KNN演算法的實現

Knn.h#pragma onceclass Knn{private: double** trainingDataset; double* arithmeticMean; double* standardDeviation; int m, n; void RescaleDistance(double* row); void RescaleTrainingDataset(); void ComputeArithmeticMean(); void ComputeStandardDeviation(); double Distance(double* x, double* y);public: Knn(double** trainingDataset, int m, int n); ~Knn(); double Vote(double* test, int k);}; Knn.cpp #include "Knn.h"#include #include using namespace std;Knn::Knn(double** trainingDataset, int m, int n){ this->trainingDataset = trainingDataset; this->m = m; this->n = n; ComputeArithmeticMean(); ComputeStandardDeviation(); RescaleTrainingDataset();}void Knn::ComputeArithmeticMean(){ arithmeticMean = new double[n - 1]; double sum; for(int i = 0; i ::iterator max; map mins; for(int i = 0; i ::value_type(i, distance));  else  {   max = mins.begin();   for(map::iterator it = mins.begin(); it != mins.end(); it++)   {    if(it->second > max->second)     max = it;   }   if(distance second)   {    mins.erase(max);    mins.insert(map::value_type(i, distance));   }  } } map votes; double temp; for(map::iterator it = mins.begin(); it != mins.end(); it++) {  temp = trainingDataset[it->first][n-1];  map::iterator voteIt = votes.find(temp);  if(voteIt != votes.end())   voteIt->second ++;  else   votes.insert(map::value_type(temp, 1)); } map::iterator maxVote = votes.begin(); for(map::iterator it = votes.begin(); it != votes.end(); it++) {  if(it->second > maxVote->second)   maxVote = it; } test[n-1] = maxVote->first; return maxVote->first;} main.cpp #include #include "Knn.h"using namespace std;int main(const int& argc, const char* argv[]){ double** train = new double* [14]; for(int i = 0; i