Javascript Murmurhash3 的实现给出与 Murmurhash3.cpp 相同的结果，由 Python 的 sklearn 中可用的转换使用

Question

（非常抱歉，我不允许添加很多 URL 来帮助我更好地解释我在这个 post 中的问题，因为我是 Whosebug 的新手，而且我的 Whosebug 帐户权限非常低）。

摘要

任何人都可以指导我如何修改 murmurhash3.js（下图），以便它生成与 MurmurHash3.cpp（下图）相同的哈希值吗？我根据需要为 MurmurHash3.cpp 提供了一个简单的 python 代码 "simple_python_wrapper.py"。如果您安装了 sklearn，simple_python_wrapper.py 应该可以在您的计算机上运行。

我在使用 sklearn（一个 Python 机器学习库）中的 transform 时大量使用 Murmurhash3.cpp（如下所示）：from sklearn.feature_extraction._hashing import transform 在我的一个机器学习中项目。 transform 在 sklearn 的 implementation/import 树的深处使用 Murmurhash3.cpp。

更多详情

hash % (2^18) {即"hash modulus 2^18"}基于MurmurHash3.cpp

"hello" gives 260679
"there" gives 45525

hash % (2^18) {即 "hash modulus 2^18"} 基于 murmurhash3.js

"hello" gives -58999
"there" gives 65775

murmurhash3.js

/*
 *  The MurmurHash3 algorithm was created by Austin Appleby.  This JavaScript port was authored
 *  by whitequark (based on Java port by Yonik Seeley) and is placed into the public domain.
 *  The author hereby disclaims copyright to this source code.
 *
 *  This produces exactly the same hash values as the final C++ version of MurmurHash3 and
 *  is thus suitable for producing the same hash values across platforms.
 *
 *  There are two versions of this hash implementation. First interprets the string as a
 *  sequence of bytes, ignoring most significant byte of each codepoint. The second one
 *  interprets the string as a UTF-16 codepoint sequence, and appends each 16-bit codepoint
 *  to the hash independently. The latter mode was not written to be compatible with
 *  any other implementation, but it should offer better performance for JavaScript-only
 *  applications.
 *
 *  See http://github.com/whitequark/murmurhash3-js for future updates to this file.
 */

var MurmurHash3 = {
    mul32: function(m, n) {
        var nlo = n & 0xffff;
        var nhi = n - nlo;
        return ((nhi * m | 0) + (nlo * m | 0)) | 0;
    },

    hashBytes: function(data, len, seed) {
        var c1 = 0xcc9e2d51, c2 = 0x1b873593;

        var h1 = seed;
        var roundedEnd = len & ~0x3;

        for (var i = 0; i < roundedEnd; i += 4) {
            var k1 = (data.charCodeAt(i)     & 0xff)        |
                ((data.charCodeAt(i + 1) & 0xff) << 8)  |
                ((data.charCodeAt(i + 2) & 0xff) << 16) |
                ((data.charCodeAt(i + 3) & 0xff) << 24);

            k1 = this.mul32(k1, c1);
            k1 = ((k1 & 0x1ffff) << 15) | (k1 >>> 17);  // ROTL32(k1,15);
            k1 = this.mul32(k1, c2);

            h1 ^= k1;
            h1 = ((h1 & 0x7ffff) << 13) | (h1 >>> 19);  // ROTL32(h1,13);
            h1 = (h1 * 5 + 0xe6546b64) | 0;
        }

        k1 = 0;

        switch(len % 4) {
            case 3:
                k1 = (data.charCodeAt(roundedEnd + 2) & 0xff) << 16;
                // fallthrough
            case 2:
                k1 |= (data.charCodeAt(roundedEnd + 1) & 0xff) << 8;
                // fallthrough
            case 1:
                k1 |= (data.charCodeAt(roundedEnd) & 0xff);
                k1 = this.mul32(k1, c1);
                k1 = ((k1 & 0x1ffff) << 15) | (k1 >>> 17);  // ROTL32(k1,15);
                k1 = this.mul32(k1, c2);
                h1 ^= k1;
        }

        // finalization
        h1 ^= len;

