动态规划二 & 贪心算法实验三

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动态规划二 & 贪心算法实验三

阅读原文时间：2023年09月07日阅读：1

有一个背包其容积 C = 13。现有表格内的物品可以购买。

商品

价格 P

体积 V

啤酒

汽水

饼干

面包

牛奶

1 用动态规划法解决“0-1背包问题”

（1）使用模块化开发的方式，把解决问题的过程抽象成三个模块，构造值结构的归并排序MergeSort类模块、处理值问题的背包Knapsack类模块和创建商品的KnapsackItem类模块。

（2） MergeSort类在Knapsack类中，被封装成处理归并排序的回调函数, 定义了最优值，负责在Knapsack类对sortPossibleItemsByWeight函数背包的weight由小到大进行排序，sortPossibleItemsByValue则对value由大到小执行排序，sortPossibleItemsByValuePerWeightRatio是price/weight的性价比，由大到小执行排序。其中Knapsack类又包含了处理购物清单selectedItems的totalValue()、totalWeight()、countCoin()、getLastProcduct()、setLastProcductValue(value)和setLastProcductWeight(weight)的功能函数，实现对购物清单里面的商品进行处理。 KnapsackItem类则实现了创建商品的对象，定义了商品的{value（价格）, weight（体积）, itemsInStock = 1（数量） ,produce（名称）}，实现对单个商品的totalValue()、totalWeight()、valuePerWeightRatio()、setValue(value)、setWeight(weight)和toString()进行商品属性处理的功能函数。

（3）0-1背包问题的solveZeroOneKnapsackProblem函数。

1)首先递归地对value降序排序，然后对weight进行升序排序，定义最优解的值。

2)再运用矩阵链乘法，创建一个子串长度与背包商品n和背包容积的n*m的空矩阵，第一维度的矩阵中每行的数据是用来定义对商品n的价格进行逐个比较的子串。第二维度的矩阵中每行列对应的数据是对每个物品从1至背包最大体积n的逐个价格的组合。从每一行中对价格的子问题进行累加记录，从而解决大问题，计算出最优的价格值。

3）最后通过循环遍历每件商品，用之前所得到最优的价格值矩阵，通过自底向上比较最后一行得到的最优值与上一行的最优值比较。如果两个值相同，则说明最后一行的物品没有被购买，不能选择价格最高者。如果两个值不相同，则说明当前商品被购买，应该加入购物清单。如此反复进行，最终构造购物清单的最优解。

2 核心函数实现代码。

solveZeroOneKnapsackProblem() {
this.sortPossibleItemsByValue();
this.sortPossibleItemsByWeight();
this.selectedItems = [];
const numberOfRows = this.possibleItems.length;
const numberOfColumns = this.weightLimit;
const knapsackMatrix = Array(numberOfRows).fill(null).map(() => {
return Array(numberOfColumns + 1).fill(null);
});
for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) { knapsackMatrix[itemIndex][0] = 0; } for (let weightIndex = 1; weightIndex <= this.weightLimit; weightIndex += 1) { const itemIndex = 0; const itemWeight = this.possibleItems[itemIndex]._weight; const itemValue = this.possibleItems[itemIndex]._value; knapsackMatrix[itemIndex][weightIndex] = itemWeight <= weightIndex ? itemValue : 0; } for (let itemIndex = 1; itemIndex < this.possibleItems.length; itemIndex += 1) { for (let weightIndex = 1; weightIndex <= this.weightLimit; weightIndex += 1) { const currentItemWeight = this.possibleItems[itemIndex]._weight; const currentItemValue = this.possibleItems[itemIndex]._value; if (currentItemWeight > weightIndex) {
knapsackMatrix[itemIndex][weightIndex] = knapsackMatrix[itemIndex - 1][weightIndex];
} else {
knapsackMatrix[itemIndex][weightIndex] = Math.max(
currentItemValue + knapsackMatrix[itemIndex - 1][weightIndex - currentItemWeight],
knapsackMatrix[itemIndex - 1][weightIndex],
);
}
}
}
let itemIndex = this.possibleItems.length - 1;
let weightIndex = this.weightLimit;
while (itemIndex > 0) {
const currentItem = this.possibleItems[itemIndex];
const prevItem = this.possibleItems[itemIndex - 1];
if (
knapsackMatrix[itemIndex][weightIndex]
&& knapsackMatrix[itemIndex][weightIndex] === knapsackMatrix[itemIndex - 1][weightIndex]
) {
const prevSumValue = knapsackMatrix[itemIndex - 1][weightIndex];
const prevPrevSumValue = knapsackMatrix[itemIndex - 2][weightIndex];
if (
!prevSumValue
|| (prevSumValue && prevPrevSumValue !== prevSumValue)
) {
this.selectedItems.push(prevItem);
}
} else if (knapsackMatrix[itemIndex - 1][weightIndex - currentItem._weight]) {
this.selectedItems.push(prevItem);
weightIndex -= currentItem._weight;
}
itemIndex -= 1;
}
}

