(非常抱歉，由于我是StackOverflow的新手，而且我的StackOverflow帐户的权限非常低，因此我不允许添加许多URL来帮助我更好地解释我的问题，)。
摘要
谁能指导我如何修改murmurhash3.js(如下)，以使其产生与MurmurHash3.cpp(下)相同的哈希？我根据需要为MurmurHash3.cpp提供了一个简单的python代码“simple_python_wrapper.py”。如果已安装sklearn，则simple_python_wrapper.py应该可以在您的计算机上运行。
我在使用sklearn(Python机器学习库)中的Murmurhash3.cpp时大量使用了transform(如下所示):在我的一个机器学习项目中使用了from sklearn.feature_extraction._hashing import transform。 transform在sklearn的实现/导入树中深入使用Murmurhash3.cpp。
更多详细信息
哈希％(2 ^ 18){即“哈希模数2 ^ 18”}}基于MurmurHash3.cpp
"hello" gives 260679
"there" gives 45525
哈希％(2 ^ 18){即“哈希模数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;
}
这是我用来测试javascript的HTML代码+ 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)
输出量
[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利用这个from sklearn.utils.murmurhash cimport murmurhash3_bytes_s32反过来利用这个https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/murmurhash.pyx以此为基础https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/src/MurmurHash3.cpp。 因此，MurmurHash3.cpp非常重要。我需要此确切MurmurHash3.cpp的Javascript版本，以便Javascript代码和MurmurHash3.cpp会产生相同的结果。
我需要这样做是因为我想使我的一些机器学习工具可以在线使用，并且散列需要在客户端的Web浏览器上进行。
到目前为止，我已经找到了MurmurHash3的一些Javascript实现。但是，murmurhash3.js
https://github.com/whitequark/murmurhash3-js/blob/master/murmurhash3.js似乎与sklearn使用的MurmurHash3.cpp最接近(就代码结构而言)。但是我仍然没有从两个人那里得到相同的哈希值。谁能指导我如何修改
murmurhash3.js(上方)，使其产生与MurmurHash3.cpp(上方)相同的哈希值？ 最佳答案
根据@ChristopherOicles的建议，我将Javascript代码(HTML代码的 header )更改为使用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(因此，我这样做了)。这些解决了我的问题，现在我从Python / C++代码和Javascript代码中获得了相同的哈希值。
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
。
再次感谢
Christopher Oicles和所有尝试以某种方式帮助我的人。