Merge pull request #1235 from KingArthur0205/master

添加 0376. 摆动序列.md, 0053.最大子数组和.md, 0122.买股票的最佳时机II.md, 0055.跳跃游戏.md, 1005. K次取反后最大化的数组和.md, 0135.分发糖果.md C语言解法
This commit is contained in:
程序员Carl
2022-05-06 09:27:28 +08:00
committed by GitHub
6 changed files with 215 additions and 2 deletions

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@ -230,6 +230,60 @@ var maxSubArray = function(nums) {
};
```
### C:
贪心:
```c
int maxSubArray(int* nums, int numsSize){
int maxVal = INT_MIN;
int subArrSum = 0;
int i;
for(i = 0; i < numsSize; ++i) {
subArrSum += nums[i];
// 若当前局部和大于之前的最大结果,对结果进行更新
maxVal = subArrSum > maxVal ? subArrSum : maxVal;
// 若当前局部和为负对结果无益。则从nums[i+1]开始应重新计算。
subArrSum = subArrSum < 0 ? 0 : subArrSum;
}
return maxVal;
}
```
动态规划:
```c
/**
* 解题思路:动态规划:
* 1. dp数组dp[i]表示从0到i的子序列中最大序列和的值
* 2. 递推公式dp[i] = max(dp[i-1] + nums[i], nums[i])
若dp[i-1]<0对最后结果无益。dp[i]则为nums[i]。
* 3. dp数组初始化dp[0]的最大子数组和为nums[0]
* 4. 推导顺序:从前往后遍历
*/
#define max(a, b) (((a) > (b)) ? (a) : (b))
int maxSubArray(int* nums, int numsSize){
int dp[numsSize];
// dp[0]最大子数组和为nums[0]
dp[0] = nums[0];
// 若numsSize为1应直接返回nums[0]
int subArrSum = nums[0];
int i;
for(i = 1; i < numsSize; ++i) {
dp[i] = max(dp[i - 1] + nums[i], nums[i]);
// 若dp[i]大于之前记录的最大值,进行更新
if(dp[i] > subArrSum)
subArrSum = dp[i];
}
return subArrSum;
}
```
### TypeScript
**贪心**
@ -267,5 +321,6 @@ function maxSubArray(nums: number[]): number {
-----------------------
<div align="center"><img src=https://code-thinking.cdn.bcebos.com/pics/01二维码一.jpg width=500> </img></div>

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@ -154,6 +154,30 @@ var canJump = function(nums) {
};
```
### C
```c
#define max(a, b) (((a) > (b)) ? (a) : (b))
bool canJump(int* nums, int numsSize){
int cover = 0;
int i;
// 只可能获取cover范围中的步数所以i<=cover
for(i = 0; i <= cover; ++i) {
// 更新cover为从i出发能到达的最大值/cover的值中较大值
cover = max(i + nums[i], cover);
// 若更新后cover可以到达最后的元素返回true
if(cover >= numsSize - 1)
return true;
}
return false;
}
```
### TypeScript
```typescript

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@ -281,7 +281,7 @@ function maxProfit(prices: number[]): number {
```
C:
贪心:
```c
int maxProfit(int* prices, int pricesSize){
int result = 0;
@ -296,5 +296,27 @@ int maxProfit(int* prices, int pricesSize){
}
```
动态规划:
```c
#define max(a, b) (((a) > (b)) ? (a) : (b))
int maxProfit(int* prices, int pricesSize){
int dp[pricesSize][2];
dp[0][0] = 0 - prices[0];
dp[0][1] = 0;
int i;
for(i = 1; i < pricesSize; ++i) {
// dp[i][0]为i-1天持股的钱数/在第i天用i-1天的钱买入的最大值。
// 若i-1天持股且第i天买入股票比i-1天持股时更亏说明应在i-1天时持股
dp[i][0] = max(dp[i-1][0], dp[i-1][1] - prices[i]);
//dp[i][1]为i-1天不持股钱数/在第i天卖出所持股票dp[i-1][0] + prices[i]的最大值
dp[i][1] = max(dp[i-1][1], dp[i-1][0] + prices[i]);
}
// 返回在最后一天不持股时的钱数(将股票卖出后钱最大化)
return dp[pricesSize - 1][1];
}
```
-----------------------
<div align="center"><img src=https://code-thinking.cdn.bcebos.com/pics/01二维码一.jpg width=500> </img></div>

