def test_evaluate_grad_respect_point(self):
result = ScaledKernel.evaluate_grad_respect_point(
np.array([5.0, 1.0]), np.array([[1]]), np.array([[4], [5]]), 1,
*([MATERN52_NAME], ))
kernel = ScaledKernel.define_kernel_from_array(1, np.array([5.0, 1.0]),
*([MATERN52_NAME], ))
assert np.all(result == kernel.grad_respect_point(
np.array([[1]]), np.array([[4], [5]])))
def test_gradient_respect_parameters_finite_differences(self):
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
dh = 0.00000001
finite_diff = FiniteDifferences.forward_difference(
lambda params: ScaledKernel.evaluate_cov_defined_by_params(
params, inputs_1, 2, *([MATERN52_NAME], )),
np.array([2.0, 3.0, 4.0]), np.array([dh]))
gradient = ScaledKernel.evaluate_grad_defined_by_params_respect_params(
np.array([2.0, 3.0, 4.0]), inputs_1, 2, *([MATERN52_NAME], ))
for i in range(3):
npt.assert_almost_equal(finite_diff[i], gradient[i])
def setUp(self):
np.random.seed(2)
n_points = 100
normal_noise = np.random.normal(0, 1.0, n_points)
points = np.linspace(0, 10, n_points)
points = points.reshape([n_points, 1])
kernel = Matern52.define_kernel_from_array(1, np.array([2.0]))
self.sigma2 = ParameterEntity(SIGMA2_NAME, np.array([1.0]), None)
kernel = ScaledKernel(1, kernel, self.sigma2)
function = SampleFunctions.sample_from_gp(points, kernel)
function = function[0, :]
evaluations = function + normal_noise + 10.0
self.training_data_gp = {
"evaluations": list(evaluations),
"points": points,
"var_noise": []
}
bounds = None
self.gp_gaussian = GPFittingGaussian([MATERN52_NAME],
self.training_data_gp, [1],
bounds,
max_steps_out=1000)
self.gp_gaussian_2 = GPFittingGaussian([SCALED_KERNEL, MATERN52_NAME],
self.training_data_gp, [1],
bounds,
max_steps_out=1000)
def test_evaluate_grad_cross_cov_respect_point(self):
value = self.gp.evaluate_grad_cross_cov_respect_point(np.array([[40.0]]),
np.array([[39.0], [38.0]]),
np.array([1.0, 1.0]))
value_2 = ScaledKernel.evaluate_grad_respect_point(np.array([1.0, 1.0]),
np.array([[40.0]]),
np.array([[39.0], [38.0]]), 1,
*([MATERN52_NAME],))
assert np.all(value == value_2)
type_kernel = [MATERN52_NAME]
training_data = {
"evaluations":
[42.2851784656, 72.3121248508, 1.0113231069, 30.9309246906, 15.5288331909],
"points": [
[42.2851784656], [72.3121248508], [1.0113231069], [30.9309246906], [15.5288331909]],
"var_noise": []}
dimensions = [1]
gp = GPFittingGaussian(type_kernel, training_data, dimensions)
value = gp.evaluate_grad_cross_cov_respect_point(np.array([[40.0]]),
np.array([[39.0], [38.0]]),
np.array([1.0]))
value_2 = Matern52.evaluate_grad_respect_point(np.array([1.0]),
np.array([[40.0]]),
np.array([[39.0], [38.0]]), 1)
assert np.all(value == value_2)
def test_define_default_kernel_2(self):
kernel = ScaledKernel.define_default_kernel(1, None, None, None,
*([MATERN52_NAME], ))
assert kernel.name == MATERN52_NAME
assert kernel.dimension == 1
assert kernel.dimension_parameters == 2
assert kernel.sigma2.value == [1.0]
assert kernel.kernel.length_scale.value == [1.0]
def setUp(self):
self.dimension = 2
self.length_scale = ParameterEntity('scale', np.array([1.0, 2.0]),
None)
