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// File: knapsack_test.go
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// Created Time: 2023-07-23
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// Author: Reanon (793584285@qq.com)
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package chapter_dynamic_programming
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import (
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"fmt"
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"testing"
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)
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func TestKnapsack(t *testing.T) {
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wgt := []int{10, 20, 30, 40, 50}
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val := []int{50, 120, 150, 210, 240}
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c := 50
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n := len(wgt)
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// 暴力搜索
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res := knapsackDFS(wgt, val, n, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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// 记忆化搜索
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mem := make([][]int, n+1)
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for i := 0; i <= n; i++ {
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mem[i] = make([]int, c+1)
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for j := 0; j <= c; j++ {
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mem[i][j] = -1
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}
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}
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res = knapsackDFSMem(wgt, val, mem, n, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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// 动态规划
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res = knapsackDP(wgt, val, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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// 空间优化后的动态规划
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res = knapsackDPComp(wgt, val, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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}
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func TestUnboundedKnapsack(t *testing.T) {
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wgt := []int{1, 2, 3}
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val := []int{5, 11, 15}
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c := 4
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// 动态规划
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res := unboundedKnapsackDP(wgt, val, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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// 空间优化后的动态规划
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res = unboundedKnapsackDPComp(wgt, val, c)
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fmt.Printf("不超过背包容量的最大物品价值为 %d\n", res)
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}
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