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import numpy as np
from sklearn.base import clone
from sklearn.gaussian_process.kernels import (
GenericKernelMixin,
Hyperparameter,
Kernel,
StationaryKernelMixin,
)
class MiniSeqKernel(GenericKernelMixin, StationaryKernelMixin, Kernel):
"""
A minimal (but valid) convolutional kernel for sequences of variable
length.
"""
def __init__(self, baseline_similarity=0.5, baseline_similarity_bounds=(1e-5, 1)):
self.baseline_similarity = baseline_similarity
self.baseline_similarity_bounds = baseline_similarity_bounds
@property
def hyperparameter_baseline_similarity(self):
return Hyperparameter(
"baseline_similarity", "numeric", self.baseline_similarity_bounds
)
def _f(self, s1, s2):
return sum(
[1.0 if c1 == c2 else self.baseline_similarity for c1 in s1 for c2 in s2]
)
def _g(self, s1, s2):
return sum([0.0 if c1 == c2 else 1.0 for c1 in s1 for c2 in s2])
def __call__(self, X, Y=None, eval_gradient=False):
if Y is None:
Y = X
if eval_gradient:
return (
np.array([[self._f(x, y) for y in Y] for x in X]),
np.array([[[self._g(x, y)] for y in Y] for x in X]),
)
else:
return np.array([[self._f(x, y) for y in Y] for x in X])
def diag(self, X):
return np.array([self._f(x, x) for x in X])
def clone_with_theta(self, theta):
cloned = clone(self)
cloned.theta = theta
return cloned
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