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from sklearn.gaussian_process.kernels import Kernel, Hyperparameter
from sklearn.gaussian_process.kernels import GenericKernelMixin
from sklearn.gaussian_process.kernels import StationaryKernelMixin
import numpy as np
from sklearn.base import clone
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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