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@ -238,6 +238,49 @@ var candy = function(ratings) {
};
```
### C
```c
#define max(a, b) (((a) > (b)) ? (a) : (b))
int *initCandyArr(int size) {
int *candyArr = (int*)malloc(sizeof(int) * size);
int i;
for(i = 0; i < size; ++i)
candyArr[i] = 1;
return candyArr;
}
int candy(int* ratings, int ratingsSize){
// 初始化数组,每个小孩开始至少有一颗糖
int *candyArr = initCandyArr(ratingsSize);
int i;
// 先判断右边是否比左边评分高。若是,右边孩子的糖果为左边孩子+1candyArr[i] = candyArr[i - 1] + 1)
for(i = 1; i < ratingsSize; ++i) {
if(ratings[i] > ratings[i - 1])
candyArr[i] = candyArr[i - 1] + 1;
}
// 再判断左边评分是否比右边高。
// 若是,左边孩子糖果为右边孩子糖果+1/自己所持糖果最大值。(若糖果已经比右孩子+1多则不需要更多糖果
// 举例ratings为[1, 2, 3, 1]。此时评分为3的孩子在判断右边比左边大后为3虽然它比最末尾的1(ratings[3])大但是candyArr[3]为1。所以不必更新candyArr[2]
for(i = ratingsSize - 2; i >= 0; --i) {
if(ratings[i] > ratings[i + 1])
candyArr[i] = max(candyArr[i], candyArr[i + 1] + 1);
}
// 求出糖果之和
int result = 0;
for(i = 0; i < ratingsSize; ++i) {
result += candyArr[i];
}
return result;
}
```
### TypeScript
```typescript
@ -264,6 +307,5 @@ function candy(ratings: number[]): number {
-----------------------
<div align="center"><img src=https://code-thinking.cdn.bcebos.com/pics/01二维码一.jpg width=500> </img></div>

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@ -298,6 +298,35 @@ var wiggleMaxLength = function(nums) {
};
```
### C
**贪心**
```c
int wiggleMaxLength(int* nums, int numsSize){
if(numsSize <= 1)
return numsSize;
int length = 1;
int preDiff , curDiff;
preDiff = curDiff = 0;
for(int i = 0; i < numsSize - 1; ++i) {
// 计算当前i元素与i+1元素差值
curDiff = nums[i+1] - nums[i];
// 若preDiff与curDiff符号不符则子序列长度+1。更新preDiff的符号
// 若preDiff与curDiff符号一致当前i元素为连续升序/连续降序子序列的中间元素。不被记录入长度
// 注当preDiff为0时curDiff为正或为负都属于符号不同
if((curDiff > 0 && preDiff <= 0) || (preDiff >= 0 && curDiff < 0)) {
preDiff = curDiff;
length++;
}
}
return length;
}
```
### TypeScript
**贪心**

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@ -211,6 +211,46 @@ var largestSumAfterKNegations = function(nums, k) {
};
```
### C
```c
#define abs(a) (((a) > 0) ? (a) : (-(a)))
// 对数组求和
int sum(int *nums, int numsSize) {
int sum = 0;
int i;
for(i = 0; i < numsSize; ++i) {
sum += nums[i];
}
return sum;
}
int cmp(const void* v1, const void* v2) {
return abs(*(int*)v2) - abs(*(int*)v1);
}
int largestSumAfterKNegations(int* nums, int numsSize, int k){
qsort(nums, numsSize, sizeof(int), cmp);
int i;
for(i = 0; i < numsSize; ++i) {
// 遍历数组,若当前元素<0则将当前元素转变k--
if(nums[i] < 0 && k > 0) {
nums[i] *= -1;
--k;
}
}
// 若遍历完数组后k还有剩余此时所有元素应均为正则将绝对值最小的元素nums[numsSize - 1]变为负
if(k % 2 == 1)
nums[numsSize - 1] *= -1;
return sum(nums, numsSize);
}
```
### TypeScript
```typescript
@ -235,5 +275,6 @@ function largestSumAfterKNegations(nums: number[], k: number): number {
-----------------------
<div align="center"><img src=https://code-thinking.cdn.bcebos.com/pics/01二维码一.jpg width=500> </img></div>