272 lines
7.8 KiB
C++
272 lines
7.8 KiB
C++
#include <iostream>
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#include <iomanip>
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#include <fstream>
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#include <sstream>
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#include <algorithm>
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#include <random>
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#include <unordered_map>
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#include <omp.h>
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#include <time.h>
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#include <string>
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#include <vector>
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#include <queue>
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#include <filesystem>
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#include <thread>
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#ifdef _WIN32
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#include <io.h>
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#include <process.h>
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#define F_OK 0
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#else
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#include <unistd.h>
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#endif
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#include <mat.h>
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#include "gmm.h"
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#include "thread_pool.h"
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using namespace std;
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using std::cout;
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using std::vector;
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namespace fs = std::filesystem;
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/* 从mat文件中读取给定名称的矩阵数据，并获取矩阵的行列数值 */
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template<typename T>
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T* ReadMatlabMat(const string &filePath, const string &mtxName, int *pRowNum, int *pColNum) {
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T* dst = nullptr;
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MATFile* pMatFile = nullptr;
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mxArray* pMxArray = nullptr;
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int rowNum, colNum;
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double* matData;
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pMatFile = matOpen(filePath.c_str(), "r"); //打开.mat文件
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if (pMatFile == nullptr) {
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cerr << "filePath is error!" << endl;
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return nullptr;
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}
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pMxArray = matGetVariable(pMatFile, mtxName.c_str()); //获取.mat文件里面名为matrixName的矩阵
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rowNum = mxGetM(pMxArray);
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colNum = mxGetN(pMxArray);
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// cout << rowNum << " " << colNum << endl;
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matData = (double*)mxGetData(pMxArray); //获取指针
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dst = new T[rowNum * colNum];
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for (int i = 0; i < rowNum; ++i) {
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for (int j = 0; j < colNum; ++j) {
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dst[i * colNum + j] = T(matData[j * rowNum + i]);
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}
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}
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mxDestroyArray(pMxArray); //释放内存
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matClose(pMatFile); // 关闭文件
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*pRowNum = rowNum;
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*pColNum = colNum;
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return dst;
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}
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/* 将数据写入mat文件中，用给定的名称命名 */
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template<typename T>
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bool SaveMatrix(T* src, MATFile* pMatFile, string matrixName, int rowNum, int colNum)
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{
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//转置存储
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int datasize = colNum * rowNum;
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double* mtxData = new double[datasize];//待存储数据转为double格式
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// memset(mtxData, 0, datasize * sizeof(double));
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for (int i = 0; i < rowNum; i++)
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{
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for (int j = 0; j < colNum; j++)
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{
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mtxData[j * rowNum + i] = double(src[i * colNum + j]);
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// *(mtxData + j * rowNum + i) = (double)src[i * colNum + j]; 可消除警告
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}
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}
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mxArray* pWriteArray = NULL;//matlab格式矩阵
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if (pMatFile == nullptr)
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{
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cerr << "mat file pointer is error!" << endl;
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return false;
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}
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//创建一个rowNum*colNum的矩阵
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pWriteArray = mxCreateDoubleMatrix(rowNum, colNum, mxREAL);
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//把data的值赋给pWriteArray指针
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memcpy((void*)(mxGetPr(pWriteArray)), (void*)mtxData, sizeof(double) * datasize);
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//给矩阵命名为matrixName
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matPutVariable(pMatFile, matrixName.c_str(), pWriteArray);
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mxDestroyArray(pWriteArray);//release resource
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delete[]mtxData;//release resource
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return true;
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}
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/* 将x向量放到宽度为binWidth大小的桶中，功能类似matlab的hist*/
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void PutXtoBin(double* x, int xSize, double binWidth, vector<double>& vXBin, vector<double>& vYBin) {
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double maxX = 0.0;
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for (int i = 0; i < xSize; ++i) {
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if (maxX < x[i]) maxX = x[i];
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}
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int binSize = (int)((maxX + binWidth / 2) / binWidth + 1);
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double binMaxVal = (binSize - 1) * binWidth;
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if (binMaxVal > maxX) { // 确保最后一个bin不大于maxX，而且不小于maxX-binWidth
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binSize -= 1;
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}
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vXBin.resize(xSize);
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vYBin.resize(binSize);
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for (int i = 0; i < binSize; ++i) vYBin[i] = 0;
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for (int i = 0; i < xSize; ++i) {
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int binIdx = (int)((x[i] + binWidth / 2) / binWidth);
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if (binIdx >= binSize) binIdx = binSize - 1;
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vYBin[binIdx] += 1;
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}
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// 按大小顺序将修改后的x数值存储在vXBin中，点的顺序不同，训练出的高斯混合模型参数会有一些不同。
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int xIdx = 0;
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for (int i = 0; i < binSize; ++i) {
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for (int j = 0; j < vYBin[i]; ++j) {
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vXBin[xIdx++] = i * binWidth;
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}
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}
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}
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/* 将标准高斯模型训练出的参数转换成自定义的系数, 并返回拟合后的Y值向量 */
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struct cmpFunc {
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bool operator()(const pair<double, double>& a, const pair<double, double>& b) { return a.first < b.first; }
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};
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void GMMToFactorEY(GMM& gmm, double binWidth, vector<double> &vYBin, vector<double>& vFactor, vector<double>& vEY) {
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/* 需要调整曲线的权重，来拟合高斯曲线，而不是用概率密度 */
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double zoomFactorSum = 0.0;
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int valNum = 0;
