📄 libstrsim-8095d292a6ea3ca3.rmeta
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rust
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�$��`��`mem���Chars�,������63 This library implements string similarity metrics.�"#,-56=>?@AEWdijkmqy��u�w�-�s��o��n��m��l�	�k�*�j�_�i�^
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NK Calculates the number of positions in the two sequences where the elements���
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�&�'�'�(a��b������������1��NK Calculates the number of positions in the two strings where the characters���C@ differ. Returns an error if the strings have different lengths.���<� ```���<9 use strsim::{hamming, StrSimError::DifferentLengthArgs};�����52 assert_eq!(Ok(3), hamming("hamming", "hammers"));�����DA assert_eq!(Err(DifferentLengthArgs), hamming("hamming", "ham"));�<��)<������$�
�'��'������LI Calculates the Jaro similarity between two sequences. The returned value���=: is between 0.0 and 1.0 (higher value means more similar).�d��	��	��	�	��� �	!! 	���/,����/,����,����	,��H�/���/�H�0d��H�/���/�H�0d���0�0��
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#�	�%<�%""�2��%3�	'�	('(�)*+�H�H�H+�%�%L�&$�&&&l�&D�&&&��������	�q�,�\�	��&$L�&���H&&
+�	�	$�&��)$��'JG Calculates the Jaro similarity between two strings. The returned value���'=�-�(�<�(�)��( use strsim::jaro;��(���(MJ assert!((0.392 - jaro("Friedrich Nietzsche", "Jean-Paul Sartre")).abs() <���(         0.001);�<�)�)$�)�����/�)
,�'�)�'�)��*���)GD Like Jaro but gives a boost to sequences that have a common prefix.���*�/�/�/�	.�	/�0�1�2�	3.3/120	�/,�*�0,�*�0,�*�0,�*�0Ժ+�0d�+�0��+�0d�+�0��,
-�	�	�/�/�0�0�'�+�'�+�*�*tvz|������--<closure_kind>�4<closure_signature>�4<upvars>�44-4	�/�/�0�0�0�0�#��0,��.EB Like Jaro but gives a boost to strings that have a common prefix.��/�<�/�)�/ use strsim::jaro_winkler;��/���/JG assert!((0.866 - jaro_winkler("cheeseburger", "cheese fries")).abs() <���0�6<�0�)d�0�����/�0
5�'�0�'�0��3���1MJ Calculates the minimum number of insertions, deletions, and substitutions���130 required to change one sequence into the other.��2�<�2�)��2$! use strsim::generic_levenshtein;��2���2A> assert_eq!(3, generic_levenshtein(&[1,2,3], &[1,2,3,4,5,6]));�<�3�)��3�/�/��	7�	8�9�:�;�	<97<8:;	�/,�3�0,�3�0,�3�0,�3�0ԩ4�0d�4�0��4�0d�4�0��4
6�	�	�/�/�0�0�'�3�'�3�3�3tvz|������;-��9M�>��:1. required to change one string into the other.��:�<�:�)��: use strsim::levenshtein;��:���:41 assert_eq!(3, levenshtein("kitten", "sitting"));�<�;�)\�;������;
=�'�;�'�;��@6��<NK Calculates a normalized score of the Levenshtein algorithm between 0.0 and���<>; 1.0 (inclusive), where 1.0 means the strings are the same.��=�<�=�)��='$ use strsim::normalized_levenshtein;��=���=UR assert!((normalized_levenshtein("kitten", "sitting") - 0.57142).abs() < 0.00001);���>DA assert!((normalized_levenshtein("", "") - 1.0).abs() < 0.00001);���>B? assert!(normalized_levenshtein("", "second").abs() < 0.00001);���?A> assert!(normalized_levenshtein("first", "").abs() < 0.00001);���@PM assert!((normalized_levenshtein("string", "string") - 1.0).abs() < 0.00001);�<�@�)��@�����/�@
>�'�@�'�A��C.��BOL Like Levenshtein but allows for adjacent transpositions. Each substring can���B only be edited once.��C�<�C�)�C use strsim::osa_distance;��C���C-* assert_eq!(3, osa_distance("ab", "bca"));�<�C�)d�D������D
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@��Pj��Q�,�Q��Sx��QLI Like optimal string alignment, but substrings can be edited an unlimited���R74 number of times, and the triangle inequality holds.��R�<�R�)��R,) use strsim::generic_damerau_levenshtein;��R���RA> assert_eq!(2, generic_damerau_levenshtein(&[1,2], &[2,3,1]));�<�S�)��S���
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b��bb��Ŋ��Ȋ�&��'���e�fef�',��',��J�&����&�J\‹��&,ы�J�'���'�J\���',��
d�&�'s1��len1�$��s2���len2�$������T��d�:g�:g�;ggdg�&�'�<��f�#T��d�:h�:h�;hhdh�&�'�<�f�#��5���L�N�Û7�O���<���)���$! use strsim::damerau_levenshtein;�������41 assert_eq!(2, damerau_levenshtein("ab", "bca"));�<��)����������
i�'���'�����>���PM Calculates a normalized score of the Damerau–Levenshtein algorithm between��ҞFC 0.0 and 1.0 (inclusive), where 1.0 means the strings are the same.����<���)���/, use strsim::normalized_damerau_levenshtein;�՟��ٟfc assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.27272).abs() < 0.00001);����LI assert!((normalized_damerau_levenshtein("", "") - 1.0).abs() < 0.00001);����JG assert!(normalized_damerau_levenshtein("", "flower").abs() < 0.00001);��ءHE assert!(normalized_damerau_levenshtein("tree", "").abs() < 0.00001);����`] assert!((normalized_damerau_levenshtein("sunglasses", "sunglasses") - 1.0).abs() < 0.00001);�<���)��������/��
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k����K��'k8��k����Zip����'�C���'�C����C���	�C��ڸ(j��������	�q�,�\8����Skip����	�@��n��@9@AvR�q��q��8l�J�r앦�r�J����r8���r?�r0�r!�rk���-��B? Calculates a Sørensen-Dice similarity distance using bigrams.����OL See <https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient>.����<���)� use strsim::sorensen_dice;�������+( assert_eq!(1.0, sorensen_dice("", ""));��Ш,) assert_eq!(0.0, sorensen_dice("", "a"));����74 assert_eq!(0.0, sorensen_dice("french", "quebec"));����74 assert_eq!(1.0, sorensen_dice("ferris", "ferris"));����EB assert_eq!(0.8888888888888888, sorensen_dice("feris", "ferris"));�<���)lª�����/Ӫ
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