self.sigma2 = ParameterEntity('sigma2', np.array([3]), None)
self.matern52 = Matern52(self.dimension, self.length_scale)
self.matern52 = ScaledKernel(self.dimension, self.matern52,
self.sigma2)
def test_define_prior_parameters(self):
training_data = {
"evaluations": np.array([1.0, 2.0]),
"points": np.array([[1.0], [2.0]]),
"var_noise": None
}
assert ScaledKernel.define_prior_parameters(training_data, 0,
None) == {
SIGMA2_NAME: 0.25,
}
def setUp(self):
self.dimension = 2
self.length_scale = ParameterEntity('scale', np.array([1.0, 2.0]),
None)
self.sigma2 = ParameterEntity('sigma2', np.array([3]), None)
self.matern52 = Matern52(self.dimension, self.length_scale)
self.matern52 = ScaledKernel(self.dimension, self.matern52,
self.sigma2)
self.inputs = np.array([[1, 0], [0, 1]])
self.prior = UniformPrior(2, [1, 1], [100, 100])
self.prior_2 = UniformPrior(1, [1], [100])
self.matern52_ = Matern52(
2,
ParameterEntity(LENGTH_SCALE_NAME, np.array([2.0, 3.0]),
self.prior))
self.matern52_ = ScaledKernel(
self.dimension, self.matern52_,
ParameterEntity('sigma2', np.array([4.0]), self.prior_2))
def test_evaluate_hessian_respect_point(self):
point = np.array([[4.5, 7.5]])
inputs = np.array([[5.0, 6.0], [8.0, 9.0]])
params = np.array([1.0, 5.0, 3.0])
result = ScaledKernel.evaluate_hessian_respect_point(
params, point, inputs, 2, *([MATERN52_NAME], ))
dh = 0.00001
finite_diff = FiniteDifferences.second_order_central(
lambda x: ScaledKernel.evaluate_cross_cov_defined_by_params(
params, x.reshape(
(1, len(x))), inputs, 2, *([MATERN52_NAME], )),
point[0, :], np.array([dh]))
for i in xrange(2):
for j in xrange(2):
npt.assert_almost_equal(
finite_diff[i, j],
np.array([[result[0, i, j], result[1, i, j]]]),
decimal=5)
def test_compare_kernels(self):
kernel_t = TasksKernel(self.dimension, np.array([0.0]))
assert ScaledKernel.compare_kernels(self.matern52, kernel_t) is False
kernel_s = Matern52(3, self.length_scale)
assert ScaledKernel.compare_kernels(self.matern52, kernel_s) is False
kernel_s = Matern52(2, self.length_scale)
assert ScaledKernel.compare_kernels(self.matern52, kernel_s) is False
sigma2 = ParameterEntity('sigma2', np.array([1]), None)
kernel = ScaledKernel(self.dimension, kernel_s, sigma2)
assert ScaledKernel.compare_kernels(self.matern52, kernel) is False
kernel_s = Matern52(
2, ParameterEntity('scale', np.array([1.0, 3.0]), None))
kernel = ScaledKernel(self.dimension, kernel_s, self.sigma2)
assert ScaledKernel.compare_kernels(self.matern52, kernel) is False
def test_cross_cov(self):
r2 = np.array([[0.0, 1.25], [1.25, 0.0]])
r = np.sqrt(r2)
left_term = ((1.0 + np.sqrt(5) * r + (5.0 / 3.0) * r2) *
np.exp(-np.sqrt(5) * r) * np.array([3]))[0, 1]
comparisons = left_term == self.matern52.cross_cov(
self.inputs, self.inputs)[0, 1]
assert np.all(comparisons)
point_1 = np.array([[2.0, 4.0]])
point_2 = np.array([[3.0, 5.0]])
matern52 = Matern52(
2, ParameterEntity('scale', np.array([2.0, 3.0]), None))
matern52 = ScaledKernel(
2, matern52, ParameterEntity('sigma2', np.array([4.0]), None))
assert np.all(
matern52.cross_cov(point_1, point_2) == np.array(
[[3.0737065834936015]]))
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