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vEY.resize(vYBin.size());
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int topM = vYBin.size() / 4;
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if (topM < 1) topM = 1;
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/* 用堆排序的方式取前topM个最大值, 用来计算缩放参数*/
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priority_queue<pair<double, double>, vector<pair<double, double> >, cmpFunc> pqTopM;
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for (int i = 0; i < vYBin.size(); ++i) {
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double xVal = i * binWidth;
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double probVal = gmm.GetProbability(&xVal);
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vEY[i] = probVal;
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pqTopM.push(make_pair(vYBin[i], probVal));
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}
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for (int i = 0; i < topM; ++i) {
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pair<double, double> topEle = pqTopM.top();
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pqTopM.pop();
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zoomFactorSum += topEle.first / topEle.second;
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}
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double zoomFactor = zoomFactorSum / topM;
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for (int i = 0; i < vEY.size(); ++i) {
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vEY[i] *= zoomFactor;
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}
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vFactor.clear();
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vFactor.push_back(zoomFactor * gmm.Prior(0) / sqrt(2 * M_PI * *gmm.Variance(0)));
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vFactor.push_back(*gmm.Mean(0));
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vFactor.push_back(sqrt(2 * *gmm.Variance(0)));
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vFactor.push_back(zoomFactor * gmm.Prior(1) / sqrt(2 * M_PI * *gmm.Variance(1)));
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vFactor.push_back(*gmm.Mean(1));
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vFactor.push_back(sqrt(2 * *gmm.Variance(1)));
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}
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/* 计算平均数 */
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template <typename T>
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T Average(vector<T>& vVal) {
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T sumVal = T(0);
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for (int i = 0; i < vVal.size(); ++i) {
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sumVal += vVal[i];
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}
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return sumVal / vVal.size();
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}
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/* 计算平方的均值 */
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template <typename T>
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T SquareAverage(vector<T>& vVal) {
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vector<T> vSquare(vVal.size());
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for (int i = 0; i < vVal.size(); ++i) {
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vSquare[i] = vVal[i] * vVal[i];
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}
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return Average(vSquare);
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}
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/* 计算向量x和y的相关距离, 向量维度必须相等*/
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double CorrelationDistance(vector<double>& vX, vector<double>& vY) {
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vector<double> vXY(vX.size());
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for (int i = 0; i < vXY.size(); ++i) {
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vXY[i] = vX[i] * vY[i];
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}
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double uv = Average(vXY);
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double uu = SquareAverage(vX);
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double vv = SquareAverage(vY);
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double dist = 1.0 - uv / sqrt(uu * vv);
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return abs(dist);
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}
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/* 处理一个知识颗粒 */
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struct ThreadParam {
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fs::path matFilePath;
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fs::path outFilePath;
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};
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void ThreadProcessData(const ThreadParam& param) {
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const fs::path& matFilePath = param.matFilePath;
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const fs::path& outFilePath = param.outFilePath;
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double* hs = nullptr;
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int rowNum = 0;
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int colNum = 0;
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hs = ReadMatlabMat<double>(matFilePath.string(), "hs", &rowNum, &colNum);
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vector<double>vXBin;
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vector<double>vYBin;
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vector<double>vEY;
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vector<double>vFactor;
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/* 用来保存数据，存入mat文件 */
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vector<double>vDist(rowNum);
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vector<double>vFactorAll;
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for (int i = 0; i < rowNum; ++i) {
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PutXtoBin(hs + i * colNum, colNum, 0.2, vXBin, vYBin);
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GMM gmm(1, 2); // 1维， 2个高斯模型
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gmm.Train(vXBin.data(), vXBin.size());
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GMMToFactorEY(gmm, 0.2, vYBin, vFactor, vEY);
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vDist[i] = CorrelationDistance(vYBin, vEY);
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vFactorAll.insert(vFactorAll.end(), vFactor.begin(), vFactor.end());
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}
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/* 写入matlab文件 */
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MATFile* pMatFile = matOpen(outFilePath.string().c_str(), "w");
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SaveMatrix<double>(vFactorAll.data(), pMatFile, "factor", rowNum, 6);
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SaveMatrix<double>(vDist.data(), pMatFile, "correlation", rowNum, 1);
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matClose(pMatFile);
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delete[] hs;
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}
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/* 程序入口 */
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int main(int argc, char** argv) {
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if (argc != 5) {
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cerr << "This program should take 4 arguments(1.parrent Dir; 2. mat file suffix; 3. out mat filename; 4. thread number)!" << endl;
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return 1;
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}
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string parrentDir(argv[1]); // 知识颗粒的父目录名称
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string hsMatSuffix(argv[2]); // hs矩阵对应的mat文件的后缀名（可以是全文件名，可以是文件名后缀，必须保证唯一）
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fs::path outFileName(argv[3]);
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ThreadPool thPool(8);
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clock_t begin, finish;
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begin = clock();
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/* 遍历所有的知识颗粒目录，逐一进行处理 */
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for (auto& childDir : fs::directory_iterator(parrentDir)) {
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fs::path outFilePath = childDir / outFileName;
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for (auto& file : fs::directory_iterator(childDir)) {
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const string& fileName = file.path().filename().string();
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auto rPos = fileName.rfind(hsMatSuffix);
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if (rPos != string::npos && fileName.size() - rPos == hsMatSuffix.size()) {
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ThreadParam tParam = { file, outFilePath };
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thPool.enqueue(ThreadProcessData, tParam);
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}
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}
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}
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thPool.~ThreadPool();
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finish = clock();
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cout << "Total time:" << (double)(finish - begin) / CLOCKS_PER_SEC << endl;
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return 0;
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} |