translation: optimize the translation of hash_map.md #1358

magentaqin · 2024-05-10T13:11:19Z

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krahets · 2024-05-18T10:31:15Z

en/docs/chapter_hashing/hash_map.md


-As shown in the figure below, given $n$ students, each with two pieces of data: "name" and "student number". If we want to implement a query feature that returns the corresponding name when given a student number, we can use the hash table shown in the figure below.
+As shown in the diagram below, given $n$ students, each student has two data fields: "Name" and "Student ID". If we want to implement a query function that takes a student ID as input and returns the corresponding name, we can use the hash table depicted below.


diagram -> figure

We use "figure" and "table" throughout the book

krahets · 2024-05-18T10:33:32Z

en/docs/chapter_hashing/hash_map.md


-As shown in the figure below, given $n$ students, each with two pieces of data: "name" and "student number". If we want to implement a query feature that returns the corresponding name when given a student number, we can use the hash table shown in the figure below.
+As shown in the diagram below, given $n$ students, each student has two data fields: "Name" and "Student ID". If we want to implement a query function that takes a student ID as input and returns the corresponding name, we can use the hash table depicted below.


the hash table depicted below -> the hash table shown in the figure below

The keywords "the figure below" will be replaced with the Figure number during the build process, so don't modify them.

krahets · 2024-05-18T10:34:07Z

en/docs/chapter_hashing/hash_map.md

@@ -283,7 +283,7 @@ Common operations of a hash table include initialization, querying, adding key-v

    https://pythontutor.com/render.html#code=%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20%23%20%E5%88%9D%E5%A7%8B%E5%8C%96%E5%93%88%E5%B8%8C%E8%A1%A8%0A%20%20%20%20hmap%20%3D%20%7B%7D%0A%20%20%20%20%0A%20%20%20%20%23%20%E6%B7%BB%E5%8A%A0%E6%93%8D%E4%BD%9C%0A%20%20%20%20%23%20%E5%9C%A8%E5%93%88%E5%B8%8C%E8%A1%A8%E4%B8%AD%E6%B7%BB%E5%8A%A0%E9%94%AE%E5%80%BC%E5%AF%B9%20%28key,%20value%29%0A%20%20%20%20hmap%5B12836%5D%20%3D%20%22%E5%B0%8F%E5%93%88%22%0A%20%20%20%20hmap%5B15937%5D%20%3D%20%22%E5%B0%8F%E5%95%B0%22%0A%20%20%20%20hmap%5B16750%5D%20%3D%20%22%E5%B0%8F%E7%AE%97%22%0A%20%20%20%20hmap%5B13276%5D%20%3D%20%22%E5%B0%8F%E6%B3%95%22%0A%20%20%20%20hmap%5B10583%5D%20%3D%20%22%E5%B0%8F%E9%B8%AD%22%0A%20%20%20%20%0A%20%20%20%20%23%20%E6%9F%A5%E8%AF%A2%E6%93%8D%E4%BD%9C%0A%20%20%20%20%23%20%E5%90%91%E5%93%88%E5%B8%8C%E8%A1%A8%E4%B8%AD%E8%BE%93%E5%85%A5%E9%94%AE%20key%20%EF%BC%8C%E5%BE%97%E5%88%B0%E5%80%BC%20value%0A%20%20%20%20name%20%3D%20hmap%5B15937%5D%0A%20%20%20%20%0A%20%20%20%20%23%20%E5%88%A0%E9%99%A4%E6%93%8D%E4%BD%9C%0A%20%20%20%20%23%20%E5%9C%A8%E5%93%88%E5%B8%8C%E8%A1%A8%E4%B8%AD%E5%88%A0%E9%99%A4%E9%94%AE%E5%80%BC%E5%AF%B9%20%28key,%20value%29%0A%20%20%20%20hmap.pop%2810583%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false

-There are three common ways to traverse a hash table: traversing key-value pairs, keys, and values. Example code is as follows:
+There are three common ways to traverse a hash table: traversing key-value pairs, traversing keys, and traversing values. Here is an example code


Here is an example code -> Here is an example code:

done. missed the punctuation...

krahets · 2024-05-18T10:35:42Z

en/docs/chapter_hashing/hash_map.md


-So, how do we locate the appropriate bucket based on the `key`? This is achieved through a <u>hash function</u>. The role of the hash function is to map a larger input space to a smaller output space. In a hash table, the input space is all possible keys, and the output space is all buckets (array indices). In other words, input a `key`, **and we can use the hash function to determine the storage location of the corresponding key-value pair in the array**.



Remove this redundant blank line. Each line number should match in both the old and new versions.

done.Thanks for your reminding.

krahets · 2024-05-18T10:36:29Z

en/docs/chapter_hashing/hash_map.md

-Afterward, we can use `index` to access the corresponding bucket in the hash table and thereby retrieve the `value`.
+Afterward, we can use the `index` to access the corresponding bucket in the hash table and thereby retrieve the `value`.
+
+Let's assume that the array length is `capacity = 100`, and the hash algorithm is defined as `hash(key) = key`. Therefore, the hash function can be expressed as `key % 100`. The following diagram illustrates the working principle of the hash function using `key` as student ID and `value` as name.



Remove this redundant blank line.

Same as others. Please carefully check the formatting

Only one blank line exists before "Afterward..." and "Let's assume..." paragraphs. You could have a double check.

krahets

Thanks for the PR! Please address the comments.

krahets · 2024-05-20T18:32:31Z

en/docs/chapter_hashing/hash_map.md


 ![Example of hash collision](hash_map.assets/hash_collision.png)

-It is easy to understand that the larger the capacity $n$ of the hash table, the lower the probability of multiple keys being allocated to the same bucket, and the fewer the collisions. Therefore, **expanding the capacity of the hash table can reduce hash collisions**.



There is still one more redundant line.

I would suggest you check the formatting at https://github.com/krahets/hello-algo/pull/1358/files

Resolved. Thanks for your reminding.

feat: translate hash_map.md to English

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krahets changed the title ~~feat: translate hash_map.md to English~~ translation: optimize the translation of hash_map.md May 18, 2024

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Merge branch 'krahets:main' into main

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doc: remove redundant lines and polish words

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translation: optimize the translation of hash_map.md #1358

translation: optimize the translation of hash_map.md #1358

magentaqin commented May 10, 2024

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krahets May 20, 2024 •

edited

magentaqin May 22, 2024


		As shown in the figure below, given $n$ students, each with two pieces of data: "name" and "student number". If we want to implement a query feature that returns the corresponding name when given a student number, we can use the hash table shown in the figure below.
		As shown in the diagram below, given $n$ students, each student has two data fields: "Name" and "Student ID". If we want to implement a query function that takes a student ID as input and returns the corresponding name, we can use the hash table depicted below.


		So, how do we locate the appropriate bucket based on the `key`? This is achieved through a <u>hash function</u>. The role of the hash function is to map a larger input space to a smaller output space. In a hash table, the input space is all possible keys, and the output space is all buckets (array indices). In other words, input a `key`, and we can use the hash function to determine the storage location of the corresponding key-value pair in the array.


		![Example of hash collision](hash_map.assets/hash_collision.png)

		It is easy to understand that the larger the capacity $n$ of the hash table, the lower the probability of multiple keys being allocated to the same bucket, and the fewer the collisions. Therefore, expanding the capacity of the hash table can reduce hash collisions.

translation: optimize the translation of hash_map.md #1358

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translation: optimize the translation of hash_map.md #1358

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magentaqin commented May 10, 2024

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krahets May 20, 2024 • edited

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krahets May 20, 2024 •

edited