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BADGE](https://img.shields.io/codacy/b18ed7d95b0a4707a0ff7b88b30d3def.svg?style=flat)](https://www.codacy.com/public/44gatti/shashi)\n[![CODECLIMATE](http://img.shields.io/codeclimate/github/rootslab/shashi.svg?style=flat)](https://codeclimate.com/github/rootslab/shashi)\n[![CODECLIMATE-TEST-COVERAGE](https://img.shields.io/codeclimate/coverage/github/rootslab/shashi.svg?style=flat)](https://codeclimate.com/github/rootslab/shashi)\n[![LICENSE](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://github.com/rootslab/shashi#mit-license)\n\n[![TRAVIS CI BUILD](http://img.shields.io/travis/rootslab/shashi.svg?style=flat)](http://travis-ci.org/rootslab/shashi)\n[![BUILD STATUS](http://img.shields.io/david/rootslab/shashi.svg?style=flat)](https://david-dm.org/rootslab/shashi)\n[![DEVDEPENDENCY STATUS](http://img.shields.io/david/dev/rootslab/shashi.svg?style=flat)](https://david-dm.org/rootslab/shashi#info=devDependencies)\n[![NPM DOWNLOADS](http://img.shields.io/npm/dm/shashi.svg?style=flat)](http://npm-stat.com/charts.html?package=shashi)\n\n[![NPM GRAPH1](https://nodei.co/npm-dl/shashi.png)](https://nodei.co/npm/shashi/)\n\n[![NPM GRAPH2](https://nodei.co/npm/shashi.png?downloads=true&downloadRank=true&stars=true)](https://nodei.co/npm/shashi/)\n\n[![status](https://sourcegraph.com/api/repos/github.com/rootslab/shashi/.badges/status.png)](https://sourcegraph.com/github.com/rootslab/shashi)\n[![views](https://sourcegraph.com/api/repos/github.com/rootslab/shashi/.counters/views.png)](https://sourcegraph.com/github.com/rootslab/shashi)\n[![views 24h](https://sourcegraph.com/api/repos/github.com/rootslab/shashi/.counters/views-24h.png)](https://sourcegraph.com/github.com/rootslab/shashi)\n\n> __Shashi__, a simple module to generate, using __pseudo-randomness__, a __universal family/set of hash functions__, which produce __integer values__ within the selected range (a __prime__ number).\n\n###A random bit of theory\n\n> A family __H__ of hash functions is __universal__ if, for any two items in the universe, the __probability of collision__ is as small as possible.\n\n> Briefly, assumed that for every input key k ∈ K and for every hash function h ∈ H:\n\n> - h(k) map to { 0, 1, .., m−1 }\n\n> then, __H is universal__ when, for any two items x != y:\n\n> - Prob( h(x) == h(y) ) = __1/m__\n\n> In other words, chosen __h__ uniformly at random, from a __universal set H__ of hash functions, if h is\n> used to hash __n__ arbitrary keys into __m__ slots, then, for a given key __k__, we expect that the total\n> number of __collisions__ with k is < __n/m__.\n\n> Universal hash functions could also be used to compute __perfect hashing__, for a determined set of keys.\n\n> __Choosing properly a prime p and a constant c__:\n> - p > c*|set of keys| (c >= ~2.09)\n\n> We expect to find a perfect hash mapping for values, in a __finite time__ (with __high probability__), interpreting lists of resulting hashed values as edges to insert in a __bipartite__ or __tripartite__ (hyper) __graph G__ = {V,E}:\n> - |E| = n (keys)\n> - |V| = p\n\n> we set:\n> - |V| = c * |E|\n\n> then we choose a prime >= 2*n:\n> - |V| = p = c * |E| = c * n > n * 2\n\n> Algo example; we choose |H| = 2, then H = { h0, h1 }:\n>\n> - while ( true ) :\n>  - pick-up at random 2 hash functions from H\n>\n>  - for every key k ∈ K, add resulting edge and vertices to the 2-graph:\n>    - edge = (v0, v1), with v0 = h0(k), v1 = h1(k)\n>\n>  - now testing the 2-graph acyclicity (it could be performed in linear time).\n>  - if 2-graph is acyclic:\n>    - break the loop, well done!\n>      - from now on, you can build a perfect hashing function for your set of keys,\n>        in a deterministic way.\n>    - otherwise let's gamble!!