feat: add minCost solution in Python and Go #3603

[Speccy-Rom]

Summary by Sourcery

Add Python and Go solutions for the Minimum Cost Path with Alternating Directions II problem and update documentation to include them.

New Features:

• Introduce Python implementation using a priority queue to handle alternating move and wait steps
• Introduce Go implementation using a rolling DP array with a helper min function

Documentation:

• Update Chinese and English READMEs to include the new Python and Go solutions

[sourcery-ai]

Reviewer's Guide

Adds end-to-end Python and Go implementations of the minCost algorithm: the Python version uses a priority queue with time-parity states to alternate move and wait steps, while the Go version leverages a rolling 1D DP array to accumulate minimum path costs.

Class diagram for the new Python Solution class (minCost)

classDiagram
    class Solution {
        +minCost(m: int, n: int, waitCost: List[List[int]]) int
    }

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File-Level Changes

Change	Details	Files
Implement Python minCost solution with alternating move/wait logic	Introduced a heap-based Dijkstra over (i,j,time) states Applied time parity to distinguish move versus wait steps Tracked visited states with minimum costs to prune Pushed initial state and handled early termination upon reaching target	solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/README.md solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/README_EN.md solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/Solution.py
Implement Go minCost solution using 1D dynamic programming	Initialized a rolling dp array for the first row Updated dp per row by taking min from above or left plus cell and enter costs Added a min64 helper and final cost adjustment subtracting the last cell's cost	solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/README.md solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/README_EN.md solution/3600-3699/3603.Minimum Cost Path with Alternating Directions II/Solution.go

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[sourcery-ai]

Hey @Speccy-Rom - I've reviewed your changes - here's some feedback:

• The Python solution is missing imports (e.g. heapq and typing.List) needed to make it runnable as a stand-alone snippet.
• The Go DP version subtracts the final cell cost as a special case and doesn’t model the alternating wait/move steps the same way as the Python Dijkstra approach—consider aligning the algorithms or adding a time dimension to your DP.
• Unify naming conventions and high-level structure between the Python and Go implementations (e.g. consistent function signatures and variable names) to improve cross-language maintainability.

Prompt for AI Agents

Please address the comments from this code review:
## Overall Comments
- The Python solution is missing imports (e.g. heapq and typing.List) needed to make it runnable as a stand-alone snippet.
- The Go DP version subtracts the final cell cost as a special case and doesn’t model the alternating wait/move steps the same way as the Python Dijkstra approach—consider aligning the algorithms or adding a time dimension to your DP.
- Unify naming conventions and high-level structure between the Python and Go implementations (e.g. consistent function signatures and variable names) to improve cross-language maintainability.

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[sourcery-ai]

suggestion (code-quality): Replace dict() with {} (dict-literal)

Suggested change

	visited = dict()
	visited = {}

Explanation

The most concise and Pythonic way to create a dictionary is to use the {}
notation.

This fits in with the way we create dictionaries with items, saving a bit of
mental energy that might be taken up with thinking about two different ways of
creating dicts.

x = {"first": "thing"}

Doing things this way has the added advantage of being a nice little performance
improvement.

Here are the timings before and after the change:

$ python3 -m timeit "x = dict()"
5000000 loops, best of 5: 69.8 nsec per loop

$ python3 -m timeit "x = {}"
20000000 loops, best of 5: 29.4 nsec per loop

Similar reasoning and performance results hold for replacing list() with [].