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update the link of pics
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lacrimosaprinz committed Sep 19, 2023
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`cosine_similarity()` 是余弦相似度,它衡量了两个向量之间夹角的余弦值,通过它们在多维空间中的接近程度来表示它们的相似性,其中 1 表示完全相似,-1 表示完全不相似。余弦相似度的计算是通过将两个向量的内积除以它们的 l2 范数的乘积来实现的。

![cosine_similarity](https://github.com/matrixorigin/artwork/blob/main/docs/reference/vector/cosine_similarity.png?raw=true)
![cosine_similarity](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/docs/reference/vector/cosine_similarity.png?raw=true)

## **函数语法**

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`INNER PRODUCT` 函数用于计算两个向量之间的内积/点积,它是两个向量的对应元素相乘然后相加的结果。

![inner_product](https://github.com/matrixorigin/artwork/blob/main/docs/reference/vector/inner_product.png?raw=true)
![inner_product](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/docs/reference/vector/inner_product.png?raw=true)

## **函数语法**

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`l1_norm` 函数用于计算 `l1`/曼哈顿/TaxiCab 范数。`l1` 范数通过对向量元素的绝对值求和得到。

![l1_normy](https://github.com/matrixorigin/artwork/blob/main/docs/reference/vector/l1_norm.png?raw=true)
![l1_normy](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/docs/reference/vector/l1_norm.png?raw=true)

你可以使用 `l1` 范数来计算 `l1` 距离。

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`l2_norm` 函数用于计算 `l2`/欧几里得范数。`l2` 范数通过对向量元素的平方和进行平方根运算得到。

![l2_normy](https://github.com/matrixorigin/artwork/blob/main/docs/reference/vector/l2_norm.png?raw=true)
![l2_normy](https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/artwork/docs/reference/vector/l2_norm.png?raw=true)

## **函数语法**

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