我有一个python（更具体 - pypy兼容）代码。我必须在其中使用直方图，如果与Hive项目中的this java直方图实现不完全相同，则应该非常相似。 Numpy有一个直方图implementation。有谁知道这两个是否在使用中是相同的还是可以通过选择适当的参数值来制作？我可以通过阅读代码找到这个，但是如果有人已经知道这个，请查看此处。
答案很长，TLDR以粗体显示
您可以编译并运行两者，而不是尝试理论工艺并猜测代码的作用。如有疑问，请运行它。通过一些工作，您可以获得链接的NumericHistogram.java，无需maven编译，只需使用CLI javac调用（只需删除对hive包和相关方法的引用）。
我只是在阵列[0,1,...,98,99]上测试了两个。
编辑：收到反馈（通过电子邮件）以包含Java代码。这里是（删除了文档字符串和一些注释，并没有包括所有公共方法）：
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
import java.util.ArrayList;
import java.util.List;
import java.util.Collections;
import java.util.Random;
public class NumericHistogram {
static class Coord implements Comparable {
double x;
double y;
public int compareTo(Object other) {
return Double.compare(x, ((Coord) other).x);
}
};
// Class variables
private int nbins;
private int nusedbins;
private ArrayList<Coord> bins;
private Random prng;
public NumericHistogram() {
nbins = 0;
nusedbins = 0;
bins = null;
// init the RNG for breaking ties in histogram merging. A fixed seed is specified here
// to aid testing, but can be eliminated to use a time-based seed (which would
// make the algorithm non-deterministic).
prng = new Random(31183);
}
public void reset() {
bins = null;
nbins = nusedbins = 0;
}
public int getUsedBins() {
return nusedbins;
}
public boolean isReady() {
return nbins != 0;
}
public Coord getBin(int b) {
return bins.get(b);
}
public void allocate(int num_bins) {
nbins = num_bins;
bins = new ArrayList<Coord>();
nusedbins = 0;
}
public void add(double v) {
int bin = 0;
for(int l=0, r=nusedbins; l < r; ) {
bin = (l+r)/2;
if (bins.get(bin).x > v) {
r = bin;
} else {
if (bins.get(bin).x < v) {
l = ++bin;
} else {
break; // break loop on equal comparator
}
}
}
if (bin < nusedbins && bins.get(bin).x == v) {
bins.get(bin).y++;
} else {
Coord newBin = new Coord();
newBin.x = v;
newBin.y = 1;
bins.add(bin, newBin);
// Trim the bins down to the correct number of bins.
if (++nusedbins > nbins) {
trim();
}
}
}
private void trim() {
while(nusedbins > nbins) {
// Find the closest pair of bins in terms of x coordinates. Break ties randomly.
double smallestdiff = bins.get(1).x - bins.get(0).x;
int smallestdiffloc = 0, smallestdiffcount = 1;
for(int i = 1; i < nusedbins-1; i++) {
double diff = bins.get(i+1).x - bins.get(i).x;
if(diff < smallestdiff) {
smallestdiff = diff;
smallestdiffloc = i;
smallestdiffcount = 1;
} else {
if(diff == smallestdiff && prng.nextDouble() <= (1.0/++smallestdiffcount) ) {
smallestdiffloc = i;
}
}
}
double d = bins.get(smallestdiffloc).y + bins.get(smallestdiffloc+1).y;
Coord smallestdiffbin = bins.get(smallestdiffloc);
smallestdiffbin.x *= smallestdiffbin.y / d;
smallestdiffbin.x += bins.get(smallestdiffloc+1).x / d * bins.get(smallestdiffloc+1).y;
smallestdiffbin.y = d;
// Shift the remaining bins left one position
bins.remove(smallestdiffloc+1);
nusedbins--;
}
}
public int getNumBins() {
return bins == null ? 0 : bins.size();
}
}
我将这个主要插入NumericHistogram类（见上文）：
public static void main(String[] args) {
NumericHistogram hist = new NumericHistogram();
hist.allocate(10);
for (int i = 0; i < 100; i++) {
hist.add(i);
}
ArrayList<Coord> bins = new ArrayList<Coord>();
for (int i = 0; i < 10; i++) {
bins.add(hist.getBin(i));
}
int index = 0;
for (Coord bin : bins) {
System.out.println(index + "th bin x: " + bin.x);
System.out.println(index + "th bin y: " + bin.y);
index++;
}
}
这个输出：
Matthews-MacBook-Pro:stackoverflow matt$ java NumericHistogram
0th bin x: 2.0
0th bin y: 5.0
1th bin x: 9.5
1th bin y: 10.0
2th bin x: 21.5
2th bin y: 14.0
3th bin x: 33.0
3th bin y: 9.0
4th bin x: 44.0
4th bin y: 13.0
5th bin x: 55.0
5th bin y: 9.0
6th bin x: 64.5
6th bin y: 10.0
7th bin x: 75.5
7th bin y: 12.0
8th bin x: 88.0
8th bin y: 13.0
9th bin x: 97.0
9th bin y: 5.0
numpy版本有点不同。这是脚本：
import numpy as np
hist = np.histogram(np.arange(100)) # 10 is the default for num bins