        // fmix(h1);
        h1 ^= h1 >>> 16;
        h1  = this.mul32(h1, 0x85ebca6b);
        h1 ^= h1 >>> 13;
        h1  = this.mul32(h1, 0xc2b2ae35);
        h1 ^= h1 >>> 16;

        return h1;
    },

    hashString: function(data, len, seed) {
        var c1 = 0xcc9e2d51, c2 = 0x1b873593;

        var h1 = seed;
        var roundedEnd = len & ~0x1;

        for (var i = 0; i < roundedEnd; i += 2) {
            var k1 = data.charCodeAt(i) | (data.charCodeAt(i + 1) << 16);

            k1 = this.mul32(k1, c1);
            k1 = ((k1 & 0x1ffff) << 15) | (k1 >>> 17);  // ROTL32(k1,15);
            k1 = this.mul32(k1, c2);

            h1 ^= k1;
            h1 = ((h1 & 0x7ffff) << 13) | (h1 >>> 19);  // ROTL32(h1,13);
            h1 = (h1 * 5 + 0xe6546b64) | 0;
        }

        if((len % 2) == 1) {
            k1 = data.charCodeAt(roundedEnd);
            k1 = this.mul32(k1, c1);
            k1 = ((k1 & 0x1ffff) << 15) | (k1 >>> 17);  // ROTL32(k1,15);
            k1 = this.mul32(k1, c2);
            h1 ^= k1;
        }

        // finalization
        h1 ^= (len << 1);

        // fmix(h1);
        h1 ^= h1 >>> 16;
        h1  = this.mul32(h1, 0x85ebca6b);
        h1 ^= h1 >>> 13;
        h1  = this.mul32(h1, 0xc2b2ae35);
        h1 ^= h1 >>> 16;

        return h1;
    }
};

if(typeof module !== "undefined" && typeof module.exports !== "undefined") {
    module.exports = MurmurHash3;
}

这是 HTML 代码 + Javascript 我用来测试 javascript

https://jsbin.com/gicomikike/edit?html,js,output

<html>
<head>

<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.1.0/jquery.min.js"></script>
<script src="murmurhash3.js"></script>

<script>

function call_murmurhash3_32_gc () {
    var key = $('textarea#textarea1').val();
    var seed = 0;
    var hash = MurmurHash3.hashString (key, key.length, seed);
    $('div#div1').text(hash);
    }
</script>

</head>
<body>
Body

<form>

    <textarea rows="4" cols="50" id=textarea1></textarea>

    <br>
    <input type="button" value="Hash" onclick="call_murmurhash3_32_gc()"/>

</form>


<div id=div1>

</div>

</body>
</html>

simple_python_wrapper.py

这在 sklearn 的导入树中使用了 MurmurHash3.cpp。

from sklearn.feature_extraction._hashing import transform 
import numpy as np

def getHashIndex (words):

    raw_X = words
    n_features = 262144 # 2 ** 18
    dtype = np.float32 #np.float64

    #transform(raw_X, Py_ssize_t n_features, dtype)
    indices_a, indptr, values = transform (raw_X, n_features, dtype)

    return indices_a 


words = [[("hello", 1), ("there", 1)]] 

print getHashIndex (words)

输出

[260679  45525]

MurmurHash3.cpp

I copied this code is available here 
https://github.com/karanlyons/murmurHash3.js/blob/master/murmurHash3.js
//-----------------------------------------------------------------------------
// MurmurHash3 was written by Austin Appleby, and is placed in the public
// domain. The author hereby disclaims copyright to this source code.

// Note - The x86 and x64 versions do _not_ produce the same results, as the
// algorithms are optimized for their respective platforms. You can still
// compile and run any of them on any platform, but your performance with the
// non-native version will be less than optimal.

#include "MurmurHash3.h"

//-----------------------------------------------------------------------------
// Platform-specific functions and macros

// Microsoft Visual Studio

#if defined(_MSC_VER)

#define FORCE_INLINE    __forceinline

#include <stdlib.h>

#define ROTL32(x,y) _rotl(x,y)
#define ROTL64(x,y) _rotl64(x,y)

#define BIG_CONSTANT(x) (x)

// Other compilers

#else   // defined(_MSC_VER)

#if defined(GNUC) && ((GNUC > 4) || (GNUC == 4 && GNUC_MINOR >= 4))

/* gcc version >= 4.4 4.1 = RHEL 5, 4.4 = RHEL 6.
 * Don't inline for RHEL 5 gcc which is 4.1 */
#define FORCE_INLINE attribute((always_inline))

