Revision 291ae6c7dbfcbded27c604f136982a5067d14b8e authored by thevincentadam on 20 January 2020, 12:17:20 UTC, committed by thevincentadam on 20 January 2020, 12:17:20 UTC
1 parent 5dc31b8
linears.py
``````import tensorflow as tf

from ..base import Parameter
from ..utilities import positive
from .base import Kernel

class Linear(Kernel):
"""
The linear kernel. Functions drawn from a GP with this kernel are linear, i.e. f(x) = cx.
The kernel equation is

k(x, y) = σ²xy

where σ² is the variance parameter.
"""

def __init__(self, variance=1.0, active_dims=None):
"""
:param variance: the (initial) value for the variance parameter(s),
to induce ARD behaviour this must be initialised as an array the same
length as the the number of active dimensions e.g. [1., 1., 1.]
:param active_dims: a slice or list specifying which columns of X are used
"""
super().__init__(active_dims)
self.variance = Parameter(variance, transform=positive())
self._validate_ard_active_dims(self.variance)

@property
def ard(self) -> bool:
"""
Whether ARD behaviour is active.
"""
return self.variance.shape.ndims > 0

def K(self, X, X2=None, presliced=False):
if not presliced:
X, X2 = self.slice(X, X2)

if X2 is None:
return tf.linalg.matmul(X * self.variance, X, transpose_b=True)

return tf.linalg.matmul(X * self.variance, X2, transpose_b=True)

def K_diag(self, X, presliced=False):
if not presliced:
X, _ = self.slice(X, None)
return tf.reduce_sum(tf.square(X) * self.variance, 1)

class Polynomial(Linear):
"""
The Polynomial kernel. Functions drawn from a GP with this kernel are
polynomials of degree `d`. The kernel equation is

k(x, y) = (σ²xy + γ)ᵈ

where:
σ² is the variance parameter,
γ is the offset parameter,
d is the degree parameter.
"""
def __init__(self, degree=3.0, variance=1.0, offset=1.0, active_dims=None):
"""
:param degree: the degree of the polynomial
:param variance: the (initial) value for the variance parameter(s),
to induce ARD behaviour this must be initialised as an array the same
length as the the number of active dimensions e.g. [1., 1., 1.]
:param offset: the offset of the polynomial
:param active_dims: a slice or list specifying which columns of X are used
"""
super().__init__(variance, active_dims)
self.degree = degree
self.offset = Parameter(offset, transform=positive())

def K(self, X, X2=None, presliced=False):
return (super().K(X, X2, presliced=presliced) + self.offset)**self.degree

def K_diag(self, X, presliced=False):
return (super().K_diag(X, presliced=presliced) + self.offset)**self.degree
`````` Computing file changes ...