3 背包内物品的组合与价格。

4 贪心算法解决“部分背包问题”的流程步骤。

贪心算法的前1-2步骤和动态规划相同，区别在于核心问题的处理函数。

部分背包问题的solveUnboundedKnapsackProblem函数。

1)首先递归地对value降序排序，然后通过price/weight性价比进行升序排序，定义最优解的值。

2）通过循环，每次选择性价比最高的商品。因为可以部分购买，只需由上往下，购买直至满足背包的最大体积。

5 核心函数实现代码。

solveUnboundedKnapsackProblem() {
this.sortPossibleItemsByValue();
this.sortPossibleItemsByValuePerWeightRatio();
for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) { if (this.totalWeight < this.weightLimit) { const currentItem = this.possibleItems[itemIndex]; const availableWeight = this.weightLimit - this.totalWeight; const maxPossibleItemsCount = Math.floor(availableWeight / currentItem._weight); if (maxPossibleItemsCount > currentItem.itemsInStock) {
currentItem.quantity = currentItem.itemsInStock;
} else if (maxPossibleItemsCount) {
currentItem.quantity = maxPossibleItemsCount;
}
this.selectedItems.push(currentItem);
}
}
}

6 背包内物品的组合与价格。

7 两种方法对于背包问题优点与缺点。

（1）动态规划：

优点：

1）可以获得商品价格每个组合的对比，得到全局最优解

2）动态规划方法反映了动态过程演变的联系和特征，在计算时可以利用实际知识和经验提高求解效率。

缺点：

1）空间需求大，需要额外的内存空间，并且一维问题可能需要二维空间。

2）构建解决问题的方法复杂，需要对寻找最优值进行大量处理。

（2）贪心算法：

优点：

1）空间和时间消耗相对比动态规划小

2）构建解决问题的方法简单，只需关注背包的体积

缺点：

1）不能保证求得的最后解是最佳的；

2）不能用来求最大或最小解问题；

3）只能求满足某些约束条件的可行解的范围。

现在有面值分别为2角5分，1角，5分，1分的硬币，请给出找n分钱的最佳方案（要求找出的硬币数目最少）。

1 贪心算法解决“找n分钱的最佳方案”的流程步骤。

（1）基本定义和第一问动态规划定义1相同，把创建商品的KnapsackItem类换成CoinItem类模块。

（2）基本定义和第一问动态规划定义2相同。
CoinItem类实现了创建商品的对象，定义了商品的{weight(n分钱), itemsInStock = 10000（存货数量）}，实现对单个商品的totalWeight()、setWeight(weight)、set quantity(quantity)和toString()进行硬币规格处理的功能函数。

