Add kotlin codes for the chapter of greedy (#1103)
* feat(kotlin): add kotlin code for dynamic programming. * Update knapsack.kt * feat(kotlin): add kotlin codes for graph. * style(kotlin): reformatted the codes. * feat(kotlin): add kotlin codes for the chapter of greedy. * Update max_product_cutting.ktpull/1105/head^2
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/**
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* File: coin_change_greedy.kt
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* Created Time: 2024-01-25
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* Author: curtishd (1023632660@qq.com)
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*/
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package chapter_greedy
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/* 零钱兑换:贪心 */
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fun coinChangeGreedy(coins: IntArray, amt: Int): Int {
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// 假设 coins 列表有序
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var am = amt
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var i = coins.size - 1
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var count = 0
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// 循环进行贪心选择,直到无剩余金额
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while (am > 0) {
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// 找到小于且最接近剩余金额的硬币
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while (i > 0 && coins[i] > am) {
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i--
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}
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// 选择 coins[i]
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am -= coins[i]
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count++
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}
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// 若未找到可行方案,则返回 -1
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return if (am == 0) count else -1
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}
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/* Driver Code */
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fun main() {
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// 贪心:能够保证找到全局最优解
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var coins = intArrayOf(1, 5, 10, 20, 50, 100)
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var amt = 186
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var res = coinChangeGreedy(coins, amt)
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println("\ncoins = ${coins.contentToString()}, amt = $amt")
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println("凑到 $amt 所需的最少硬币数量为 $res")
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// 贪心:无法保证找到全局最优解
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coins = intArrayOf(1, 20, 50)
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amt = 60
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res = coinChangeGreedy(coins, amt)
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println("\ncoins = ${coins.contentToString()}, amt = $amt")
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println("凑到 $amt 所需的最少硬币数量为 $res")
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println("实际上需要的最少数量为 3 ,即 20 + 20 + 20")
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// 贪心:无法保证找到全局最优解
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coins = intArrayOf(1, 49, 50)
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amt = 98
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res = coinChangeGreedy(coins, amt)
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println("\ncoins = ${coins.contentToString()}, amt = $amt")
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println("凑到 $amt 所需的最少硬币数量为 $res")
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println("实际上需要的最少数量为 2 ,即 49 + 49")
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}
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/**
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* File: fractional_knapsack.kt
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* Created Time: 2024-01-25
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* Author: curtishd (1023632660@qq.com)
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*/
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package chapter_greedy
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import java.util.*
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/* 物品 */
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class Item(
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val w: Int, // 物品
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val v: Int // 物品价值
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)
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/* 分数背包:贪心 */
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fun fractionalKnapsack(
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wgt: IntArray,
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value: IntArray,
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c: Int
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): Double {
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// 创建物品列表,包含两个属性:重量、价值
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var cap = c
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val items = arrayOfNulls<Item>(wgt.size)
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for (i in wgt.indices) {
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items[i] = Item(wgt[i], value[i])
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}
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// 按照单位价值 item.v / item.w 从高到低进行排序
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Arrays.sort(items, Comparator.comparingDouble { item: Item -> -(item.v.toDouble() / item.w) })
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// 循环贪心选择
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var res = 0.0
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for (item in items) {
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if (item!!.w <= cap) {
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// 若剩余容量充足,则将当前物品整个装进背包
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res += item.v.toDouble()
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cap -= item.w
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} else {
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// 若剩余容量不足,则将当前物品的一部分装进背包
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res += item.v.toDouble() / item.w * cap
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// 已无剩余容量,因此跳出循环
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break
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}
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}
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return res
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}
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/* Driver Code */
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fun main() {
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val wgt = intArrayOf(10, 20, 30, 40, 50)
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val values = intArrayOf(50, 120, 150, 210, 240)
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val cap = 50
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// 贪心算法
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val res = fractionalKnapsack(wgt, values, cap)
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println("不超过背包容量的最大物品价值为 $res")
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}
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