
kernels.LocalGaussianKernel(log_width=0.5, location=1.0),
kernels.LocalGaussianKernel(log_width=0.1, location=0.5, ndim=2),
kernels.LocalGaussianKernel(log_width=1.5, location=-0.5, ndim=2, axes=1),
kernels.LocalGaussianKernel(log_width=2.0,
location=0.75,
ndim=5,
axes=[2, 3]),
kernels.LinearKernel(order=0, log_gamma2=0.0),
kernels.LinearKernel(order=2, log_gamma2=0.0),
kernels.LinearKernel(order=2, log_gamma2=0.0),
kernels.LinearKernel(order=5, log_gamma2=1.0, ndim=2),
kernels.LinearKernel(order=3, log_gamma2=-1.0, ndim=5, axes=2),
kernels.LinearKernel(order=0, log_gamma2=0.0) +
kernels.LinearKernel(order=1, log_gamma2=-1.0) +
kernels.LinearKernel(order=2, log_gamma2=-2.0),
kernels.PolynomialKernel(order=0, log_sigma2=-10.0),
kernels.PolynomialKernel(order=2, log_sigma2=-10.0),
kernels.PolynomialKernel(order=2, log_sigma2=0.0),
kernels.PolynomialKernel(order=5, log_sigma2=1.0, ndim=2),
kernels.PolynomialKernel(order=3, log_sigma2=-1.0, ndim=5, axes=2),
12. * kernels.ExpSine2Kernel(gamma=0.4, log_period=1.0, ndim=5),
12. * kernels.ExpSquaredKernel(0.4, ndim=3) + 0.1,
]