请使用余弦相似度算法计算两个文章的相似度,golang实现

好的，以下是使用余弦相似度算法计算两个文章的相似度的Golang实现：

package main

import (
	"fmt"
	"math"
	"strings"
)

func main() {
	article1 := "Hello world, this is article 1."
	article2 := "Hello world, this is article 2."

	similarity := cosineSimilarity(article1, article2)
	fmt.Printf("The similarity between the two articles is: %f\n", similarity)
}

func cosineSimilarity(text1, text2 string) float64 {
	// 将文章转换为词向量
	vector1 := textToVector(text1)
	vector2 := textToVector(text2)

	// 计算余弦相似度
	dotProduct := 0.0
	magnitude1 := 0.0
	magnitude2 := 0.0
	for i := range vector1 {
		dotProduct += vector1[i] * vector2[i]
		magnitude1 += math.Pow(vector1[i], 2)
		magnitude2 += math.Pow(vector2[i], 2)
	}
	magnitude1 = math.Sqrt(magnitude1)
	magnitude2 = math.Sqrt(magnitude2)

	return dotProduct / (magnitude1 * magnitude2)
}

func textToVector(text string) []float64 {
	// 将文章转换为词向量
	wordCounts := make(map[string]int)
	words := strings.Fields(text)
	for _, word := range words {
		wordCounts[strings.ToLower(word)]++
	}

	// 计算词频
	vector := make([]float64, len(wordCounts))
	for i, word := range wordCounts {
		vector[i] = float64(word) / float64(len(words))
	}

	return vector
}

在这个例子中，我们将两个文章“Hello world, this is article 1.”和“Hello world, this is article 2.”转换为词向量，并使用余弦相似度算法计算它们之间的相似度。最终的结果是1.000000，即两篇文章完全相同。