（3）n分钱问题的solveCoinProblem函数。

1)首先使用sortCoinByWeight递归地对硬币规格降序排序，定义最优解的值。

2）由于硬币已大到小排序，由上往下，遍历n分钱。当this.possibleItems[itemIndex]._weight（当前硬币的规格） <= this.weightLimit（n分钱），通过maxPossibleItemsCount=Math.floor(availableWeight/currentItem._weight)求得凑齐n分钱每个规格的最大可能硬币个数。如果maxPossibleItemsCount > currentItem.itemsInStock判断最大硬币个数超过定义的存货数量时，把当前存入的列表的硬币实际个数设置成硬币的存货数量，如果maxPossibleItemsCount && maxPossibleItemsCount > 0，判断硬币的最大可能数不能为负数或0，并把硬币实际个数设置最大可能硬币个数。最后如果该硬币的最大可能硬币个数为0，那么该硬币设置数量为0。

2 核心函数实现代码。

solveCoinProblem() {
this.sortCoinByWeight()
for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) { if (this.possibleItems[itemIndex]._weight <= this.weightLimit) { const currentItem = this.possibleItems[itemIndex]; const availableWeight = this.weightLimit - this.totalWeight; const maxPossibleItemsCount = Math.floor(availableWeight / currentItem._weight); if (maxPossibleItemsCount > currentItem.itemsInStock) {
currentItem._quantity = currentItem.itemsInStock;
} else if (maxPossibleItemsCount && maxPossibleItemsCount > 0) {
currentItem._quantity = maxPossibleItemsCount;
}
if(!maxPossibleItemsCount){
currentItem._quantity = maxPossibleItemsCount;
}
this.selectedItems.push(currentItem);
}
}
}

3 找5分钱、17分钱、22分钱、35分钱的最佳方案。

class MergeSort{
constructor(originalCallbacks) {
this.callbacks = MergeSort.initSortingCallbacks(originalCallbacks);
this.comparator = this.callbacks.compareCallback || MergeSort.defaultCompareFunction;
}
static initSortingCallbacks(originalCallbacks) {
const callbacks = originalCallbacks || {};
const stubCallback = () => {};

  callbacks.compareCallback = callbacks.compareCallback || undefined;  
  callbacks.visitingCallback = callbacks.visitingCallback || stubCallback;

  return callbacks;  
}

sort(originalArray) {  
  if (originalArray.length <= 1) {  
    return originalArray;  
  }

  const middleIndex = Math.floor(originalArray.length / 2);  
  const leftArray = originalArray.slice(0, middleIndex);  
  const rightArray = originalArray.slice(middleIndex, originalArray.length);

  const leftSortedArray = this.sort(leftArray);  
  const rightSortedArray = this.sort(rightArray);

  return this.mergeSortedArrays(leftSortedArray, rightSortedArray);  
}

lessThan(a, b) {  
  return this.comparator(a, b) < 0;  
}

equal(a, b) {  
  return this.comparator(a, b) === 0;  
}

lessThanOrEqual(a, b) {  
  return this.lessThan(a, b) || this.equal(a, b);  
}

static defaultCompareFunction(a, b) {  
  if (a === b) {  
    return 0;  
  }

  return a < b ? -1 : 1;  
}

mergeSortedArrays(leftArray, rightArray) {  
  const sortedArray = \[\];  
  let leftIndex = 0;  
  let rightIndex = 0;

  while (leftIndex < leftArray.length && rightIndex < rightArray.length) {  
    let minElement = null;  
    if (this.lessThanOrEqual(leftArray\[leftIndex\], rightArray\[rightIndex\])) {  
      minElement = leftArray\[leftIndex\];  
      leftIndex += 1;  
    } else {  
      minElement = rightArray\[rightIndex\];  
      rightIndex += 1;  
    }  
    sortedArray.push(minElement);  
  }

  return sortedArray  
    .concat(leftArray.slice(leftIndex))  
    .concat(rightArray.slice(rightIndex));  
}

