使用JavaScript实现的Murmurhash3,以产生与Python的sklearn库中transform函数使用的Murmurhash3.cpp相同的结果

2024-06-26 13:31:09 发布

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(我很抱歉,我没有被允许添加许多网址来帮助我更好地解释我在这篇文章中的问题,因为我是StackOverflow的新手,我的StackOverflow帐户的权限非常低)。在

摘要

有谁能指导我如何修改murmurhash3.js(以下)使其产生与{}(下面)相同的哈希值吗?我提供了一个简单的python代码“simple\upython_包装器.py“根据我的需要为杂音hash3.cpp。简单的Python_包装器.py如果安装了sklearn,应该可以在您的计算机上运行。在

在我的一个机器学习项目中,我大量使用了Murmurhash3.cpp(如下所示),同时使用了sklearn(Python机器学习库)中的transformtransform在sklearn的实现/导入树中使用Murmurhash3.cpp。在

更多详情

散列%(2^18){即“散列模2^18”},基于hurrayHash3.cpp

"hello" gives 260679
"there" gives 45525

哈希%(2^18){即“哈希模数2^18”},基于hurrayHash3.js

^{pr2}$

杂音hash3.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;
}

在这里使用Javascript来测试

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>

简单python_包装器.py

这将使用sklearn的导入树中的hurrhash3.cpp。在

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]

杂音3.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。在

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 反过来又利用了这一点 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/murmurhash.pyx 是建立在这个基础上的 https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/src/MurmurHash3.cpp所以,杂音hash3.cpp非常重要。我需要这个精确的murnhash3.cpp的Javascript版本,这样Javascript代码和whurrenhash3.cpp将产生相同的结果。在

我需要这个,因为我想让我的一些机器学习工具在网上可用,哈希需要在客户端的web浏览器上完成。在

到目前为止,我发现了一些杂音hash3的Javascript实现。然而,杂音hash3.jshttps://github.com/whitequark/murmurhash3-js/blob/master/murmurhash3.js似乎最接近sklearn使用的hurrayhash3.cpp(就代码结构而言)。但我还是不能从他们两个身上得到相同的哈希值。在

有谁能告诉我如何修改murmurhash3.js(上面的)使其产生与{}(上面)相同的散列值吗?在


Tags: datalenk2k1h2thish1tail
2条回答

根据@christoroicles的建议,我修改了我的Javascript代码(我的HTML代码的头)为使用hashBytes,而不是{},如下所示。我还注意到,为了我的目的,我需要将hashBytes的返回值更改为其绝对值(所以我这样做了)。这些解决了我的问题,现在我从Python/C++代码和JavaScript代码中得到相同的哈希。在

在我的HTML文件中修改了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 . 在

再次感谢Christopher Oicles和所有在某些方面帮助我的人。在

我参加聚会迟到了,但我现在才碰到这个问题。

如果字符串不是由常规ASCII字符组成的,则您使用的实现将产生与引用实现不同的结果。这是因为uses ^{} 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,则除非您在其他平台上执行完全相同的操作来解码字符串,否则结果将出现分歧。在

我制作了a fork of MurmurHash3js,它使用字节而不是字符串作为输入。以下是您的使用方法:

npm install murmurhash3js-revisited

然后在js文件中可以执行以下操作:

^{pr2}$

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

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