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https://github.com/morpheus65535/bazarr.git
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eb296e13c1
* Use Hamming textdistance library Used Hamming textdistance to sort by closest match. * Global search UI improvements Increased dropdown height to show more results initially (and which can also be scrolled into view). Scrollbars will appear automatically as needed. Remove dropdown when Search box is cleared. * Added textdistance 4.6.2 library
127 lines
3.1 KiB
Python
127 lines
3.1 KiB
Python
from __future__ import annotations
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# built-in
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from itertools import takewhile
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from typing import Sequence
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# app
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from .base import Base as _Base, BaseSimilarity as _BaseSimilarity
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from .types import SimFunc
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__all__ = [
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'Prefix', 'Postfix', 'Length', 'Identity', 'Matrix',
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'prefix', 'postfix', 'length', 'identity', 'matrix',
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]
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class Prefix(_BaseSimilarity):
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"""prefix similarity
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"""
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def __init__(self, qval: int = 1, sim_test: SimFunc = None) -> None:
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self.qval = qval
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self.sim_test = sim_test or self._ident
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def __call__(self, *sequences: Sequence) -> Sequence:
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if not sequences:
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return ''
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sequences = self._get_sequences(*sequences)
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def test(seq):
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return self.sim_test(*seq)
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result = [c[0] for c in takewhile(test, zip(*sequences))]
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s = sequences[0]
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if isinstance(s, str):
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return ''.join(result)
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if isinstance(s, bytes):
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return b''.join(result)
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return result
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def similarity(self, *sequences: Sequence) -> int:
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return len(self(*sequences))
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class Postfix(Prefix):
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"""postfix similarity
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"""
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def __call__(self, *sequences: Sequence) -> Sequence:
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s = sequences[0]
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sequences = [list(reversed(s)) for s in sequences]
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result = reversed(super().__call__(*sequences))
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if isinstance(s, str):
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return ''.join(result)
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if isinstance(s, bytes):
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return b''.join(result)
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return list(result)
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class Length(_Base):
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"""Length distance
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"""
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def __call__(self, *sequences: Sequence) -> int:
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lengths = list(map(len, sequences))
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return max(lengths) - min(lengths)
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class Identity(_BaseSimilarity):
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"""Identity similarity
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"""
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def maximum(self, *sequences: Sequence) -> int:
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return 1
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def __call__(self, *sequences: Sequence) -> int:
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return int(self._ident(*sequences))
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class Matrix(_BaseSimilarity):
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"""Matrix similarity
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"""
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def __init__(
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self,
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mat=None,
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mismatch_cost: int = 0,
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match_cost: int = 1,
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symmetric: bool = True,
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external: bool = True,
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) -> None:
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self.mat = mat
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self.mismatch_cost = mismatch_cost
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self.match_cost = match_cost
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self.symmetric = symmetric
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def maximum(self, *sequences: Sequence) -> int:
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return self.match_cost
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def __call__(self, *sequences: Sequence) -> int:
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if not self.mat:
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if self._ident(*sequences):
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return self.match_cost
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return self.mismatch_cost
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# search in matrix
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if sequences in self.mat:
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return self.mat[sequences]
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# search in symmetric matrix
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if self.symmetric:
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sequences = tuple(reversed(sequences))
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if sequences in self.mat:
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return self.mat[sequences]
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# if identity then return match_cost
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if self._ident(*sequences):
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return self.match_cost
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# not found
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return self.mismatch_cost
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prefix = Prefix()
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postfix = Postfix()
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length = Length()
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identity = Identity()
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matrix = Matrix()
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