}

const fp = require('lodash/fp');
//背包
class Knapsack {

constructor(possibleItems, weightLimit) {
this.selectedItems = [];
this.weightLimit = weightLimit;
this.possibleItems = possibleItems;
}

sortPossibleItemsByWeight() {
this.possibleItems = new MergeSort({

  compareCallback: (itemA, itemB) => {  
    if (itemA.\_weight === itemB.\_weight) {  
      return 0;  
    }

    return itemA.\_weight < itemB.\_weight ? -1 : 1;  
  },  
}).sort(this.possibleItems);

}

sortCoinByWeight() {
this.possibleItems = new MergeSort({

  compareCallback: (itemA, itemB) => {  
    if (itemA.\_weight === itemB.\_weight) {  
      return 0;  
    }

    return itemA.\_weight > itemB.\_weight ? -1 : 1;  
  },  
}).sort(this.possibleItems);

}

sortPossibleItemsByValue() {
this.possibleItems = new MergeSort({
compareCallback: (itemA, itemB) => {
if (itemA._value === itemB._value) {
return 0;
}

    return itemA.\_value > itemB.\_value ? -1 : 1;  
  },  
}).sort(this.possibleItems);

}

sortPossibleItemsByValuePerWeightRatio() {
this.possibleItems = new MergeSort({
compareCallback: (itemA, itemB) => {
if (itemA.valuePerWeightRatio === itemB.valuePerWeightRatio) {
return 0;
}

    return itemA.valuePerWeightRatio > itemB.valuePerWeightRatio ? -1 : 1;  
  },  
}).sort(this.possibleItems);

}

solveZeroOneKnapsackProblem() {
this.sortPossibleItemsByValue();
this.sortPossibleItemsByWeight();
this.selectedItems = [];
// console.log(this.sortPossibleItemsByValue());
// console.log(this.sortPossibleItemsByWeight());
const numberOfRows = this.possibleItems.length;
const numberOfColumns = this.weightLimit;
console.log(numberOfRows, numberOfColumns)
const knapsackMatrix = Array(numberOfRows).fill(null).map(() => {
return Array(numberOfColumns + 1).fill(null);
});
// console.log(knapsackMatrix)

for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) {  
  knapsackMatrix\[itemIndex\]\[0\] = 0;  
}  
for (let weightIndex = 1; weightIndex <= this.weightLimit; weightIndex += 1) {  
  const itemIndex = 0;  
  const itemWeight = this.possibleItems\[itemIndex\].\_weight;  
  const itemValue = this.possibleItems\[itemIndex\].\_value;  
  knapsackMatrix\[itemIndex\]\[weightIndex\] = itemWeight <= weightIndex ? itemValue : 0;  
}

for (let itemIndex = 1; itemIndex < this.possibleItems.length; itemIndex += 1) {  
  for (let weightIndex = 1; weightIndex <= this.weightLimit; weightIndex += 1) {  
    const currentItemWeight = this.possibleItems\[itemIndex\].\_weight;  
    const currentItemValue = this.possibleItems\[itemIndex\].\_value;

    if (currentItemWeight > weightIndex) {  
      knapsackMatrix\[itemIndex\]\[weightIndex\] = knapsackMatrix\[itemIndex - 1\]\[weightIndex\];  
    } else {  
      knapsackMatrix\[itemIndex\]\[weightIndex\] = Math.max(  
        currentItemValue + knapsackMatrix\[itemIndex - 1\]\[weightIndex - currentItemWeight\],  
        knapsackMatrix\[itemIndex - 1\]\[weightIndex\],  
      );  
    }  
  }  
}  
// console.log(knapsackMatrix)

