Python 用matplotlib拟合构造Zipf分布

我有一个段落列表，我想在这些段落的组合上运行zipf发行版

我的代码如下：

from itertools import *
from pylab import *
from collections import Counter
import matplotlib.pyplot as plt


paragraphs = " ".join(targeted_paragraphs)
for paragraph in paragraphs:
   frequency = Counter(paragraph.split())
counts = array(frequency.values())
tokens = frequency.keys()

ranks = arange(1, len(counts)+1)
indices = argsort(-counts)
frequencies = counts[indices]
loglog(ranks, frequencies, marker=".")
title("Zipf plot for Combined Article Paragraphs")
xlabel("Frequency Rank of Token")
ylabel("Absolute Frequency of Token")
grid(True)
for n in list(logspace(-0.5, log10(len(counts)-1), 20).astype(int)):
    dummy = text(ranks[n], frequencies[n], " " + tokens[indices[n]],
    verticalalignment="bottom",
    horizontalalignment="left")

---

目的我尝试在该图中绘制“拟合线”，并将其值分配给变量。然而，我不知道如何补充这一点。对于这两个问题的任何帮助，我们都将不胜感激。

我知道这个问题提出已经有一段时间了。然而，我在上遇到了这个问题的可能解决方案。
我想我会在这里张贴，以防其他人需要

我没有段落信息，所以这里有一个快速生成的

dict

，名为

frequency

，它的值是段落出现次数

然后我们得到它的值并转换为numpy数组。定义

zipf分布参数

，该参数必须大于1

最后显示样本的直方图以及概率密度函数

工作代码：

import random
import matplotlib.pyplot as plt
from scipy import special
import numpy as np

#Generate sample dict with random value to simulate paragraph data
frequency = {}
for i,j in enumerate(range(50)):
    frequency[i]=random.randint(1,50)

counts = frequency.values()
tokens = frequency.keys()


#Convert counts of values to numpy array
s = np.array(counts)

#define zipf distribution parameter. Has to be >1
a = 2. 

# Display the histogram of the samples,
#along with the probability density function
count, bins, ignored = plt.hist(s, 50, normed=True)
plt.title("Zipf plot for Combined Article Paragraphs")
x = np.arange(1., 50.)
plt.xlabel("Frequency Rank of Token")
y = x**(-a) / special.zetac(a)
plt.ylabel("Absolute Frequency of Token")
plt.plot(x, y/max(y), linewidth=2, color='r')
plt.show()

绘图