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feat(csharp) .NET 8.0 code migration (#966)

Browse Source 
* .net 8.0 migration

* update docs

* revert change

* revert change and update appendix docs

* remove static

* Update binary_search_insertion.cs

* Update binary_search_insertion.cs

* Update binary_search_edge.cs

* Update binary_search_insertion.cs

* Update binary_search_edge.cs

---------

Co-authored-by: Yudong Jin <krahets@163.com>
This commit is contained in:
hpstory
2023-11-26 23:18:44 +08:00
committed by GitHub
parent d960c99a1f
commit 56b20eff36
93 changed files with 539 additions and 487 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
codes/csharp/chapter_dynamic_programming/climbing_stairs_backtrack.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class climbing_stairs_backtrack {
/* 回溯 */
public void Backtrack(List<int> choices, int state, int n, List<int> res) {
void Backtrack(List<int> choices, int state, int n, List<int> res) {
// 当爬到第 n 阶时，方案数量加 1
if (state == n)
res[0]++;
@@ -24,10 +24,10 @@ public class climbing_stairs_backtrack {
}
/* 爬楼梯：回溯 */
public int ClimbingStairsBacktrack(int n) {
List<int> choices = new() { 1, 2 }; // 可选择向上爬 1 或 2 阶
int ClimbingStairsBacktrack(int n) {
List<int> choices = [1, 2]; // 可选择向上爬 1 或 2 阶
int state = 0; // 从第 0 阶开始爬
List<int> res = new() { 0 }; // 使用 res[0] 记录方案数量
List<int> res = [0]; // 使用 res[0] 记录方案数量
Backtrack(choices, state, n, res);
return res[0];
}

2
codes/csharp/chapter_dynamic_programming/climbing_stairs_constraint_dp.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class climbing_stairs_constraint_dp {
/* 带约束爬楼梯：动态规划 */
public int ClimbingStairsConstraintDP(int n) {
int ClimbingStairsConstraintDP(int n) {
if (n == 1 || n == 2) {
return 1;
}

4
codes/csharp/chapter_dynamic_programming/climbing_stairs_dfs.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class climbing_stairs_dfs {
/* 搜索 */
public int DFS(int i) {
int DFS(int i) {
// 已知 dp[1] 和 dp[2] ，返回之
if (i == 1 || i == 2)
return i;
@@ -18,7 +18,7 @@ public class climbing_stairs_dfs {
}
/* 爬楼梯：搜索 */
public int ClimbingStairsDFS(int n) {
int ClimbingStairsDFS(int n) {
return DFS(n);
}

4
codes/csharp/chapter_dynamic_programming/climbing_stairs_dfs_mem.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class climbing_stairs_dfs_mem {
/* 记忆化搜索 */
public int DFS(int i, int[] mem) {
int DFS(int i, int[] mem) {
// 已知 dp[1] 和 dp[2] ，返回之
if (i == 1 || i == 2)
return i;
@@ -23,7 +23,7 @@ public class climbing_stairs_dfs_mem {
}
/* 爬楼梯：记忆化搜索 */
public int ClimbingStairsDFSMem(int n) {
int ClimbingStairsDFSMem(int n) {
// mem[i] 记录爬到第 i 阶的方案总数，-1 代表无记录
int[] mem = new int[n + 1];
Array.Fill(mem, -1);

4
codes/csharp/chapter_dynamic_programming/climbing_stairs_dp.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class climbing_stairs_dp {
/* 爬楼梯：动态规划 */
public int ClimbingStairsDP(int n) {
int ClimbingStairsDP(int n) {
if (n == 1 || n == 2)
return n;
// 初始化 dp 表，用于存储子问题的解
@@ -24,7 +24,7 @@ public class climbing_stairs_dp {
}
/* 爬楼梯：空间优化后的动态规划 */
public int ClimbingStairsDPComp(int n) {
int ClimbingStairsDPComp(int n) {
if (n == 1 || n == 2)
return n;
int a = 1, b = 2;

6
codes/csharp/chapter_dynamic_programming/coin_change.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class coin_change {
/* 零钱兑换：动态规划 */
public int CoinChangeDP(int[] coins, int amt) {
int CoinChangeDP(int[] coins, int amt) {
int n = coins.Length;
int MAX = amt + 1;
// 初始化 dp 表
@@ -33,7 +33,7 @@ public class coin_change {
}
/* 零钱兑换：空间优化后的动态规划 */
public int CoinChangeDPComp(int[] coins, int amt) {
int CoinChangeDPComp(int[] coins, int amt) {
int n = coins.Length;
int MAX = amt + 1;
// 初始化 dp 表
@@ -57,7 +57,7 @@ public class coin_change {
[Test]
public void Test() {
int[] coins = { 1, 2, 5 };
int[] coins = [1, 2, 5];
int amt = 4;
// 动态规划