#else

#define FORCE_INLINE

#endif


inline uint32_t rotl32 ( uint32_t x, int8_t r )
{
  return (x << r) | (x >> (32 - r));
}

inline uint64_t rotl64 ( uint64_t x, int8_t r )
{
  return (x << r) | (x >> (64 - r));
}

#define ROTL32(x,y) rotl32(x,y)
#define ROTL64(x,y) rotl64(x,y)

#define BIG_CONSTANT(x) (x##LLU)

#endif // !defined(_MSC_VER)

//-----------------------------------------------------------------------------
// Block read - if your platform needs to do endian-swapping or can only
// handle aligned reads, do the conversion here

FORCE_INLINE uint32_t getblock ( const uint32_t * p, int i )
{
  return p[i];
}

FORCE_INLINE uint64_t getblock ( const uint64_t * p, int i )
{
  return p[i];
}

//-----------------------------------------------------------------------------
// Finalization mix - force all bits of a hash block to avalanche

FORCE_INLINE uint32_t fmix ( uint32_t h )
{
  h ^= h >> 16;
  h *= 0x85ebca6b;
  h ^= h >> 13;
  h *= 0xc2b2ae35;
  h ^= h >> 16;

  return h;
}

//----------

FORCE_INLINE uint64_t fmix ( uint64_t k )
{
  k ^= k >> 33;
  k *= BIG_CONSTANT(0xff51afd7ed558ccd);
  k ^= k >> 33;
  k *= BIG_CONSTANT(0xc4ceb9fe1a85ec53);
  k ^= k >> 33;

  return k;
}

//-----------------------------------------------------------------------------

void MurmurHash3_x86_32 ( const void * key, int len,
                          uint32_t seed, void * out )
{
  const uint8_t * data = (const uint8_t*)key;
  const int nblocks = len / 4;

  uint32_t h1 = seed;

  uint32_t c1 = 0xcc9e2d51;
  uint32_t c2 = 0x1b873593;

  //----------
  // body

  const uint32_t * blocks = (const uint32_t *)(data + nblocks*4);

  for(int i = -nblocks; i; i++)
  {
    uint32_t k1 = getblock(blocks,i);

    k1 *= c1;
    k1 = ROTL32(k1,15);
    k1 *= c2;

    h1 ^= k1;
    h1 = ROTL32(h1,13);
    h1 = h1*5+0xe6546b64;
  }

  //----------
  // tail

  const uint8_t * tail = (const uint8_t*)(data + nblocks*4);

  uint32_t k1 = 0;

  switch(len & 3)
  {
  case 3: k1 ^= tail[2] << 16;
  case 2: k1 ^= tail[1] << 8;
  case 1: k1 ^= tail[0];
          k1 *= c1; k1 = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
  };

  //----------
  // finalization

  h1 ^= len;

  h1 = fmix(h1);

  *(uint32_t*)out = h1;
}

//-----------------------------------------------------------------------------

void MurmurHash3_x86_128 ( const void * key, const int len,
                           uint32_t seed, void * out )
{
  const uint8_t * data = (const uint8_t*)key;
  const int nblocks = len / 16;

  uint32_t h1 = seed;
  uint32_t h2 = seed;
  uint32_t h3 = seed;
  uint32_t h4 = seed;

  uint32_t c1 = 0x239b961b;
  uint32_t c2 = 0xab0e9789;
  uint32_t c3 = 0x38b34ae5;
  uint32_t c4 = 0xa1e38b93;

  //----------
  // body

  const uint32_t * blocks = (const uint32_t *)(data + nblocks*16);

  for(int i = -nblocks; i; i++)
  {
    uint32_t k1 = getblock(blocks,i*4+0);
    uint32_t k2 = getblock(blocks,i*4+1);
    uint32_t k3 = getblock(blocks,i*4+2);
    uint32_t k4 = getblock(blocks,i*4+3);

    k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;

    h1 = ROTL32(h1,19); h1 += h2; h1 = h1*5+0x561ccd1b;

    k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;

    h2 = ROTL32(h2,17); h2 += h3; h2 = h2*5+0x0bcaa747;

    k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;

    h3 = ROTL32(h3,15); h3 += h4; h3 = h3*5+0x96cd1c35;

    k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;

    h4 = ROTL32(h4,13); h4 += h1; h4 = h4*5+0x32ac3b17;
  }

  //----------
  // tail

  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);

  uint32_t k1 = 0;
  uint32_t k2 = 0;
  uint32_t k3 = 0;
  uint32_t k4 = 0;