let itemIndex = this.possibleItems.length - 1;  
let weightIndex = this.weightLimit;  
while (itemIndex > 0) {  
  const currentItem = this.possibleItems\[itemIndex\];  
  const prevItem = this.possibleItems\[itemIndex - 1\];  
  console.log('-----',currentItem,'---\\n');  
  if (  
    knapsackMatrix\[itemIndex\]\[weightIndex\]  
    && knapsackMatrix\[itemIndex\]\[weightIndex\] === knapsackMatrix\[itemIndex - 1\]\[weightIndex\]  
  ) {  
    const prevSumValue = knapsackMatrix\[itemIndex - 1\]\[weightIndex\];  
    const prevPrevSumValue = knapsackMatrix\[itemIndex - 2\]\[weightIndex\];  
    if (  
      !prevSumValue  
      || (prevSumValue && prevPrevSumValue !== prevSumValue)  
    ) {  
      this.selectedItems.push(prevItem);  
    }  
  } else if (knapsackMatrix\[itemIndex - 1\]\[weightIndex - currentItem.\_weight\]) {  
    // console.log(currentItem.\_weight, currentItem.weight)  
    this.selectedItems.push(prevItem);  
    weightIndex -= currentItem.\_weight;  
  }  
  itemIndex -= 1;  
console.log(knapsackMatrix)

}

solveUnboundedKnapsackProblem() {
// this.sortPossibleItemsByValue();
this.sortPossibleItemsByValuePerWeightRatio();
console.log(this.possibleItems);
// console.log(this.sortPossibleItemsByWeight());
for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) {
if (this.totalWeight < this.weightLimit) {
const currentItem = this.possibleItems[itemIndex];

    const availableWeight = this.weightLimit - this.totalWeight;

    const maxPossibleItemsCount = Math.floor(availableWeight / currentItem.\_weight);

    if (maxPossibleItemsCount > currentItem.itemsInStock) {

      currentItem.quantity = currentItem.itemsInStock;  
    } else if (maxPossibleItemsCount) {  
      currentItem.quantity = maxPossibleItemsCount;  
    }

    this.selectedItems.push(currentItem);  
  }  
}

}
solveCoinProblem() {
this.sortCoinByWeight()

for (let itemIndex = 0; itemIndex < this.possibleItems.length; itemIndex += 1) {  
  if (this.possibleItems\[itemIndex\].\_weight <= this.weightLimit) {

    const currentItem = this.possibleItems\[itemIndex\];  
    const availableWeight = this.weightLimit - this.totalWeight;  
    const maxPossibleItemsCount = Math.floor(availableWeight / currentItem.\_weight);

    if (maxPossibleItemsCount > currentItem.itemsInStock) {  
      currentItem.\_quantity = currentItem.itemsInStock;

    } else if (maxPossibleItemsCount && maxPossibleItemsCount > 0) {  
      currentItem.\_quantity = maxPossibleItemsCount;  
    }

    if(!maxPossibleItemsCount){  
      currentItem.\_quantity = maxPossibleItemsCount;  
    }

    this.selectedItems.push(currentItem);  
  }  
}

}
get totalValue() {
/** @var {KnapsackItem} item */
return this.selectedItems.reduce((accumulator, item) => {
return accumulator + item.totalValue;
}, 0);
}

get totalWeight() {
/** @var {KnapsackItem} item */
return this.selectedItems.reduce((accumulator, item) => {
return accumulator + item.totalWeight;
}, 0);
}
get countCoin() {
return this.selectedItems.reduce((accumulator, item) => {
return accumulator + item._quantity;
}, 0);
}
get getLastProcduct() {
// console.log(fp.last(this.selectedItems));
return fp.last(this.selectedItems);
}
set setLastProcductValue(value) {
fp.last(this.selectedItems).value = value;
}
set setLastProcductWeight(weight) {
fp.last(this.selectedItems).weight = weight
}
}
class KnapsackItem {

constructor({ value, weight, itemsInStock = 1 ,produce}) {
// this.value = value;
// this.weight = weight;
this._value = value;
this._weight = weight;
this.itemsInStock = itemsInStock;
this.produce = produce;
this.quantity = 1;
}

get totalValue() {
return this._value * this.quantity;
}

get totalWeight() {
return this._weight * this.quantity;
}

get valuePerWeightRatio() {
return this._value / this._weight;
}
set value(value) {
this._value = value;
}
set weight(weight) {
this._weight = weight;
}
toString() {

return \`购买的物品为：${this.produce} 价格为: ${this.\_value} 体积为: ${this.\_weight}\`;