6
codes/csharp/chapter_dynamic_programming/coin_change_ii.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class coin_change_ii {
/* 零钱兑换 II：动态规划 */
public int CoinChangeIIDP(int[] coins, int amt) {
int CoinChangeIIDP(int[] coins, int amt) {
int n = coins.Length;
// 初始化 dp 表
int[,] dp = new int[n + 1, amt + 1];
@@ -32,7 +32,7 @@ public class coin_change_ii {
}
/* 零钱兑换 II：空间优化后的动态规划 */
public int CoinChangeIIDPComp(int[] coins, int amt) {
int CoinChangeIIDPComp(int[] coins, int amt) {
int n = coins.Length;
// 初始化 dp 表
int[] dp = new int[amt + 1];
@@ -54,7 +54,7 @@ public class coin_change_ii {
[Test]
public void Test() {
int[] coins = { 1, 2, 5 };
int[] coins = [1, 2, 5];
int amt = 5;
// 动态规划

8
codes/csharp/chapter_dynamic_programming/edit_distance.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class edit_distance {
/* 编辑距离：暴力搜索 */
public int EditDistanceDFS(string s, string t, int i, int j) {
int EditDistanceDFS(string s, string t, int i, int j) {
// 若 s 和 t 都为空，则返回 0
if (i == 0 && j == 0)
return 0;
@@ -30,7 +30,7 @@ public class edit_distance {
}
/* 编辑距离：记忆化搜索 */
public int EditDistanceDFSMem(string s, string t, int[][] mem, int i, int j) {
int EditDistanceDFSMem(string s, string t, int[][] mem, int i, int j) {
// 若 s 和 t 都为空，则返回 0
if (i == 0 && j == 0)
return 0;
@@ -56,7 +56,7 @@ public class edit_distance {
}
/* 编辑距离：动态规划 */
public int EditDistanceDP(string s, string t) {
int EditDistanceDP(string s, string t) {
int n = s.Length, m = t.Length;
int[,] dp = new int[n + 1, m + 1];
// 状态转移：首行首列
@@ -82,7 +82,7 @@ public class edit_distance {
}
/* 编辑距离：空间优化后的动态规划 */
public int EditDistanceDPComp(string s, string t) {
int EditDistanceDPComp(string s, string t) {
int n = s.Length, m = t.Length;
int[] dp = new int[m + 1];
// 状态转移：首行

12
codes/csharp/chapter_dynamic_programming/knapsack.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class knapsack {
/* 0-1 背包：暴力搜索 */
public int KnapsackDFS(int[] weight, int[] val, int i, int c) {
int KnapsackDFS(int[] weight, int[] val, int i, int c) {
// 若已选完所有物品或背包无容量，则返回价值 0
if (i == 0 || c == 0) {
return 0;
@@ -25,7 +25,7 @@ public class knapsack {
}
/* 0-1 背包：记忆化搜索 */
public int KnapsackDFSMem(int[] weight, int[] val, int[][] mem, int i, int c) {
int KnapsackDFSMem(int[] weight, int[] val, int[][] mem, int i, int c) {
// 若已选完所有物品或背包无容量，则返回价值 0
if (i == 0 || c == 0) {
return 0;
@@ -47,7 +47,7 @@ public class knapsack {
}
/* 0-1 背包：动态规划 */
public int KnapsackDP(int[] weight, int[] val, int cap) {
int KnapsackDP(int[] weight, int[] val, int cap) {
int n = weight.Length;
// 初始化 dp 表
int[,] dp = new int[n + 1, cap + 1];
@@ -67,7 +67,7 @@ public class knapsack {
}
/* 0-1 背包：空间优化后的动态规划 */
public int KnapsackDPComp(int[] weight, int[] val, int cap) {
int KnapsackDPComp(int[] weight, int[] val, int cap) {
int n = weight.Length;
// 初始化 dp 表
int[] dp = new int[cap + 1];
@@ -89,8 +89,8 @@ public class knapsack {
[Test]
public void Test() {
int[] weight = { 10, 20, 30, 40, 50 };
int[] val = { 50, 120, 150, 210, 240 };
int[] weight = [10, 20, 30, 40, 50];
int[] val = [50, 120, 150, 210, 240];
int cap = 50;
int n = weight.Length;

