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Translate all code to English (#1836)

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* Review the EN heading format.

* Fix pythontutor headings.

* Fix pythontutor headings.

* bug fixes

* Fix headings in **/summary.md

* Revisit the CN-to-EN translation for Python code using Claude-4.5

* Revisit the CN-to-EN translation for Java code using Claude-4.5

* Revisit the CN-to-EN translation for Cpp code using Claude-4.5.

* Fix the dictionary.

* Fix cpp code translation for the multipart strings.

* Translate Go code to English.

* Update workflows to test EN code.

* Add EN translation for C.

* Add EN translation for CSharp.

* Add EN translation for Swift.

* Trigger the CI check.

* Revert.

* Update en/hash_map.md

* Add the EN version of Dart code.

* Add the EN version of Kotlin code.

* Add missing code files.

* Add the EN version of JavaScript code.

* Add the EN version of TypeScript code.

* Fix the workflows.

* Add the EN version of Ruby code.

* Add the EN version of Rust code.

* Update the CI check for the English version  code.

* Update Python CI check.

* Fix cmakelists for en/C code.

* Fix Ruby comments
This commit is contained in:
Yudong Jin
2025-12-31 07:44:52 +08:00
committed by GitHub
parent 45e1295241
commit 2778a6f9c7
1284 changed files with 71557 additions and 3275 deletions
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en/docs/chapter_dynamic_programming/edit_distance_problem.md
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@@ -1,4 +1,4 @@
# Edit distance problem
# Edit Distance Problem
Edit distance, also known as Levenshtein distance, refers to the minimum number of edits required to transform one string into another, commonly used in information retrieval and natural language processing to measure the similarity between two sequences.
@@ -20,7 +20,7 @@ From the perspective of the decision tree, the goal of this problem is to find t
![Representing edit distance problem based on decision tree model](edit_distance_problem.assets/edit_distance_decision_tree.png)
### Dynamic programming approach
### Dynamic Programming Approach
**Step 1: Think about the decisions in each round, define the state, and thus obtain the $dp$ table**
@@ -65,7 +65,7 @@ When both strings are empty, the number of edit steps is $0$, i.e., $dp[0, 0] =
Observing the state transition equation, the solution $dp[i, j]$ depends on solutions to the left, above, and upper-left, so the entire $dp$ table can be traversed in order through two nested loops.
### Code implementation
### Code Implementation
```src
[file]{edit_distance}-[class]{}-[func]{edit_distance_dp}
@@ -118,7 +118,7 @@ As shown in the figure below, the state transition process for the edit distance
=== "<15>"
![edit_distance_dp_step15](edit_distance_problem.assets/edit_distance_dp_step15.png)
### Space optimization
### Space Optimization
Since $dp[i, j]$ is transferred from the solutions above $dp[i-1, j]$, to the left $dp[i, j-1]$, and to the upper-left $dp[i-1, j-1]$, forward traversal will lose the upper-left solution $dp[i-1, j-1]$, and reverse traversal cannot build $dp[i, j-1]$ in advance, so neither traversal order is feasible.