分割(partition,stable_partition)

本文深入探讨了C++标准模板库中的partition和stable_partition算法,详细解释了这两种算法如何将数组元素根据指定条件分为两部分,以及它们之间的区别。通过实例展示了stable_partition的使用方法,强调了其在保持元素相对顺序方面的优势。

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template <class ForwardIterator, class UnaryPredicate>
  ForwardIterator partition (ForwardIterator first,
                             ForwardIterator last, UnaryPredicate pred);
template <class ForwardIterator, class UnaryPredicate>
  ForwardIterator stable_partition (ForwardIterator first,
                             ForwardIterator last, UnaryPredicate pred);
  1. 将数组中的元素分为两部分,第一部分[first,middle)中的每个元素都是pred(i)为true,第二部分[middle,last)中的每个元素都是pred(i)为false
  2. 返回值为middle
  3. partition执行速度很快,除非要求稳定性使用stable_partition,否则使用partition
  4. partition不保证与原来的数组中元素相对顺序相同,sable_partition保证
  5. stable_partition是一个adpative算法,会试图分配内存缓冲区,其运行复杂度取决于缓冲区中有多少内存,最坏情况下没有分配任何内存,至多调用swap Nlog(N)次,N为last-first,最好情况下(缓冲区中有足够内存)与N呈线性关系,两种情况下pred都被调用N次

    #include <iostream>
    #include <vector>
    #include <algorithm>
    #include <iterator>
    #include <functional>
    using namespace std;
    
    class F
    {
        public:
            bool operator()(int i)
            {
                return (i&1)==0;    
            }    
    };
    int main()
    {
        vector<int> v{1,2,3,4,5,6};
        auto it=stable_partition(v.begin(),v.end(),F());
        for_each(v.begin(),v.end(),[](int i)
        {
            cout<<i<<" ";
        });
        cout<<endl;
        return 0;
    }

     

转载于:https://www.cnblogs.com/tianzeng/p/10386097.html

优化一下这段代码,减少LUT的使用,并且减少for循环的出现,点数平滑可设置: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); } } }
最新发布
07-23
分析一下这段HLS代码:#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); } } }
07-23
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