561. Array Partition I(python+cpp)

本文介绍了一种算法问题,即给定一个包含2n个整数的数组,如何将其分成n对,使得每对中较小数之和尽可能大。通过排序和选择偶数位置元素的方法,实现了最优解。附带提供了Python和C++代码实现。

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题目:

Given an array of 2n integers, your task is to group these integers
into n pairs of integer, say (a1, b1), (a2, b2), …, (an, bn) which
makes sum of min(ai, bi) for all i from 1 to n as large as possible.

Example 1:
Input: [1,4,3,2]
Output: 4
Explanation: n is 2, and the maximum sum of pairs is 4 = min(1, 2) + min(3, 4). Note: n is a positive integer, which is in the range of [1, 10000]. All the integers in the array will be in the range of [-10000, 10000].

解释:
先对列表排序,然后按顺序每次取出两个min一下求和~

python代码:

class Solution(object):
    def arrayPairSum(self, nums):
        """
        :type nums: List[int]
        :rtype: int
        """
        return sum(sorted(nums)[::2])

c++代码:

#include <algorithm>
class Solution {
public:
    int arrayPairSum(vector<int>& nums) {
        sort(nums.begin(),nums.end());
        int result=0;
        for(int i=0;i<nums.size();i+=2)
            result+=nums[i];
        return result;
            
    }
};

总结:
#include <algorithm>里面的sort()函数。

根据这段HLS代码写一段仿真测试文件:#include “SmoothProfileOnXAxisMean.h” void SmoothProfileOnXAxisMean(hls::stream& points_in_z, hls::stream& smoothed_z, ap_uint<6> mean_win_size, float invalid_z ) { // #pragma HLS DATAFLOW #pragma HLS INTERFACE axis port=points_in_z #pragma HLS INTERFACE axis port=smoothed_z #pragma HLS INTERFACE ap_none port=mean_win_size #pragma HLS INTERFACE ap_none port=invalid_z #pragma HLS INTERFACE ap_ctrl_none port=return float result = 0.0f; float mean_registers [33]; #pragma HLS ARRAY_PARTITION dim=1 type=complete variable=mean_registers // 上一周期window size static ap_uint<6> last_mean_win_size = 0; // 新周期刷新window size if (mean_win_size != last_mean_win_size) { last_mean_win_size = mean_win_size; } // 使用稳定window size ap_uint<6> stable_mean_win_size = 0; stable_mean_win_size = last_mean_win_size; int half_window = stable_mean_win_size / 2; // 初始化寄存器 for (int i = 0; i < 33; i++) { #pragma HLS UNROLL mean_registers[i] = 0.0f; } // 主循环 for (int i = 0; i < 3200 + half_window; i++) { #pragma HLS PIPELINE II=1 bool data_available = (i < 3200); // 数据读使能 float new_value = 0.0f; ap_uint<8> gray_new_value = 0; // 接收数据流 if (data_available) { new_value = points_in_z.read(); } // 数据移位寄存 for (int j = 32; j > 0; j–) { mean_registers[j] = mean_registers[j - 1]; } mean_registers[0] = data_available ? new_value : mean_registers[0]; // 如果窗口中心点是无效值,则直接输出无效值 if (mean_registers[half_window] == invalid_z) { smoothed_z.write(invalid_z); continue; } // 边界处直接输出源值 if (((i >= half_window) && (i < stable_mean_win_size - 1)) || (i >= 3200)) { smoothed_z.write(mean_registers[half_window]); continue; } // 窗口填满后开始滤波处理 else if ((i >= stable_mean_win_size - 1) && (i <= 3200)) { float sum = 0.0f; int valid_count = 0; if (stable_mean_win_size == 1) { for (int k = 0; k < 1; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } if (stable_mean_win_size == 3) { for (int k = 0; k < 3; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } if (stable_mean_win_size == 5) { for (int k = 0; k < 5; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 9) { for (int k = 0; k < 9; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 17) { for (int k = 0; k < 17; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 33) { for (int k = 0; k < 33; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } // 计算并输出均值 result = (valid_count > 0) ? sum / valid_count : invalid_z; smoothed_z.write(result); } } }
07-23
优化这段代码,改用定点,尽量不要使用for循环,而且尽量流水线设计,不阻塞:#include "SmoothProfileOnXAxisMean.h" void SmoothProfileOnXAxisMean(hls::stream<float>& points_in_z, hls::stream<float>& smoothed_z, ap_uint<6> mean_win_size, float invalid_z ) { // #pragma HLS DATAFLOW #pragma HLS INTERFACE axis port=points_in_z #pragma HLS INTERFACE axis port=smoothed_z #pragma HLS INTERFACE ap_none port=mean_win_size #pragma HLS INTERFACE ap_none port=invalid_z #pragma HLS INTERFACE ap_ctrl_none port=return float result = 0.0f; float mean_registers [33]; #pragma HLS ARRAY_PARTITION dim=1 type=complete variable=mean_registers // 上一周期window size static ap_uint<6> last_mean_win_size = 0; // 新周期刷新window size if (mean_win_size != last_mean_win_size) { last_mean_win_size = mean_win_size; } // 使用稳定window size ap_uint<6> stable_mean_win_size = 0; stable_mean_win_size = last_mean_win_size; int half_window = stable_mean_win_size / 2; // 初始化寄存器 for (int i = 0; i < 33; i++) { #pragma HLS UNROLL mean_registers[i] = 0.0f; } // 主循环 for (int i = 0; i < 3200 + half_window; i++) { #pragma HLS PIPELINE II=1 bool data_available = (i < 3200); // 数据读使能 float new_value = 0.0f; ap_uint<8> gray_new_value = 0; // 接收数据流 if (data_available) { new_value = points_in_z.read(); } // 数据移位寄存 for (int j = 32; j > 0; j--) { mean_registers[j] = mean_registers[j - 1]; } mean_registers[0] = data_available ? new_value : mean_registers[0]; // 如果窗口中心点是无效值,则直接输出无效值 if (mean_registers[half_window] == invalid_z) { smoothed_z.write(invalid_z); continue; } // 边界处直接输出源值 if (((i >= half_window) && (i < stable_mean_win_size - 1)) || (i >= 3200)) { smoothed_z.write(mean_registers[half_window]); continue; } // 窗口填满后开始滤波处理 else if ((i >= stable_mean_win_size - 1) && (i <= 3200)) { float sum = 0.0f; int valid_count = 0; if (stable_mean_win_size == 1) { for (int k = 0; k < 1; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } if (stable_mean_win_size == 3) { for (int k = 0; k < 3; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } if (stable_mean_win_size == 5) { for (int k = 0; k < 5; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 9) { for (int k = 0; k < 9; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 17) { for (int k = 0; k < 17; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } else if (stable_mean_win_size == 33) { for (int k = 0; k < 33; k++) { if (mean_registers[k] != invalid_z) { sum += mean_registers[k]; valid_count++; } } } // 计算并输出均值 result = (valid_count > 0) ? sum / valid_count : invalid_z; smoothed_z.write(result); } } }
07-26
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