Feature/chapter dynamic programming swift (#608)

* feat: add Swift codes for intro_to_dynamic_programming article

* feat: add Swift codes for dp_problem_features article

* feat: add Swift codes for dp_solution_pipeline article

* feat: add Swift codes for knapsack_problem article

* feat: add Swift codes for unbounded_knapsack_problem article

* feat: add Swift codes for edit_distance_problem article

codes/swift/chapter_dynamic_programming/unbounded_knapsack.swift

@@ -0,0 +1,63 @@
+/**
+ * File: unbounded_knapsack.swift
+ * Created Time: 2023-07-15
+ * Author: nuomi1 (nuomi1@qq.com)
+ */
+
+/* 完全背包：动态规划 */
+func unboundedKnapsackDP(wgt: [Int], val: [Int], cap: Int) -> Int {
+    let n = wgt.count
+    // 初始化 dp 表
+    var dp = Array(repeating: Array(repeating: 0, count: cap + 1), count: n + 1)
+    // 状态转移
+    for i in stride(from: 1, through: n, by: 1) {
+        for c in stride(from: 1, through: cap, by: 1) {
+            if wgt[i - 1] > c {
+                // 若超过背包容量，则不选物品 i
+                dp[i][c] = dp[i - 1][c]
+            } else {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[i][c] = max(dp[i - 1][c], dp[i][c - wgt[i - 1]] + val[i - 1])
+            }
+        }
+    }
+    return dp[n][cap]
+}
+
+/* 完全背包：状态压缩后的动态规划 */
+func unboundedKnapsackDPComp(wgt: [Int], val: [Int], cap: Int) -> Int {
+    let n = wgt.count
+    // 初始化 dp 表
+    var dp = Array(repeating: 0, count: cap + 1)
+    // 状态转移
+    for i in stride(from: 1, through: n, by: 1) {
+        for c in stride(from: 1, through: cap, by: 1) {
+            if wgt[i - 1] > c {
+                // 若超过背包容量，则不选物品 i
+                dp[c] = dp[c]
+            } else {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[c] = max(dp[c], dp[c - wgt[i - 1]] + val[i - 1])
+            }
+        }
+    }
+    return dp[cap]
+}
+
+@main
+enum UnboundedKnapsack {
+    /* Driver Code */
+    static func main() {
+        let wgt = [1, 2, 3]
+        let val = [5, 11, 15]
+        let cap = 4
+
+        // 动态规划
+        var res = unboundedKnapsackDP(wgt: wgt, val: val, cap: cap)
+        print("不超过背包容量的最大物品价值为 \(res)")
+
+        // 状态压缩后的动态规划
+        res = unboundedKnapsackDPComp(wgt: wgt, val: val, cap: cap)
+        print("不超过背包容量的最大物品价值为 \(res)")
+    }
+}