6
codes/csharp/chapter_dynamic_programming/min_cost_climbing_stairs_dp.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class min_cost_climbing_stairs_dp {
/* 爬楼梯最小代价：动态规划 */
public int MinCostClimbingStairsDP(int[] cost) {
int MinCostClimbingStairsDP(int[] cost) {
int n = cost.Length - 1;
if (n == 1 || n == 2)
return cost[n];
@@ -25,7 +25,7 @@ public class min_cost_climbing_stairs_dp {
}
/* 爬楼梯最小代价：空间优化后的动态规划 */
public int MinCostClimbingStairsDPComp(int[] cost) {
int MinCostClimbingStairsDPComp(int[] cost) {
int n = cost.Length - 1;
if (n == 1 || n == 2)
return cost[n];
@@ -40,7 +40,7 @@ public class min_cost_climbing_stairs_dp {
[Test]
public void Test() {
int[] cost = { 0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1 };
int[] cost = [0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1];
Console.WriteLine("输入楼梯的代价列表为");
PrintUtil.PrintList(cost);

20
codes/csharp/chapter_dynamic_programming/min_path_sum.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class min_path_sum {
/* 最小路径和：暴力搜索 */
public int MinPathSumDFS(int[][] grid, int i, int j) {
int MinPathSumDFS(int[][] grid, int i, int j) {
// 若为左上角单元格，则终止搜索
if (i == 0 && j == 0) {
return grid[0][0];
@@ -25,7 +25,7 @@ public class min_path_sum {
}
/* 最小路径和：记忆化搜索 */
public int MinPathSumDFSMem(int[][] grid, int[][] mem, int i, int j) {
int MinPathSumDFSMem(int[][] grid, int[][] mem, int i, int j) {
// 若为左上角单元格，则终止搜索
if (i == 0 && j == 0) {
return grid[0][0];
@@ -47,7 +47,7 @@ public class min_path_sum {
}
/* 最小路径和：动态规划 */
public int MinPathSumDP(int[][] grid) {
int MinPathSumDP(int[][] grid) {
int n = grid.Length, m = grid[0].Length;
// 初始化 dp 表
int[,] dp = new int[n, m];
@@ -70,7 +70,7 @@ public class min_path_sum {
}
/* 最小路径和：空间优化后的动态规划 */
public int MinPathSumDPComp(int[][] grid) {
int MinPathSumDPComp(int[][] grid) {
int n = grid.Length, m = grid[0].Length;
// 初始化 dp 表
int[] dp = new int[m];
@@ -94,12 +94,12 @@ public class min_path_sum {
[Test]
public void Test() {
int[][] grid =
{
new int[4] { 1, 3, 1, 5 },
new int[4] { 2, 2, 4, 2 },
new int[4] { 5, 3, 2, 1 },
new int[4] { 4, 3, 5, 2 }
};
[
[1, 3, 1, 5],
[2, 2, 4, 2],
[5, 3, 2, 1],
[4, 3, 5, 2]
];
int n = grid.Length, m = grid[0].Length;

8
codes/csharp/chapter_dynamic_programming/unbounded_knapsack.cs
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@@ -8,7 +8,7 @@ namespace hello_algo.chapter_dynamic_programming;
public class unbounded_knapsack {
/* 完全背包：动态规划 */
public int UnboundedKnapsackDP(int[] wgt, int[] val, int cap) {
int UnboundedKnapsackDP(int[] wgt, int[] val, int cap) {
int n = wgt.Length;
// 初始化 dp 表
int[,] dp = new int[n + 1, cap + 1];
@@ -28,7 +28,7 @@ public class unbounded_knapsack {
}
/* 完全背包：空间优化后的动态规划 */
public int UnboundedKnapsackDPComp(int[] wgt, int[] val, int cap) {
int UnboundedKnapsackDPComp(int[] wgt, int[] val, int cap) {
int n = wgt.Length;
// 初始化 dp 表
int[] dp = new int[cap + 1];
@@ -49,8 +49,8 @@ public class unbounded_knapsack {
[Test]
public void Test() {
int[] wgt = { 1, 2, 3 };
int[] val = { 5, 11, 15 };
int[] wgt = [1, 2, 3];
int[] val = [5, 11, 15];
int cap = 4;
// 动态规划