inputs_2 = np.array([[1.5, 9.0], [-3.0, 8.0]])
assert np.all(
matern52.cross_cov(inputs_1, inputs_2) == np.array([[
0.87752659905500319, 0.14684671522649542
], [1.0880320585678382, 0.084041575076539962]]))
inputs_1 = np.array([[2.0, 4.0]])
inputs_2 = np.array([[1.5, 9.0], [-3.0, 8.0]])
assert np.all(
matern52.cross_cov(inputs_1, inputs_2) == np.array(
[[0.87752659905500319, 0.14684671522649542]]))
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
inputs_2 = np.array([[1.5, 9.0]])
npt.assert_almost_equal(
matern52.cross_cov(inputs_1, inputs_2),
np.array([[0.87752659905500319], [1.0880320585678382]]))
class TestMatern52(unittest.TestCase):
def setUp(self):
self.dimension = 2
self.length_scale = ParameterEntity('scale', np.array([1.0, 2.0]),
None)
self.sigma2 = ParameterEntity('sigma2', np.array([3]), None)
self.matern52 = Matern52(self.dimension, self.length_scale)
self.matern52 = ScaledKernel(self.dimension, self.matern52,
self.sigma2)
self.inputs = np.array([[1, 0], [0, 1]])
self.prior = UniformPrior(2, [1, 1], [100, 100])
self.prior_2 = UniformPrior(1, [1], [100])
self.matern52_ = Matern52(
2,
ParameterEntity(LENGTH_SCALE_NAME, np.array([2.0, 3.0]),
self.prior))
self.matern52_ = ScaledKernel(
self.dimension, self.matern52_,
ParameterEntity('sigma2', np.array([4.0]), self.prior_2))
def test_hypers(self):
assert {
'scale': self.length_scale,
'sigma2': self.sigma2
} == self.matern52.hypers
def test_set_parameters(self):
length = ParameterEntity('scale_', np.array([1, 2]), None)
sigma2 = ParameterEntity('sigma2', np.array([3]), None)
self.matern52.set_parameters([length], sigma2=sigma2)
assert self.matern52.hypers == {'scale_': length, 'sigma2': sigma2}
def test_cov(self):
expect(self.matern52).cross_cov.once().and_return(0)
assert self.matern52.cov(self.inputs) == 0
def test_cross_cov(self):
r2 = np.array([[0.0, 1.25], [1.25, 0.0]])
r = np.sqrt(r2)
left_term = ((1.0 + np.sqrt(5) * r + (5.0 / 3.0) * r2) *
np.exp(-np.sqrt(5) * r) * np.array([3]))[0, 1]
comparisons = left_term == self.matern52.cross_cov(
self.inputs, self.inputs)[0, 1]
assert np.all(comparisons)
point_1 = np.array([[2.0, 4.0]])
point_2 = np.array([[3.0, 5.0]])
matern52 = Matern52(
2, ParameterEntity('scale', np.array([2.0, 3.0]), None))
matern52 = ScaledKernel(
2, matern52, ParameterEntity('sigma2', np.array([4.0]), None))
assert np.all(
matern52.cross_cov(point_1, point_2) == np.array(
[[3.0737065834936015]]))
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
inputs_2 = np.array([[1.5, 9.0], [-3.0, 8.0]])
assert np.all(
matern52.cross_cov(inputs_1, inputs_2) == np.array([[
0.87752659905500319, 0.14684671522649542
], [1.0880320585678382, 0.084041575076539962]]))
inputs_1 = np.array([[2.0, 4.0]])
inputs_2 = np.array([[1.5, 9.0], [-3.0, 8.0]])
assert np.all(
matern52.cross_cov(inputs_1, inputs_2) == np.array(
[[0.87752659905500319, 0.14684671522649542]]))
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
inputs_2 = np.array([[1.5, 9.0]])
npt.assert_almost_equal(
matern52.cross_cov(inputs_1, inputs_2),
np.array([[0.87752659905500319], [1.0880320585678382]]))
def test_gradient_respect_parameters(self):
