NLP之路-一点小语言工具函数

最新推荐文章于 2023-03-13 15:55:40 发布

原创最新推荐文章于 2023-03-13 15:55:40 发布 · 830 阅读

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本文深入探讨了近邻搜索及其在多种应用领域的关键作用，特别是高维空间中的挑战与解决策略。重点分析了近似最短路径算法在实际问题中的应用，包括图像识别、数据压缩、模式识别、分类、机器学习、文档检索系统、统计与数据分析。此外，文章还对比了精确搜索与近似搜索算法的性能，指出近似搜索在大多数实际应用中提供了足够好的解决方案，并且通常具有更高的效率。

统计工具

#coding=utf-8
def lexical_diversity(my_text_data):
	word_count=len(my_text_data)
	vocal_size=len(set(my_text_data))
	diversity_score=word_count/vocal_size
	return diversity_score
	
my_text_data="The problem of nearest neighbor search is one of major importance in a variety of applications such as image recognition, data compression, pattern recognition and classi?cation, machine learning, document retrieval systems, statistics and data analysis. However, solving this problem in high dimensional spaces seems to be a very di?cult task and there is no algorithm that performs signi?cantly better than the standard brute-force search. This has lead to an increasing interest in a class of algorithms that perform approximate nearest neighbor searches, which have proven to be a good-enough approximation in most practical applications and in most cases, orders of magnitude faster that the algorithms performing the exact searches"
print len(my_text_data)
print len(set(my_text_data))
print lexical_diversity(my_text_data)