print(hist)
这个输出：
(array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10]), array([ 0. , 9.9, 19.8, 29.7, 39.6, 49.5, 59.4, 69.3, 79.2, 89.1, 99. ]))
为了与java版本进行比较，我们真的需要压缩它们，你可以这样做：
y_values, x_values = hist
i = 0
for x_val, y_val in zip(x_values, y_values):
print(str(i) + "th bin x: " + str(x_val))
print(str(i) + "th bin y: " + str(y_val))
i += 1
它输出：
0th bin x: 0.0
0th bin y: 10
1th bin x: 9.9
1th bin y: 10
2th bin x: 19.8
2th bin y: 10
3th bin x: 29.700000000000003
3th bin y: 10
4th bin x: 39.6
4th bin y: 10
5th bin x: 49.5
5th bin y: 10
6th bin x: 59.400000000000006
6th bin y: 10
7th bin x: 69.3
7th bin y: 10
8th bin x: 79.2
8th bin y: 10
9th bin x: 89.10000000000001
9th bin y: 10
它们是等价的吗？不，他们不等同。他们使用不同的分区方案为bin分配值。 Java版本使用舍入规则将项目放在“最接近”的bin中，而numpy正在进行范围分组（即0-9.9,9.9-19.8，...，89.1-100）。此外，它们生成的默认箱也不一样，这种方式有意义，因为方案是如此不同。
我可以让它们匹配吗？是的，但如果你想要一般的实现，你必须弄清楚如何在Python领域中生成与Java完全相同的随机数。我尝试复制实现，我让它工作，但我无法得到随机数是一样的。即使您使用numpy而不是将逻辑复制到Python，您仍然必须能够生成Java的随机数，以便在一般情况下使它们匹配。这个项目似乎很相关，但它只是Java 6 RNG，而不是Java的后续版本：https://github.com/MostAwesomeDude/java-random。有可能使这个巨大的答案更长，这里是复制Java实现的Python代码：
""" Just a mindless copy for easy verification. Not good style or performant. """
import random
class Coord:
def __init__(self, x, y):
self.x = float(x)
self.y = float(y)
def __repr__(self): # debug
return "Coord(" + str(self.x) + ", " + str(self.y) + ")"
def __str__(self): # debug
return "Coord(" + str(self.x) + ", " + str(self.y) + ")"
class Random:
""" This class needs fixin. You'll have to do some work here to make it match your version of Java. """
def __init__(self, seed):
random.seed(seed)
def nextDouble(self):
return random.uniform(0, 1)
class NumericHistogram:
def __init__(self):
self.nbins = 0
self.nusedbins = 0
self.bins = None
self.prng = Random(31183) # This should behave the same as Java's RNG for your Java version.
def allocate(self, num_bins):
self.nbins = num_bins
self.bins = []
self.nusedbins = 0
def add(self, v):
bin = 0
l = 0
r = self.nusedbins
while(l < r):
bin = (l+r)//2
if self.bins[bin].x > v:
r = bin
else:
if self.bins[bin].x < v:
l = bin + 1; bin += 1
else:
break
if bin < self.nusedbins and self.bins[bin].x == v:
self.bins[bin].y += 1
else:
newBin = Coord(x=v, y=1)
if bin == len(self.bins):
self.bins.append(newBin)
else:
self.bins[bin] == newBin
self.nusedbins += 1
if (self.nusedbins > self.nbins):
self.trim()
def trim(self):
while self.nusedbins > self.nbins:
smallestdiff = self.bins[1].x - self.bins[0].x
smallestdiffloc = 0
smallestdiffcount = 1
for i in range(1, self.nusedbins-1):
diff = self.bins[i+1].x - self.bins[i].x
if diff < smallestdiff:
smallestdiff = diff
smallestdiffloc = i
smallestdiffcount = 1
else:
smallestdiffcount += 1
if diff == smallestdiff and self.prng.nextDouble() <= (1.0/smallestdiffcount):
smallestdiffloc = i
d = self.bins[smallestdiffloc].y + self.bins[smallestdiffloc+1].y
smallestdiffbin = self.bins[smallestdiffloc]
smallestdiffbin.x *= smallestdiffbin.y / d
smallestdiffbin.x += self.bins[smallestdiffloc+1].x / d * self.bins[smallestdiffloc+1].y
smallestdiffbin.y = d
self.bins.pop(smallestdiffloc+1)
self.nusedbins -= 1
而“主要”：
hist = NumericHistogram()
hist.allocate(10)
for i in range(100):
hist.add(i)
for ind, bin in enumerate(hist.bins):
print(str(ind) + "th bin x: " + str(bin.x))
print(str(ind) + "th bin y: " + str(bin.y))
这个输出：
0th bin x: 4.0
0th bin y: 9.0
1th bin x: 13.0
1th bin y: 9.0
2th bin x: 24.0
2th bin y: 13.0
3th bin x: 35.0
3th bin y: 9.0
4th bin x: 46.5
4th bin y: 14.0
5th bin x: 57.5
5th bin y: 8.0
6th bin x: 66.0
6th bin y: 9.0
7th bin x: 76.49999999999999
7th bin y: 12.0
8th bin x: 88.99999999999999
8th bin y: 13.0
9th bin x: 97.5
9th bin y: 4.0
所以它有点接近，但没有香蕉。区别在于RNG（据我所知）。我对Java Random或RNG一般不太了解：所以最好在这里发布另一个关于如何生成与Java [insert-your-java-version-here]完全相同的随机数的问题。
HTH可以帮助您开始正确的方向。