expect(GradientLSMatern52).gradient_respect_parameters_ls.once(
).and_return({'a': 0})
expect(self.matern52).cov.once().and_return(1.0)
assert self.matern52.gradient_respect_parameters(self.inputs) == {
'a': 0,
'sigma2': 1.0 / 3
}
def test_gradient_respect_parameters_finite_differences(self):
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
dh = 0.00000001
finite_diff = FiniteDifferences.forward_difference(
lambda params: ScaledKernel.evaluate_cov_defined_by_params(
params, inputs_1, 2, *([MATERN52_NAME], )),
np.array([2.0, 3.0, 4.0]), np.array([dh]))
gradient = ScaledKernel.evaluate_grad_defined_by_params_respect_params(
np.array([2.0, 3.0, 4.0]), inputs_1, 2, *([MATERN52_NAME], ))
for i in range(3):
npt.assert_almost_equal(finite_diff[i], gradient[i])
def test_grad_respect_point(self):
expect(GradientLSMatern52).grad_respect_point.once().and_return(0)
assert 0 == self.matern52.grad_respect_point(self.inputs, self.inputs)
def test_grad_respect_point_finite_differences(self):
dh = 0.000000000001
inputs_1 = np.array([[2.0, 4.0], [3.0, 5.0]])
point = np.array([[42.0, 35.0]])
finite_diff = FiniteDifferences.forward_difference(
lambda point: self.matern52_.cross_cov(point.reshape([1, 2]),
inputs_1),
np.array([42.0, 35.0]), np.array([dh]))
gradient = self.matern52_.grad_respect_point(point, inputs_1)
for i in range(2):
npt.assert_almost_equal(finite_diff[i],
gradient[:, i:i + 1].transpose())
def test_gradient_respect_parameters_ls(self):
expect(GradientLSMatern52).gradient_respect_distance.once().and_return(
4)
expect(Distances).gradient_distance_length_scale_respect_ls.once(
).and_return({
0: 3,
1: 2
})
assert GradientLSMatern52.gradient_respect_parameters_ls(
self.inputs, self.length_scale) == {
'scale': {
0: 12,
1: 8
}
}
def test_gradient_respect_distance(self):
expect(GradientLSMatern52).gradient_respect_distance_cross.once(
).and_return(0)
assert GradientLSMatern52.gradient_respect_distance(
self.length_scale, self.inputs) == 0
def test_gradient_respect_distance_cross(self):
expect(Distances).dist_square_length_scale.once().and_return(
np.array([0.0]))
assert GradientLSMatern52.gradient_respect_distance_cross(
self.length_scale, self.inputs, self.inputs) == np.array([0.0])
def test_grad_respect_point_2(self):
expect(GradientLSMatern52).gradient_respect_distance_cross.once(
).and_return(np.array([[1, 0], [0, 1]]))
expect(Distances).gradient_distance_length_scale_respect_point.once(
).and_return(1.0)
comparisons = GradientLSMatern52.grad_respect_point(self.length_scale,
self.inputs, self.inputs) == \
np.array([[1, 0], [0, 1]])
assert np.all(comparisons)
def test_grad_respect_point_matern(self):
expect(GradientLSMatern52).grad_respect_point.once().and_return(0.0)
assert self.matern52.grad_respect_point(self.inputs,
self.inputs) == 0.0
def test_name_parameters_as_list(self):
assert self.matern52.name_parameters_as_list == \
[('scale', [(0, None), (1, None)]), ('sigma2', None)]
def test_define_kernel_from_array(self):
kernel = Matern52.define_kernel_from_array(2, np.array([1, 3]))
assert np.all(kernel.length_scale.value == np.array([1, 3]))
def test_evaluate_cov_defined_by_params(self):