  switch(len & 15)
  {
  case 15: k4 ^= tail[14] << 16;
  case 14: k4 ^= tail[13] << 8;
  case 13: k4 ^= tail[12] << 0;
           k4 *= c4; k4  = ROTL32(k4,18); k4 *= c1; h4 ^= k4;

  case 12: k3 ^= tail[11] << 24;
  case 11: k3 ^= tail[10] << 16;
  case 10: k3 ^= tail[ 9] << 8;
  case  9: k3 ^= tail[ 8] << 0;
           k3 *= c3; k3  = ROTL32(k3,17); k3 *= c4; h3 ^= k3;

  case  8: k2 ^= tail[ 7] << 24;
  case  7: k2 ^= tail[ 6] << 16;
  case  6: k2 ^= tail[ 5] << 8;
  case  5: k2 ^= tail[ 4] << 0;
           k2 *= c2; k2  = ROTL32(k2,16); k2 *= c3; h2 ^= k2;

  case  4: k1 ^= tail[ 3] << 24;
  case  3: k1 ^= tail[ 2] << 16;
  case  2: k1 ^= tail[ 1] << 8;
  case  1: k1 ^= tail[ 0] << 0;
           k1 *= c1; k1  = ROTL32(k1,15); k1 *= c2; h1 ^= k1;
  };

  //----------
  // finalization

  h1 ^= len; h2 ^= len; h3 ^= len; h4 ^= len;

  h1 += h2; h1 += h3; h1 += h4;
  h2 += h1; h3 += h1; h4 += h1;

  h1 = fmix(h1);
  h2 = fmix(h2);
  h3 = fmix(h3);
  h4 = fmix(h4);

  h1 += h2; h1 += h3; h1 += h4;
  h2 += h1; h3 += h1; h4 += h1;

  ((uint32_t*)out)[0] = h1;
  ((uint32_t*)out)[1] = h2;
  ((uint32_t*)out)[2] = h3;
  ((uint32_t*)out)[3] = h4;
}

//-----------------------------------------------------------------------------

void MurmurHash3_x64_128 ( const void * key, const int len,
                           const uint32_t seed, void * out )
{
  const uint8_t * data = (const uint8_t*)key;
  const int nblocks = len / 16;

  uint64_t h1 = seed;
  uint64_t h2 = seed;

  uint64_t c1 = BIG_CONSTANT(0x87c37b91114253d5);
  uint64_t c2 = BIG_CONSTANT(0x4cf5ad432745937f);

  //----------
  // body

  const uint64_t * blocks = (const uint64_t *)(data);

  for(int i = 0; i < nblocks; i++)
  {
    uint64_t k1 = getblock(blocks,i*2+0);
    uint64_t k2 = getblock(blocks,i*2+1);

    k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;

    h1 = ROTL64(h1,27); h1 += h2; h1 = h1*5+0x52dce729;

    k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;

    h2 = ROTL64(h2,31); h2 += h1; h2 = h2*5+0x38495ab5;
  }

  //----------
  // tail

  const uint8_t * tail = (const uint8_t*)(data + nblocks*16);

  uint64_t k1 = 0;
  uint64_t k2 = 0;

  switch(len & 15)
  {
  case 15: k2 ^= uint64_t(tail[14]) << 48;
  case 14: k2 ^= uint64_t(tail[13]) << 40;
  case 13: k2 ^= uint64_t(tail[12]) << 32;
  case 12: k2 ^= uint64_t(tail[11]) << 24;
  case 11: k2 ^= uint64_t(tail[10]) << 16;
  case 10: k2 ^= uint64_t(tail[ 9]) << 8;
  case  9: k2 ^= uint64_t(tail[ 8]) << 0;
           k2 *= c2; k2  = ROTL64(k2,33); k2 *= c1; h2 ^= k2;

  case  8: k1 ^= uint64_t(tail[ 7]) << 56;
  case  7: k1 ^= uint64_t(tail[ 6]) << 48;
  case  6: k1 ^= uint64_t(tail[ 5]) << 40;
  case  5: k1 ^= uint64_t(tail[ 4]) << 32;
  case  4: k1 ^= uint64_t(tail[ 3]) << 24;
  case  3: k1 ^= uint64_t(tail[ 2]) << 16;
  case  2: k1 ^= uint64_t(tail[ 1]) << 8;
  case  1: k1 ^= uint64_t(tail[ 0]) << 0;
           k1 *= c1; k1  = ROTL64(k1,31); k1 *= c2; h1 ^= k1;
  };