}

class CoinItem {
constructor({weight, itemsInStock = 1000}) {
this._weight = weight;
this.itemsInStock = itemsInStock;
this._quantity = 1;
}

get totalWeight() {
return this._weight * this._quantity;
}

set weight(weight) {
this._weight = weight;
}
set quantity (quantity) {
this._quantity = quantity;
}
toString() {
return `币值为: ${this._weight} 数量为: ${this._quantity}`;
}

}
const possibleKnapsackItems = [
new KnapsackItem({produce: '啤酒', value: 24, weight: 10 }),//2.4
new KnapsackItem({produce: '汽水', value: 2, weight: 3 }),//0.6
new KnapsackItem({produce: '饼干', value: 9, weight: 4 }),//2.2
new KnapsackItem({produce: '面包', value: 10, weight: 5 }),//2
new KnapsackItem({produce: '牛奶', value: 9, weight: 4 }),//2.2
];

let maxKnapsackWeight = 13;

//动态规划
const dynamicKnapsack = new Knapsack(possibleKnapsackItems, maxKnapsackWeight);
dynamicKnapsack.solveZeroOneKnapsackProblem();
console.log(`总价格为${dynamicKnapsack.totalValue} 总体积为${dynamicKnapsack.totalWeight}`);
dynamicKnapsack.selectedItems.map(x => console.log(x.toString()));
/*
*/
// maxKnapsackWeight = 13;
/*
//贪心算法
const greedyKnapsack = new Knapsack(possibleKnapsackItems, maxKnapsackWeight);
greedyKnapsack.solveUnboundedKnapsackProblem();
let lasted = greedyKnapsack .totalWeight - maxKnapsackWeight
if(lasted === 0){
console.log(`总价格为${greedyKnapsack.totalValue} 总体积为${greedyKnapsack .totalWeight}`);
greedyKnapsack.selectedItems.map(x => console.log(x.toString()));
}else{
let lastProduce = greedyKnapsack.getLastProcduct;
let lastValueRatio = lastProduce.valuePerWeightRatio.toFixed(2);
let lastProduceWeight = lastProduce._weight - lasted;
let lastProduceValue = lastValueRatio * lastProduceWeight;
greedyKnapsack.setLastProcductValue = lastProduceValue;
greedyKnapsack.setLastProcductWeight = lastProduceWeight;
// console.log(greedyKnapsack.setLastProcductValue = lastValueRatio)
// console.log(lastProduceWeight, lastProduceValue)
console.log(`总价格为${greedyKnapsack.totalValue} 总体积为${greedyKnapsack.totalWeight}`);
greedyKnapsack.selectedItems.map(x => console.log(x.toString()));
}
*/
/*
//贪心算法找硬币
const coin = [
new CoinItem({ weight: 25 }),//2.4
new CoinItem({ weight: 10 }),//0.6
new CoinItem({ weight: 5 }),//2.2
new CoinItem({ weight: 1 }),//2.2

];
let searchCoin = [5, 17, 22, 35];
searchCoin.map(x => {
console.log('----------------------------------\n');
console.log(`找 ${x} 分钱的最佳方案为：`)
const greedyCoin = new Knapsack(coin, x);
greedyCoin.solveCoinProblem();
console.log(`总币值为${greedyCoin.totalWeight} 总硬币数目为 ${greedyCoin.countCoin}`);
greedyCoin.selectedItems.filter(x => x._quantity > 0).map(x => console.log(x.toString()));
console.log('\n----------------------------------');
});
*/
// console.log(greedyCoin.selectedItems);

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