mirror of https://github.com/morpheus65535/bazarr
113 lines
2.8 KiB
Python
113 lines
2.8 KiB
Python
"""
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IMPORTANT: it's just draft
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"""
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# built-in
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from functools import reduce
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from typing import Any
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# app
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from .base import Base as _Base, BaseSimilarity as _BaseSimilarity
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try:
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# external
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import numpy
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except ImportError:
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numpy = None # type: ignore[assignment]
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class Chebyshev(_Base):
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def _numpy(self, s1, s2):
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s1, s2 = numpy.asarray(s1), numpy.asarray(s2)
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return max(abs(s1 - s2))
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def _pure(self, s1, s2):
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return max(abs(e1 - e2) for e1, e2 in zip(s1, s2))
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def __call__(self, s1, s2) -> Any:
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if numpy:
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return self._numpy(s1, s2)
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else:
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return self._pure(s1, s2)
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class Minkowski(_Base):
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def __init__(self, p: int = 1, weight: int = 1) -> None:
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if p < 1:
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raise ValueError('p must be at least 1')
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self.p = p
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self.weight = weight
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def _numpy(self, s1, s2):
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s1, s2 = numpy.asarray(s1), numpy.asarray(s2)
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result = (self.weight * abs(s1 - s2)) ** self.p
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return result.sum() ** (1.0 / self.p)
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def _pure(self, s1, s2):
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result = (self.weight * abs(e1 - e2) for e1, e2 in zip(s1, s2))
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result = sum(e ** self.p for e in result)
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return result ** (1.0 / self.p)
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def __call__(self, s1, s2) -> Any:
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if numpy:
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return self._numpy(s1, s2)
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else:
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return self._pure(s1, s2)
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class Manhattan(_Base):
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def __call__(self, s1, s2) -> Any:
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raise NotImplementedError
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class Euclidean(_Base):
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def __init__(self, squared: bool = False) -> None:
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self.squared = squared
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def _numpy(self, s1, s2):
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s1 = numpy.asarray(s1)
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s2 = numpy.asarray(s2)
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q = numpy.matrix(s1 - s2)
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result = (q * q.T).sum()
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if self.squared:
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return result
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return numpy.sqrt(result)
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def _pure(self, s1, s2) -> None:
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raise NotImplementedError
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def __call__(self, s1, s2) -> Any:
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if numpy:
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return self._numpy(s1, s2)
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else:
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return self._pure(s1, s2)
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class Mahalanobis(_Base):
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def __call__(self, s1, s2) -> Any:
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raise NotImplementedError
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class Correlation(_BaseSimilarity):
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def _numpy(self, *sequences):
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sequences = [numpy.asarray(s) for s in sequences]
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ssm = [s - s.mean() for s in sequences]
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result = reduce(numpy.dot, sequences)
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for sm in ssm:
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result /= numpy.sqrt(numpy.dot(sm, sm))
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return result
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def _pure(self, *sequences):
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raise NotImplementedError
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def __call__(self, *sequences):
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if numpy:
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return self._numpy(*sequences)
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else:
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return self._pure(*sequences)
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class Kulsinski(_BaseSimilarity):
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def __call__(self, s1, s2) -> Any:
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raise NotImplementedError
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