Fingerprint

Package stopwords allows you to customize the list of stopwords Package stopwords implements the Levenshtein Distance algorithm to evaluate the diference between 2 strings Package stopwords implements Charikar's simhash algorithm to generate a 64-bit fingerprint of a given document. Package stopwords contains various algorithms of text comparison (Simhash, Levenshtein)

Package cuckoo provides a Cuckoo Filter, a Bloom filter replacement for approximated set-membership queries. While Bloom filters are well-known space-efficient data structures to serve queries like "if item x is in a set?", they do not support deletion. Their variances to enable deletion (like counting Bloom filters) usually require much more space. Cuckoo filters provide the flexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom filters, for applications that require low false positive rates (< 3%). For details about the algorithm and citations please use this article: "Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky (https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf) Note: This implementation uses a a static bucket size of 4 fingerprints and a fingerprint size of 1 byte based on my understanding of an optimal bucket/fingerprint/size ratio from the aforementioned paper.

Package cuckoo provides a Cuckoo Filter, a Bloom filter replacement for approximated set-membership queries. While Bloom filters are well-known space-efficient data structures to serve queries like "if item x is in a set?", they do not support deletion. Their variances to enable deletion (like counting Bloom filters) usually require much more space. Cuckoo filters provide the flexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom filters, for applications that require low false positive rates (< 3%). "Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky (https://www.cs.cmu.edu/~dga/papers/cuckoo-conext2014.pdf)

Package pkcs12 implements some of PKCS#12 (also known as P12 or PFX). It is intended for decoding DER-encoded P12/PFX files for use with the crypto/tls package, and for encoding P12/PFX files for use by legacy applications which do not support newer formats. Since PKCS#12 uses weak encryption primitives, it SHOULD NOT be used for new applications. Note that only DER-encoded PKCS#12 files are supported, even though PKCS#12 allows BER encoding. This is because encoding/asn1 only supports DER. This package is forked from golang.org/x/crypto/pkcs12, which is frozen. The implementation is distilled from https://tools.ietf.org/html/rfc7292 and referenced documents. By definition, the Fingerprint is a hash of the public key bytes, hence one can use the Fingerprint returned from reading a p12 file to match the key with the certificate, as they will have the same hash. This is important as when a p12 file is loaded, it may have multiple keys and certificates, which can be provided in any order. Before loading the proper cert with key to make a tls.Certificate, it is a good idea to do something like the following: