class KernelExpFiniteGaussianSurrogate(StaticSurrogate):
def __init__(self, ndim, sigma, lmbda, m):
self.surrogate = KernelExpFiniteGaussian(sigma, lmbda, m, D=ndim)
def train(self, samples):
self.surrogate.fit(samples)
def log_pdf_gradient(self, x):
return self.surrogate.grad(x)
surrogate = KernelExpFiniteGaussian(sigma=sigma, lmbda=lmbda, m=m, D=benchmark_samples.shape[1])
surrogate.fit(benchmark_samples)
fake = empty_class()
def replace_2(x_2d, a, i, j):
a = a.copy()
a[i] = x_2d[0]
a[j] = x_2d[1]
return a
for i in range(benchmark_samples.shape[1]):
for j in range(benchmark_samples.shape[1]):
if i == j:
continue
fake.log_pdf = lambda x_2d: surrogate.log_pdf(replace_2(x_2d, true_mean, i, j))
fake.grad = lambda x_2d: surrogate.grad(replace_2(x_2d, true_mean, i, j))
visualise_fit_2d(fake, benchmark_samples[:, [i, j]],
Xs=np.linspace(benchmark_samples[:, i].min(), benchmark_samples[:, i].max(), 30),
Ys=np.linspace(benchmark_samples[:, j].min(), benchmark_samples[:, j].max(), 30),
)
plt.show()
plt.plot(log2_sigmas, Js_mean, 'b-')
plt.plot(log2_sigmas, Js_mean - 2 * np.sqrt(Js_var[i]), 'b--')
plt.plot(log2_sigmas, Js_mean + 2 * np.sqrt(Js_var[i]), 'b--')
plt.show()
surrogate.fit(benchmark_samples)
fake = empty_class()
def replace_2(x_2d, a, i, j):
a = a.copy()
a[i] = x_2d[0]
a[j] = x_2d[1]
return a
for i in range(benchmark_samples.shape[1]):
for j in range(benchmark_samples.shape[1]):
if i == j:
continue
fake.log_pdf = lambda x_2d: surrogate.log_pdf(
replace_2(x_2d, true_mean, i, j))
fake.grad = lambda x_2d: surrogate.grad(
replace_2(x_2d, true_mean, i, j))
visualise_fit_2d(
fake,
benchmark_samples[:, [i, j]],
Xs=np.linspace(benchmark_samples[:, i].min(),
benchmark_samples[:, i].max(), 30),
Ys=np.linspace(benchmark_samples[:, j].min(),
benchmark_samples[:, j].max(), 30),
)
plt.show()
plt.plot(log2_sigmas, Js_mean, 'b-')
plt.plot(log2_sigmas, Js_mean - 2 * np.sqrt(Js_var[i]), 'b--')
plt.plot(log2_sigmas, Js_mean + 2 * np.sqrt(Js_var[i]), 'b--')
plt.show()