result = Matern52.evaluate_cov_defined_by_params(
np.array([1, 3, 5]), np.array([[4, 5]]), 2)
kernel = Matern52.define_kernel_from_array(2, np.array([1, 3, 5]))
assert result == kernel.cov(np.array([[4, 5]]))
def test_evaluate_grad_defined_by_params_respect_params(self):
result = Matern52.evaluate_grad_defined_by_params_respect_params(
np.array([1, 3]), np.array([[4, 5]]), 2)
kernel = Matern52.define_kernel_from_array(2, np.array([1, 3]))
grad_kernel = kernel.gradient_respect_parameters(np.array([[4, 5]]))
assert result == {
0: grad_kernel['length_scale'][0],
1: grad_kernel['length_scale'][1]
}
def test_hypers_as_list(self):
assert self.matern52_.hypers_as_list == [
self.matern52_.kernel.length_scale, self.matern52_.sigma2
]
def test_hypers_values_as_array(self):
assert np.all(
self.matern52_.hypers_values_as_array == np.array([2.0, 3.0, 4.0]))
def test_sample_parameters(self):
parameters = self.matern52_.hypers_as_list
samples = []
np.random.seed(1)
for parameter in parameters:
samples.append(parameter.sample_from_prior(2))
assert np.all(
self.matern52_.sample_parameters(2, random_seed=1) == np.array([[
samples[0][0, 0], samples[0][0, 1], samples[1][0]
], [samples[0][1, 0], samples[0][1, 1], samples[1][1]]]))
np.random.seed(1)
matern52 = Matern52(
2,
ParameterEntity(LENGTH_SCALE_NAME, np.array([2.0, 3.0]),
self.prior))
samples = []
parameters1 = matern52.hypers_as_list
for parameter in parameters1:
samples.append(parameter.sample_from_prior(2))
assert np.all(
matern52.sample_parameters(2, random_seed=1) == samples[0])
def test_get_bounds_parameters(self):
assert self.matern52_.get_bounds_parameters() == 3 * [
(SMALLEST_NUMBER, LARGEST_NUMBER)
]
def test_update_value_parameters(self):
self.matern52_.update_value_parameters(np.array([1, 5, 10]))
assert self.matern52_.sigma2.value == np.array([10])
parameters = self.matern52_.hypers
assert np.all(parameters[LENGTH_SCALE_NAME].value == np.array([1, 5]))
def test_define_default_kernel(self):
kern1 = Matern52.define_default_kernel(1)
assert kern1.name == MATERN52_NAME
assert kern1.dimension == 1
assert kern1.dimension_parameters == 1
assert kern1.length_scale.value == np.array([1])
assert kern1.length_scale.prior.max == [LARGEST_NUMBER]
assert kern1.length_scale.prior.min == [SMALLEST_POSITIVE_NUMBER]
kern2 = Matern52.define_default_kernel(1, default_values=np.array([5]))
assert kern2.name == MATERN52_NAME
assert kern2.dimension == 1
assert kern2.dimension_parameters == 1
assert kern2.length_scale.value == np.array([5])
assert kern2.length_scale.prior.max == [LARGEST_NUMBER]
assert kern2.length_scale.prior.min == [SMALLEST_POSITIVE_NUMBER]
kern3 = Matern52.define_default_kernel(1, bounds=[[5, 6]])
assert kern3.name == MATERN52_NAME
assert kern3.dimension == 1
assert kern3.dimension_parameters == 1
assert kern3.length_scale.value == np.array([1])
assert kern3.length_scale.prior.max == [20.0]
assert kern3.length_scale.prior.min == [SMALLEST_POSITIVE_NUMBER]
def test_compare_kernels(self):
kernel = Matern52.define_kernel_from_array(1, np.ones(1))
kernel_ = copy.deepcopy(kernel)
kernel_.name = 'a'
assert Matern52.compare_kernels(kernel, kernel_) is False
