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sklearn/gaussian_process/tests/_mini_sequence_kernel.py
54 строки
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Adrin Jalali
MNT add isort to ruff's rules (#26649)
21 июн 2023, 18:50
Не верифицирован
21 июн 2023, 18:50
42173fd
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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