\n>      - continue loop, picking-up 2 others random hash functions\n\n> See also:\n> - __[Universal Hashing](http://en.wikipedia.org/wiki/Universal_hashing)__\n> - __[Perfect Hash Function](http://en.wikipedia.org/wiki/Perfect_hash_function)__\n> - __[Brando](https://github.com/rootslab/brando)__\n> - __[Nprime](https://github.com/rootslab/nprime)__\n\n###Install\n\n```bash\n$ npm install shashi [-g]\n```\n> __require__:\n\n```javascript\nvar Shashi = require( 'shashi' );\n```\n###Run Tests\n\n> __to run all test files, install devDependecies:__\n\n```bash\n $ cd shashi/\n # install or update devDependecies\n $ npm install\n # run tests\n $ npm test\n```\n> __to execute a single test file simply do__:\n\n```bash\n $ node test/file-name.js\n```\n###Run Benchmarks\n\n> run miscellaneous benchmarks for Shashi:\n\n```bash\n $ cd shashi/\n $ npm run bench\n # or to run a single file\n $ node bench/file-name-bench.js\n```\n----------------------------------------------------------------------------------------------\n\n###Method\n\n> Arguments within [ ] are optional.\n\n#####Generate seed sequence using Math.random.\n\n> get a method to use a family of hash functions.\n\n```javascript\n/*\n * A trivial example, Shashi( 2, 16, 257 ) generates:\n * - 2 hash functions, h0 and h1 (indexes 0, 1)\n * - every hash fn, expects/accepts at most 16 items to encode\n * - the range of items should be: [0,255], then using 1 byte\n *   per item in the seed sequence.\n *\n * NOTE:\n * - if the p number is not a prime, the next greater prime number will be calculated.\n * - if the p number is out of range (> 2^53), you'll get an Error.\n */\nShashi( Number hash_fn, Number items_to_hash, Number prime_for_seed_range ) : Array\n```\n\n> It returns an Array composed by:\n> - a method used to retrieve and execute a particular hash function, using an index (0-k).\n> - the underlying seed __[Sequence](https://github.com/rootslab/brando)__, used for generating the hashed value (an integer).\n\n```javascript\n/*\n * When you only need to regenerate random data for seeding your hash functions,\n * you can simply refresh the seed sequence using Sequence#fill or Sequence#parse.\n *\n * See also https://github.com/rootslab/brando.\n */\nArray : [ Function uhash, Sequence seed ]\n\n// universal set of hash functions\nuhash : function ( Number fn_index, Buffer input_data [, Number bytes_per_item ] )\n```\n\n#####Generate seed sequence using a random sample.\n\n> When a data Buffer is used to fill the seed sequence, the method automatically switches to the callback mode.\n\n```javascript\nShashi( Number h, Number i, Number p [, Buffer src [, Function cback ] ] ) : undefined\n```\n> the callback gets 3 arguments:\n\n```javascript\n/*\n * NOTE: an error was returned when:\n * - the number supplied for the seed sequence range is not a prime.\n * - the sample data are not enough to fill the random seed Sequence.\n */\ncback : function ( Error e, Function uhash, Sequence seed ) { .. }\n```\n> See also __[examples](example/)__.\n\n### MIT License\n\n> Copyright (c) 2015 &lt; Guglielmo Ferri : 44gatti@gmail.com &gt;\n\n> Permission is hereby granted, free of charge, to any person obtaining\n> a copy of this software and associated documentation files (the\n> 'Software'), to deal in the Software without restriction, including\n> without limitation the rights to use, copy, modify, merge, publish,\n> distribute, sublicense, and/or sell copies of the Software, and to\n> permit persons to whom the Software is furnished to do so, subject to\n> the following conditions:\n\n> __The above copyright notice and this permission notice shall be\n> included in all copies or substantial portions of the Software.__\n\n> THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,\n> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n> MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\n> CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n> TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\n> SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 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