  //----------
  // finalization

  h1 ^= len; h2 ^= len;

  h1 += h2;
  h2 += h1;

  h1 = fmix(h1);
  h2 = fmix(h2);

  h1 += h2;
  h2 += h1;

  ((uint64_t*)out)[0] = h1;
  ((uint64_t*)out)[1] = h2;
}

//-----------------------------------------------------------------------------

让我再解释一下。

from sklearn.feature_extraction._hashing import transform 使用此代码 https://github.com/scikit-learn/scikit-learn/blob/412996f09b6756752dfd3736c306d46fca8f1aa1/sklearn/feature_extraction/_hashing.pyx 它利用了这个 from sklearn.utils.murmurhash cimport murmurhash3_bytes_s32 反过来又利用了这个 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/murmurhash.pyx 这是建立在这个 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/src/MurmurHash3.cpp。 所以，MurmurHash3.cpp很重要。我需要这个 MurmurHash3.cpp 的 Javascript 版本，这样 Javascript 代码和 MurmurHash3.cpp 会产生相同的结果 .

我需要这个，因为我想让我的一些机器学习工具在线可用，并且哈希需要在客户端的网络浏览器上完成。

到目前为止，我已经找到了几个 Javascript MurmurHash3 的实现。但是，murmurhash3.js https://github.com/whitequark/murmurhash3-js/blob/master/murmurhash3.js 似乎（就代码结构而言）最接近 sklearn 使用的 MurmurHash3.cpp。但是我仍然没有从他们两个那里得到相同的哈希值。

任何人都可以指导我如何修改 murmurhash3.js（上文），以便它生成与 MurmurHash3.cpp（上文）相同的哈希值吗？

Answer 1

根据@ChristopherOicles 的建议，我更改了我的 Javascript 代码（我的 header 关闭了我的 HTML 代码）以使用 hashBytes 而不是 hashString 如下所示。我还注意到，出于我的目的，我需要将 hashBytes 的返回值更改为其绝对值（我这样做了）。这些解决了我的问题，现在我从 Python/C++ 代码和 Javascript 代码中得到了相同的散列值。

修改了我的 HTML 文件中的 Javascript 函数

<script>
function call_murmurhash3_32_gc () {
    var key = $('textarea#textarea1').val();
    var seed = 0;
    var hash = MurmurHash3.hashBytes (key, key.length, seed);
    $('div#div1').text(Math.abs (hash) % 262144);
    }
</script>

我的完整解决方案在这里https://jsbin.com/qilokot/edit?html,js,output .

再次感谢 Christopher Oicles 以及所有试图以某种方式帮助我的人。

Answer 2

来晚了，现在才遇到这个问题

如果字符串不是由常规 ASCII 字符组成，您使用的实现将产生与参考实现不同的结果。这是因为它 uses charCodeAt and a byte mask 从输入字符串中获取需要散列的字节。如果字符串包含任何其他字符，除非您在其他平台上执行完全相同的操作来解码字符串，否则结果会有所不同。

我制作了 a fork of MurmurHash3js，它使用字节而不是字符串作为输入。以下是您如何使用它：

npm install murmurhash3js-revisited

然后在你的js文件中你可以做：

import MurmurHash3 from 'murmurhash3js-revisited';

const str = "My hovercraft is full of eels.";
// get utf-8 bytes
const bytes = new TextEncoder().encode(str);

MurmurHash3.x86.hash32(bytes);
// output: 2953494853

MurmurHash3.x86.hash128(bytes);
// output: "e3a186aee169ba6c6a8bd9343c68fa9c"

MurmurHash3.x64.hash128(bytes);
// output: "03e5e14d358c16d1e5ae86df7ed5cfcb"

MurmurHash3.x86.hash32("any string");
// output: undefined
// (x86.hash128 and x64.hash128 also return undefined)

您可以阅读有关性能注意事项的信息，并尝试在 my library's documentation 中的不同 JavaScript 实现之间进行交互式比较。

Javascript Murmurhash3 的实现给出与 Murmurhash3.cpp 相同的结果，由 Python 的 sklearn 中可用的转换使用

Javascript implementation of Murmurhash3 to give the same result as Murmurhash3.cpp used by transform available in Python's sklearn

javascript

c++

python

murmurhash

scikit-learn