def __init__(self):
self.real_dim = 2
self.active_dim = 1
self.no_samples = 5
self.kernel = TripathyMaternKernel(self.real_dim, self.active_dim)
# Parameters
self.sn = 2.
self.W = self.kernel.sample_W()
self.function = Parabola()
self.real_W = np.asarray([
[1],
[1]
])
self.real_W = self.real_W / np.linalg.norm(self.real_W)
self.X = np.random.rand(self.no_samples, self.real_dim)
Z = np.dot(self.X, self.real_W).reshape((-1, 1))
self.Y = self.function.f(Z.T).squeeze()
self.w_optimizer = t_WOptimizer(
self.kernel, # TODO: does the kernel take over the W?
self.sn,
np.asscalar(self.kernel.inner_kernel.variance),
self.kernel.inner_kernel.lengthscale,
self.X, self.Y
)
# Define the plotting variables
self.tau_arr = np.linspace(0., self.w_optimizer.tau_max, 100)
def __init__(self):
self.real_dim = 2
self.active_dim = 1
self.no_samples = 50
self.kernel = TripathyMaternKernel(self.real_dim, self.active_dim)
# Parameters
self.sn = 0.1
self.W = self.kernel.sample_W()
self.function = Parabola()
self.real_W = np.asarray([
[1],
[1]
])
self.real_W = self.real_W / np.linalg.norm(self.real_W)
self.X = np.random.rand(self.no_samples, self.real_dim)
Z = np.dot(self.X, self.real_W)
self.Y = self.function.f(Z.T).reshape(-1, 1)
self.w_optimizer = t_WOptimizer(
self.kernel, # TODO: does the kernel take over the W?
self.sn,
np.asscalar(self.kernel.inner_kernel.variance),
self.kernel.inner_kernel.lengthscale,
self.X, self.Y
)
self.no_tries = 1000
def init(self):
self.real_dim = 2
self.active_dim = 1
self.no_samples = 75
self.kernel = TripathyMaternKernel(self.real_dim, self.active_dim)
# Hide the matrix over here!
if self.real_dim == 3 and self.active_dim == 2:
self.function = Camelback()
self.real_W = np.asarray([
[0, 1],
[1, 0],
[0, 0]
])
elif self.real_dim == 2 and self.active_dim == 1:
self.function = Parabola()
self.real_W = np.asarray([
[1],
[1],
])
self.real_W = self.real_W / np.linalg.norm(self.real_W)
else:
assert False, "W was not set!"
self.sn = 0.1
self.X = np.random.rand(self.no_samples, self.real_dim)
Z = np.dot(self.X, self.real_W)
self.Y = self.function.f(Z.T).reshape(-1, 1)
self.w_optimizer = t_WOptimizer(
self.kernel,
self.sn,
np.asscalar(self.kernel.inner_kernel.variance),
self.kernel.inner_kernel.lengthscale,
self.X, self.Y
)
# We create the following kernel just to have access to the sample_W function!
# TripathyMaternKernel(self.real_dim)
self.tries = 10
self.max_iter = 1 # 150
assert False
self.metrics = Metrics(self.no_samples)
def init(self):
self.real_dim = 3
self.active_dim = 2
self.no_samples = 5
self.kernel = TripathyMaternKernel(self.real_dim, self.active_dim)
self.W = self.kernel.sample_W()
self.sn = 2.
self.function = Rosenbrock()
self.real_W = np.asarray([[0, 0], [0, 1], [1, 0]])
self.function = Rosenbrock()
self.X = np.random.rand(self.no_samples, self.real_dim)
Z = np.dot(self.X, self.real_W)
self.Y = self.function.f(Z.T)
self.w_optimizer = t_WOptimizer(
self.kernel, self.sn,
np.asscalar(self.kernel.inner_kernel.variance),
self.kernel.inner_kernel.lengthscale, self.X, self.Y)
def __init__(self):
self.real_dim = 2
self.active_dim = 1
self.no_samples = 100
self.kernel = TripathyMaternKernel(self.real_dim, self.active_dim)
# Parameters
self.sn = 0.1 # 1e-7 # 0.1
self.W = self.kernel.sample_W()
self.function = AugmentedSinusoidal()
self.real_W = np.asarray([
[3],
[1]
])
self.real_W = self.real_W / np.linalg.norm(self.real_W)
# [[0.9486833]
# [0.31622777]]
x_range = np.linspace(0., 1., int(np.sqrt(self.no_samples)))
y_range = np.linspace(0., 1., int(np.sqrt(self.no_samples)))
self.X = cartesian([x_range, y_range])
#self.X = np.random.rand(self.no_samples, self.real_dim)
print(self.X.shape)
Z = np.dot(self.X, self.real_W).reshape(-1, 1)
print(Z.shape)
self.Y = self.function.f(Z.T).reshape(-1, 1)
self.w_optimizer = t_WOptimizer(
self.kernel, # TODO: does the kernel take over the W?
self.sn,
np.asscalar(self.kernel.inner_kernel.variance),
self.kernel.inner_kernel.lengthscale,
self.X, self.Y
)
self.no_tries = 1000
self.PLOT_MEAN = True