kernel_ = copy.deepcopy(kernel)
kernel_.dimension = 2
assert Matern52.compare_kernels(kernel, kernel_) is False
kernel_ = copy.deepcopy(kernel)
kernel_.dimension_parameters = 5
assert Matern52.compare_kernels(kernel, kernel_) is False
kernel_ = copy.deepcopy(kernel)
kernel_.length_scale.value = np.array([-1])
assert Matern52.compare_kernels(kernel, kernel_) is False
def test_define_prior_parameters(self):
data = {
'points': np.array([[1]]),
'evaluations': np.array([1]),
'var_noise': None,
}
dimension = 1
result = Matern52.define_prior_parameters(data, dimension)
assert result == {
LENGTH_SCALE_NAME: [0.0],
}
data2 = {
'points': np.array([[1], [2]]),
'evaluations': np.array([1, 2]),
'var_noise': None,
}
dimension2 = 1
result2 = Matern52.define_prior_parameters(data2, dimension2)
assert result2 == {
LENGTH_SCALE_NAME: [1.5432098765432098],
}
def test_evaluate_grad_respect_point(self):
result = Matern52.evaluate_grad_respect_point(np.array([5.0]),
np.array([[1]]),
np.array([[4], [5]]), 1)
kernel = Matern52.define_kernel_from_array(1, np.array([5.0]))
assert np.all(result == kernel.grad_respect_point(
np.array([[1]]), np.array([[4], [5]])))
def test_evaluate_hessian_respect_point(self):
point = np.array([[4.5, 7.5]])
inputs = np.array([[5.0, 6.0], [8.0, 9.0]])
params = np.array([1.0, 5.0])
result = Matern52.evaluate_hessian_respect_point(
params, point, inputs, 2)
dh = 0.00001
finite_diff = FiniteDifferences.second_order_central(
lambda x: Matern52.evaluate_cross_cov_defined_by_params(
params, x.reshape((1, len(x))), inputs, 2), point[0, :],
np.array([dh]))
for i in xrange(2):
for j in xrange(2):
print i, j
npt.assert_almost_equal(
finite_diff[i, j],
np.array([[result[0, i, j], result[1, i, j]]]),
decimal=5)
def test_hessian_distance_length_scale_respect_point(self):
params = np.array([1.0, 5.0])
point = np.array([[4.5, 7.5]])
inputs = np.array([[5.0, 6.0], [8.0, 9.0]])
result = Distances.gradient_distance_length_scale_respect_point(
params, point, inputs, second=True)
result = result['second']
dh = 0.00001
finite_diff = FiniteDifferences.second_order_central(
lambda x: np.sqrt(
Distances.dist_square_length_scale(
params, x.reshape((1, len(x))), inputs)), point[0, :],
np.array([dh]))
for i in xrange(2):
for j in xrange(2):
print i, j
npt.assert_almost_equal(
finite_diff[i, j],
np.array([[result[0, i, j], result[1, i, j]]]),
decimal=5)
def get_kernel_default(kernel_name,
dimension,
bounds=None,
default_values=None,
parameters_priors=None,
**kernel_parameters):
"""
Returns a default kernel object associated to the kernel_name
:param kernel_name: [str]
:param dimension: [int]. It's the number of tasks for the task kernel.
:param bounds: [[float, float]], lower bound and upper bound for each entry. This parameter
is to compute priors in a smart way.
:param default_values: np.array(k), default values for the parameters of the kernel
:param parameters_priors: {
SIGMA2_NAME: float,
LENGTH_SCALE_NAME: [float],
LOWER_TRIANG_NAME: [float],
}
:param kernel_parameters: additional kernel parameters,
- SAME_CORRELATION: (boolean) True or False. Parameter used only for task kernel.
