```python import nltk import urllib.request from collections import Counter import matplotlib.pyplot as plt import numpy as np

1. Download the corpus

url = "http://www.gutenberg.org/files/2600/2600-0.txt" response = urllib.request.urlopen(url) long_txt = response.read().decode('utf8')

import random

1. Tokenize the text

tokenizer = nltk.tokenize.RegexpTokenizer('\w+') tokens = tokenizer.tokenize(long_txt.lower()) tokens = tokens[940:]

1. Prepare arrays to hold the counts of total words and unique words

total_words = np.arange(1, len(tokens) + 1) unique_words = np.zeros(len(tokens))

1. Count unique words while progressing through the text

word_set = set() for i, token in enumerate(tokens):

   word_set.add(token)
   unique_words[i] = len(word_set)

1. Fit Heap's law: unique_words = K * total_words ^ beta

log_total_words = np.log(total_words) log_unique_words = np.log(unique_words) beta, logK = np.polyfit(log_total_words, log_unique_words, 1) K = np.exp(logK)

1. Print the estimated parameters

print('K:', K) print('beta:', beta)

1. Plot total words vs. unique words

plt.figure(figsize=(8, 6)) plt.plot(total_words, unique_words, label='Empirical Data') plt.plot(total_words, K * total_words ** beta, '--', label=f'Heaps\' Law Fit: K={K:.2f}, beta={beta:.2f}')

1. Tokenize the text

tokenizer = nltk.tokenize.RegexpTokenizer('\w+') tokens = tokenizer.tokenize(long_txt.lower()) tokens = tokens[940:] random.shuffle(tokens)

1. Prepare arrays to hold the counts of total words and unique words

total_words = np.arange(1, len(tokens) + 1) unique_words = np.zeros(len(tokens))

1. Count unique words while progressing through the text

word_set = set() for i, token in enumerate(tokens):

   word_set.add(token)
   unique_words[i] = len(word_set)

1. Fit Heap's law: unique_words = K * total_words ^ beta

log_total_words = np.log(total_words) log_unique_words = np.log(unique_words) beta, logK = np.polyfit(log_total_words, log_unique_words, 1) K = np.exp(logK)

1. Print the estimated parameters

print('K:', K) print('beta:', beta)

1. Plot total words vs. unique words

plt.plot(total_words, unique_words, label='Shuffled Empirical Data') plt.plot(total_words, K * total_words ** beta, '--', label=f'Heaps\' Law Fit for shuffled data: K={K:.2f}, beta={beta:.2f}')

plt.xlabel('Total Words') plt.ylabel('Unique Words') plt.legend() plt.grid(True) plt.title('Verification of Heaps\' Law on "War and Peace"') plt.savefig("war and peace.svg", bbox_inches='tight', format='svg')