基於距離的離羣點檢測算法C++實現

參考博客：參考

算法簡介： 該算法比較簡單，需要人爲設置兩個閾值，一個是距離閾值distance，一個是分數閾值score。算法僞代碼如下：

 

C++實現（支持多維數據，會對數據進行歸一化操作，歸一化參考）：

#include <iostream>
#include <vector>
#include <cmath>

// support multidimensional data
std::vector<double> OutlierDetect(double* src, int src_rows, int src_cols) {
  // save data to vector
  std::vector<double> vec_src;
  for(int i = 0; i < src_rows; ++i) {
    int shift = i * src_cols;
    for(int j = 0; j < src_cols; ++j) {
      vec_src.push_back(src[shift + j]);
    }
  }

  // get the max and the min in each col
  std::vector<double> vec_max(src_cols, -1000);
  std::vector<double> vec_min(src_cols, 1000);
  for(int i = 0; i < src_cols; ++i) {
    for(int j = 0; j < src_rows; ++j) {
      int shift = j * src_cols;
      if(vec_src[shift + i] > vec_max[i]) {
        vec_max[i] = vec_src[shift + i];
      }
      if(vec_src[shift + i] < vec_min[i]) {
        vec_min[i] = vec_src[shift + i];
      } // end if
    } // end for loop
  } // end for loop

  // normalized: min-max
  for(int i = 0; i < src_cols; ++i) {
    for(int j = 0; j < src_rows; ++j) {
      int shift = j * src_cols;
      vec_src[shift + i] =
        (vec_src[shift + i] - vec_min[i]) / (vec_max[i] - vec_min[i]);
    } // end if
  } // end for loop

/**********************distance-based outlier detection********************/
  double distance = 0.01;
  double score = 0.5;              // 0 < score < 1
  std::vector<double> outliers;
  std::vector<int> outliers_index;
  std::vector<double> inliers;
  std::vector<int> inliers_index;

  int count_temp;
  int shift_i;
  int shift_j;
  for(int i = 0; i < src_rows; ++i) {
    count_temp = 0;
    for(int j = 0; j < src_rows; ++j) {
      if(i == j) {
        continue;
      }
      shift_i = i * src_cols;
      shift_j = j * src_cols;
      double dist = 0;
      for(int k = 0; k < src_cols; ++k) {
        double dist_temp = vec_src[shift_i + k] - vec_src[shift_j + k];
        dist += pow(dist_temp, 2);                      // Euclidean distance
      }
      if(sqrt(dist) < distance) {
        count_temp++;
      }
    }

    // save outliers and its index
    if(count_temp < score * src_rows) {
      outliers_index.push_back(i);
      for(int l = 0; l < src_cols; ++l) {
        outliers.push_back(vec_src[shift_i + l]);
      }
    }
    else {
      inliers_index.push_back(i);
      for(int l = 0; l < src_cols; ++l) {
        inliers.push_back(vec_src[shift_i + l]);
      }
    }
  } // end for loop
/**********************distance-based outlier detection********************/

  return inliers;
//  return outliers;
}

代碼未經驗證，僅供參考。