Merge branch 'master' into patch-59

problems/0416.分割等和子集.md

@@ -416,6 +416,7 @@ var canPartition = function(nums) {
 };
 ```
 
+
 TypeScript:
 
 ```ts
@@ -436,5 +437,163 @@ function canPartition(nums: number[]): boolean {
 ```
 
 
+C:
+二维dp：
+```c
+/**
+1. dp数组含义：dp[i][j]为背包重量为j时，从[0-i]元素和最大值
+2. 递推公式：dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - nums[i]] + nums[i])
+3. 初始化：dp[i][0]初始化为0。因为背包重量为0时，不可能放入元素。dp[0][j] = nums[0]，当j >= nums[0] && j < target时
+4. 遍历顺序：先遍历物品，再遍历背包
+*/
+#define MAX(a, b) (((a) > (b)) ? (a) : (b))
+
+int getSum(int* nums, int numsSize) {
+    int sum = 0;
+
+    int i;
+    for(i = 0; i < numsSize; ++i) {
+        sum += nums[i];
+    }
+    return sum;
+}
+
+bool canPartition(int* nums, int numsSize){
+    // 求出元素总和
+    int sum = getSum(nums, numsSize);
+    // 若元素总和为奇数，则不可能得到两个和相等的子数组
+    if(sum % 2)
+        return false;
+
+    // 若子数组的和等于target，则nums可以被分割
+    int target = sum / 2;
+    // 初始化dp数组
+    int dp[numsSize][target + 1];
+    // dp[j][0]都应被设置为0。因为当背包重量为0时，不可放入元素
+    memset(dp, 0, sizeof(int) * numsSize * (target + 1));
+
+    int i, j;
+    // 当背包重量j大于nums[0]时，可以在dp[0][j]中放入元素nums[0]
+    for(j = nums[0]; j <= target; ++j) {
+        dp[0][j] = nums[0];
+    }
+
+    for(i = 1; i < numsSize; ++i) {
+        for(j = 1; j <= target; ++j) {
+            // 若当前背包重量j小于nums[i]，则其值等于只考虑0到i-1物品时的值
+            if(j < nums[i])
+                dp[i][j] = dp[i - 1][j];
+            // 否则，背包重量等于在背包中放入num[i]/不放入nums[i]的较大值
+            else
+                dp[i][j] = MAX(dp[i - 1][j], dp[i - 1][j  - nums[i]] + nums[i]);
+        }
+    }
+    // 判断背包重量为target，且考虑到所有物品时，放入的元素和是否等于target
+    return dp[numsSize - 1][target] == target;
+}
+```
+滚动数组：
+```c
+/**
+1. dp数组含义：dp[j]为背包重量为j时，其中可放入元素的最大值
+2. 递推公式：dp[j] = max(dp[j], dp[j - nums[i]] + nums[i])
+3. 初始化：均初始化为0即可
+4. 遍历顺序：先遍历物品，再后序遍历背包
+*/
+#define MAX(a, b) (((a) > (b)) ? (a) : (b))
+
+int getSum(int* nums, int numsSize) {
+    int sum = 0;
+
+    int i;
+    for(i = 0; i < numsSize; ++i) {
+        sum += nums[i];
+    }
+    return sum;
+}
+
+bool canPartition(int* nums, int numsSize){
+    // 求出元素总和
+    int sum = getSum(nums, numsSize);
+    // 若元素总和为奇数，则不可能得到两个和相等的子数组
+    if(sum % 2)
+        return false;
+    // 背包容量
+    int target = sum / 2;
+
+    // 初始化dp数组，元素均为0
+    int dp[target + 1];
+    memset(dp, 0, sizeof(int) * (target + 1));
+
+    int i, j;
+    // 先遍历物品，后遍历背包
+    for(i = 0; i < numsSize; ++i) {
+        for(j = target; j >= nums[i]; --j) {
+            dp[j] = MAX(dp[j], dp[j - nums[i]] + nums[i]);
+        }
+    }
+    
+    // 查看背包容量为target时，元素总和是否等于target
+    return dp[target] == target;
+}
+```
+
+TypeScript:
+
+> 一维数组，简洁
+
+```typescript
+function canPartition(nums: number[]): boolean {
+    const sum: number = nums.reduce((pre, cur) => pre + cur);
+    if (sum % 2 === 1) return false;
+    const bagSize: number = sum / 2;
+    const goodsNum: number = nums.length;
+    const dp: number[] = new Array(bagSize + 1).fill(0);
+    for (let i = 0; i < goodsNum; i++) {
+        for (let j = bagSize; j >= nums[i]; j--) {
+            dp[j] = Math.max(dp[j], dp[j - nums[i]] + nums[i]);
+        }
+    }
+    return dp[bagSize] === bagSize;
+};
+```
+
+> 二维数组，易懂
+
+```typescript
+function canPartition(nums: number[]): boolean {
+    /**
+        weightArr = nums;
+        valueArr = nums;
+        bagSize = sum / 2; (sum为nums各元素总和);
+        按照0-1背包处理
+     */
+    const sum: number = nums.reduce((pre, cur) => pre + cur);
+    if (sum % 2 === 1) return false;
+    const bagSize: number = sum / 2;
+    const weightArr: number[] = nums;
+    const valueArr: number[] = nums;
+    const goodsNum: number = weightArr.length;
+    const dp: number[][] = new Array(goodsNum)
+    	.fill(0)
+    	.map(_ => new Array(bagSize + 1).fill(0));
+    for (let i = weightArr[0]; i <= bagSize; i++) {
+        dp[0][i] = valueArr[0];
+    }
+    for (let i = 1; i < goodsNum; i++) {
+        for (let j = 0; j <= bagSize; j++) {
+            if (j < weightArr[i]) {
+                dp[i][j] = dp[i - 1][j];
+            } else {
+                dp[i][j] = Math.max(dp[i - 1][j], dp[i - 1][j - weightArr[i]] + valueArr[i]);
+            }
+        }
+    }
+    return dp[goodsNum - 1][bagSize] === bagSize;
+};
+```
+
+
+
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