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Yudong Jin 2778a6f9c7 Translate all code to English (#1836)
* 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
2025-12-31 07:44:52 +08:00

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Python

"""
File: unbounded_knapsack.py
Created Time: 2023-07-10
Author: krahets (krahets@163.com)
"""
def unbounded_knapsack_dp(wgt: list[int], val: list[int], cap: int) -> int:
"""Unbounded knapsack: Dynamic programming"""
n = len(wgt)
# Initialize dp table
dp = [[0] * (cap + 1) for _ in range(n + 1)]
# State transition
for i in range(1, n + 1):
for c in range(1, cap + 1):
if wgt[i - 1] > c:
# If exceeds knapsack capacity, don't select item i
dp[i][c] = dp[i - 1][c]
else:
# The larger value between not selecting and selecting item i
dp[i][c] = max(dp[i - 1][c], dp[i][c - wgt[i - 1]] + val[i - 1])
return dp[n][cap]
def unbounded_knapsack_dp_comp(wgt: list[int], val: list[int], cap: int) -> int:
"""Unbounded knapsack: Space-optimized dynamic programming"""
n = len(wgt)
# Initialize dp table
dp = [0] * (cap + 1)
# State transition
for i in range(1, n + 1):
# Traverse in forward order
for c in range(1, cap + 1):
if wgt[i - 1] > c:
# If exceeds knapsack capacity, don't select item i
dp[c] = dp[c]
else:
# The larger value between not selecting and selecting item i
dp[c] = max(dp[c], dp[c - wgt[i - 1]] + val[i - 1])
return dp[cap]
"""Driver Code"""
if __name__ == "__main__":
wgt = [1, 2, 3]
val = [5, 11, 15]
cap = 4
# Dynamic programming
res = unbounded_knapsack_dp(wgt, val, cap)
print(f"The maximum item value not exceeding knapsack capacity is {res}")
# Space-optimized dynamic programming
res = unbounded_knapsack_dp_comp(wgt, val, cap)
print(f"The maximum item value not exceeding knapsack capacity is {res}")