huangzj
51 lines
1.3 KiB
Go
51 lines
1.3 KiB
Go
/*
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* @Author : huangzj
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* @Time : 2020/7/31 15:35
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* @Description：背包最优问题工具类
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* 基本场景：给定n个重量为w1w 1，w2w 2 ,w3w 3 ,…,wnw n ，价值为v1v 1 ,v2v 2 ,v3v 3 ,…,vnv n 的物品和容量为CC的背包，求这个物品中一个最有价值的子集，使得在满足背包的容量的前提下，包内的总价值最大
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* 参考地址：https://blog.csdn.net/chanmufeng/article/details/82955730
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*/
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package knapsackOptimization
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import (
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"fmt"
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"testing"
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)
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func TestKnapsackOptimizationUtil(t *testing.T) {
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bagItem := make([]*BagItem, 0)
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bagItem = append(bagItem, &BagItem{
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Value: 3,
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Weight: 2,
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})
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bagItem = append(bagItem, &BagItem{
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Value: 3,
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Weight: 3,
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})
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bagItem = append(bagItem, &BagItem{
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Value: 4,
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Weight: 4,
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})
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bagItem = append(bagItem, &BagItem{
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Value: 5,
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Weight: 5,
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})
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bagItem = append(bagItem, &BagItem{
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Value: 6,
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Weight: 2,
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})
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bagItem = append(bagItem, &BagItem{
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Value: 7,
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Weight: 2,
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})
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cmd := NewKnapsackOptimization(bagItem, 12)
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cmd1 := NewKnapsackOptimization(bagItem, 12)
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cmd2 := NewKnapsackOptimization(bagItem, 12)
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fmt.Print(cmd.OptimizePackageByRecursion()) //通过递归解背包问题
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fmt.Println(cmd1.KnapsackCycle()) //通过逆序解背包问题.
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fmt.Println(cmd2.KnapsackCycleSimple()) //通过一维数组来解决背包问题
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}
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