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Many bug fixes and improvements (#1270)

Browse Source 
* Add Ruby and Kotlin icons
Add the avatar of @curtishd

* Update README

* Synchronize zh-hant and zh versions.

* Translate the pythontutor blocks to traditional Chinese

* Fix en/mkdocs.yml

* Update the landing page of the en version.

* Fix the Dockerfile

* Refine the en landingpage

* Fix en landing page

* Reset the README.md
This commit is contained in:
Yudong Jin
2024-04-11 20:18:19 +08:00
committed by GitHub
parent 07977184ad
commit b2f0d4603d
192 changed files with 2382 additions and 1196 deletions
Download Patch File Download Diff File Expand all files Collapse all files
zh-hant/codes/kotlin/chapter_dynamic_programming/knapsack.kt
+21 -23
View File
@@ -6,13 +6,12 @@
package chapter_dynamic_programming
import java.util.*
import kotlin.math.max
/* 0-1 背包：暴力搜尋 */
fun knapsackDFS(
wgt: IntArray,
value: IntArray,
_val: IntArray,
i: Int,
c: Int
): Int {
@@ -22,19 +21,19 @@ fun knapsackDFS(
}
// 若超過背包容量，則只能選擇不放入背包
if (wgt[i - 1] > c) {
return knapsackDFS(wgt, value, i - 1, c)
return knapsackDFS(wgt, _val, i - 1, c)
}
// 計算不放入和放入物品 i 的最大價值
val no = knapsackDFS(wgt, value, i - 1, c)
val yes = knapsackDFS(wgt, value, i - 1, c - wgt[i - 1]) + value[i - 1]
val no = knapsackDFS(wgt, _val, i - 1, c)
val yes = knapsackDFS(wgt, _val, i - 1, c - wgt[i - 1]) + _val[i - 1]
// 返回兩種方案中價值更大的那一個
return max(no.toDouble(), yes.toDouble()).toInt()
return max(no, yes)
}
/* 0-1 背包：記憶化搜尋 */
fun knapsackDFSMem(
wgt: IntArray,
value: IntArray,
_val: IntArray,
mem: Array<IntArray>,
i: Int,
c: Int
@@ -49,20 +48,20 @@ fun knapsackDFSMem(
}
// 若超過背包容量，則只能選擇不放入背包
if (wgt[i - 1] > c) {
return knapsackDFSMem(wgt, value, mem, i - 1, c)
return knapsackDFSMem(wgt, _val, mem, i - 1, c)
}
// 計算不放入和放入物品 i 的最大價值
val no = knapsackDFSMem(wgt, value, mem, i - 1, c)
val yes = knapsackDFSMem(wgt, value, mem, i - 1, c - wgt[i - 1]) + value[i - 1]
val no = knapsackDFSMem(wgt, _val, mem, i - 1, c)
val yes = knapsackDFSMem(wgt, _val, mem, i - 1, c - wgt[i - 1]) + _val[i - 1]
// 記錄並返回兩種方案中價值更大的那一個
mem[i][c] = max(no.toDouble(), yes.toDouble()).toInt()
mem[i][c] = max(no, yes)
return mem[i][c]
}
/* 0-1 背包：動態規劃 */
fun knapsackDP(
wgt: IntArray,
value: IntArray,
_val: IntArray,
cap: Int
): Int {
val n = wgt.size
@@ -76,8 +75,7 @@ fun knapsackDP(
dp[i][c] = dp[i - 1][c]
} else {
// 不選和選物品 i 這兩種方案的較大值
dp[i][c] = max(dp[i - 1][c].toDouble(), (dp[i - 1][c - wgt[i - 1]] + value[i - 1]).toDouble())
.toInt()
dp[i][c] = max(dp[i - 1][c], dp[i - 1][c - wgt[i - 1]] + _val[i - 1])
}
}
}
@@ -87,7 +85,7 @@ fun knapsackDP(
/* 0-1 背包：空間最佳化後的動態規劃 */
fun knapsackDPComp(
wgt: IntArray,
value: IntArray,
_val: IntArray,
cap: Int
): Int {
val n = wgt.size
@@ -100,7 +98,7 @@ fun knapsackDPComp(
if (wgt[i - 1] <= c) {
// 不選和選物品 i 這兩種方案的較大值
dp[c] =
max(dp[c].toDouble(), (dp[c - wgt[i - 1]] + value[i - 1]).toDouble()).toInt()
max(dp[c], dp[c - wgt[i - 1]] + _val[i - 1])
}
}
}
@@ -110,27 +108,27 @@ fun knapsackDPComp(
/* Driver Code */
fun main() {
val wgt = intArrayOf(10, 20, 30, 40, 50)
val value = intArrayOf(50, 120, 150, 210, 240)
val _val = intArrayOf(50, 120, 150, 210, 240)
val cap = 50
val n = wgt.size
// 暴力搜尋
var res = knapsackDFS(wgt, value, n, cap)
var res = knapsackDFS(wgt, _val, n, cap)
println("不超過背包容量的最大物品價值為 $res")
// 記憶化搜尋
val mem = Array(n + 1) { IntArray(cap + 1) }
for (row in mem) {
Arrays.fill(row, -1)
row.fill(-1)
}
res = knapsackDFSMem(wgt, value, mem, n, cap)
res = knapsackDFSMem(wgt, _val, mem, n, cap)
println("不超過背包容量的最大物品價值為 $res")
// 動態規劃
res = knapsackDP(wgt, value, cap)
res = knapsackDP(wgt, _val, cap)
println("不超過背包容量的最大物品價值為 $res")
// 空間最佳化後的動態規劃
res = knapsackDPComp(wgt, value, cap)
res = knapsackDPComp(wgt, _val, cap)
println("不超過背包容量的最大物品價值為 $res")
}
}