:return: kernel object
"""
if kernel_name[0] == SCALED_KERNEL:
if kernel_name[1] == MATERN52_NAME:
return ScaledKernel.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors,
*([MATERN52_NAME], ))
if kernel_name[0] == MATERN52_NAME:
return Matern52.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors)
if kernel_name[0] == TASKS_KERNEL_NAME:
return TasksKernel.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors,
**kernel_parameters)
if kernel_name[0] == PRODUCT_KERNELS_SEPARABLE:
values = []
cont = 0
bounds_ = []
cont_b = 0
for name, dim in zip(kernel_name[1:], dimension[1:]):
n_params = get_number_parameters_kernel([name], [dim],
**kernel_parameters)
if default_values is not None:
value_kernel = default_values[cont:cont + n_params]
else:
value_kernel = None
if bounds is not None:
if name == MATERN52_NAME:
bounds_.append(bounds[cont_b:cont_b + dim])
cont_b += dim
if name == TASKS_KERNEL_NAME:
bounds_.append(bounds[cont_b:cont_b + 1])
cont_b += 1
cont += n_params
values.append(value_kernel)
if len(bounds_) > 0:
bounds = bounds_
return ProductKernels.define_default_kernel(dimension[1:], bounds,
values, parameters_priors,
kernel_name[1:],
**kernel_parameters)
def define_prior_parameters_using_data(data,
type_kernel,
dimensions,
sigma2=None,
**kernel_parameters):
"""
Defines value of the parameters of the prior distributions of the kernel's parameters.
:param data: {'points': np.array(nxm), 'evaluations': np.array(n),
'var_noise': np.array(n) or None}
:param type_kernel: [str]
:param dimensions: [int], It has only the n_tasks for the task_kernels, and for the
PRODUCT_KERNELS_SEPARABLE contains the dimensions of every kernel in the product, and
the total dimension of the product_kernels_separable too in the first entry.
:param: sigma2: float
:param kernel_parameters: additional kernel parameters,
- SAME_CORRELATION: (boolean) True or False. Parameter used only for task kernel.
:return: {
SIGMA2_NAME: float,
LENGTH_SCALE_NAME: [float],
LOWER_TRIANG_NAME: [float],
}
"""
# We assume that there is at most one task kernel, and mattern52 kernel in the product.
parameters_priors = {
SIGMA2_NAME: None,
LENGTH_SCALE_NAME: None,
LOWER_TRIANG_NAME: None,
}
index = 1
if TASKS_KERNEL_NAME in type_kernel:
same_correlation = kernel_parameters.get(SAME_CORRELATION, False)
index = type_kernel.index(TASKS_KERNEL_NAME)
index_tasks = 0
for i in xrange(1, index):
index_tasks += dimensions[i]
n_tasks = dimensions[index]
if n_tasks > 1:
tasks_index = data['points'][:, index_tasks]
task_data = data.copy()
task_data['points'] = tasks_index.reshape((len(tasks_index), 1))
else:
task_data = None
task_parameters = TasksKernel.define_prior_parameters(
task_data,
n_tasks,
same_correlation=same_correlation,
var_evaluations=sigma2)
parameters_priors[LOWER_TRIANG_NAME] = task_parameters[
LOWER_TRIANG_NAME]
if MATERN52_NAME in type_kernel:
m = data['points'].shape[1]
indexes = [i for i in range(m) if i != m - index + 1]
points_matern = data['points'][:, indexes]
matern_data = data.copy()
matern_data['points'] = points_matern
matern52_parameters = Matern52.define_prior_parameters(
matern_data, len(indexes))
parameters_priors[LENGTH_SCALE_NAME] = matern52_parameters[
LENGTH_SCALE_NAME]
if SCALED_KERNEL in type_kernel:
parameters = \
ScaledKernel.define_prior_parameters(data, len(indexes), var_evaluations=sigma2)
parameters_priors[SIGMA2_NAME] = parameters[SIGMA2_NAME]
return parameters_priors