237 lines
8.6 KiB
C++
237 lines
8.6 KiB
C++
#include <mex.h>
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#include <mat.h>
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#include <iostream>
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#include <algorithm>
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#include <vector>
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#include <string>
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#include <unordered_set>
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#include <ctime>
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using std::cout;
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using std::endl;
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using namespace std;
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#define STRING_BUF_SIZE 204800
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/* 读取二层cell包裹的字符串,和数值，ds,fr */
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#define OUTER_FOR_BEGIN \
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rowNum = (int)mxGetM(pMxArray); \
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colNum = (int)mxGetN(pMxArray); \
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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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mxArray* pCell = mxGetCell(pMxArray, j * rowNum + i); \
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int childRowNum = (int)mxGetM(pCell); \
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int childColNum = (int)mxGetN(pCell);
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#define OUTER_FOR_END \
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} \
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}
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#define INNTER_FOR_BEGIN \
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for (int ii = 0; ii < childRowNum; ii++) { \
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for (int jj = 0; jj < childColNum; jj++) { \
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mxArray *pChildCell = mxGetCell(pCell, jj * childRowNum + ii);
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#define INNTER_FOR_END \
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} \
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}
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// 将matlab存储方式转换成c存储方式
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#define TRANS_ROW_COL(dst, src, rowNum, colNum) \
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for (int rowI = 0; rowI < rowNum; ++rowI) { \
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for (int colJ = 0; colJ < colNum; ++colJ) { \
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dst[rowI * colNum + colJ] = src[colJ * rowNum + rowI]; \
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} \
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}
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// 将二维索引转成一维的索引
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inline int Get1DIndex(int colNum, int row, int col) {
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return row * colNum + col;
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}
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// 读取G结构体中的ds和fr
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void GetFrDs(const mxArray* pMxParent, vector<vector<string> >& vvDs, vector<vector<double> >& vvFr) {
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// 读取ds字符串
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int rowNum, colNum;
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char *strBuf = new char[STRING_BUF_SIZE];
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mxArray* pMxArray = mxGetField(pMxParent, 0, "ds"); // ds
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OUTER_FOR_BEGIN
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vvDs.push_back(vector<string>());
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vvDs.back().resize(childRowNum * childColNum);
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INNTER_FOR_BEGIN
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if (mxGetString(pChildCell, strBuf, STRING_BUF_SIZE) != 0) {
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cout << "String is too large to fit in the buffer! " << i + 1 << '\t' << j + 1 << endl;
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delete[]strBuf;
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return;
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}
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vvDs.back()[ii * childColNum + jj] = strBuf;
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auto& lastStr = vvDs.back()[ii * childColNum + jj];
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transform(lastStr.begin(), lastStr.end(), lastStr.begin(), ::toupper); // 转成大写
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INNTER_FOR_END
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OUTER_FOR_END
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// 读取fr数值
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pMxArray = mxGetField(pMxParent, 0, "fr"); // fr
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OUTER_FOR_BEGIN
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vvFr.push_back(vector<double>());
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vvFr.back().resize(childRowNum * childColNum);
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double* pVal = (double*)mxGetData(pCell); //获取指针
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TRANS_ROW_COL(vvFr.back(), pVal, childRowNum, childColNum); // 行列存储方式转换
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OUTER_FOR_END
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delete[]strBuf;
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}
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/* 读取abs */
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void GetAbstract(const mxArray* pMxAbs, vector<string>& vAbs) {
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int rowNum = (int)mxGetM(pMxAbs);
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int colNum = (int)mxGetN(pMxAbs);
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char *strBuf = new char[STRING_BUF_SIZE];
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vAbs.resize(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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mxArray* pCell = mxGetCell(pMxAbs, j * rowNum + i);
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if (mxGetString(pCell, strBuf, STRING_BUF_SIZE) != 0) {
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cout << "String is too large to fit in the buffer! " << i + 1 << '\t' << j + 1 << endl;
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delete[]strBuf;
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return;
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}
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vAbs[i * colNum + j] = strBuf;
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}
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}
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delete[]strBuf;
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}
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/*
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nlhs：输出参数数目(Number Left - hand side)，等号左边
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plhs：指向输出参数的指针(Point Left - hand side)，等号左边
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nrhs：输入参数数目(Number Right - hand side)，等号右边
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prhs：指向输入参数的指针(Point Right - hand side)，等号右边。要注意prhs是const的指针数组，即不能改变其指向内容。
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*/
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void mexFunction(int nlhs, mxArray* plhs[], int nrhs, const mxArray* prhs[]) {
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//cout << "MexCalcEntropy" << endl;
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//cout << nlhs << '\t' << nrhs << endl;
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if (nrhs != 2) {
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cout << "2 arguments should be given for this function!" << endl;
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return;
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}
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clock_t begin, finish;
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begin = clock();
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vector<vector<string> > vvDs; // 每个知识颗粒的ds矩阵（词汇矩阵）
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vector<vector<double> > vvFr; // 词汇对应的频率
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GetFrDs(prhs[1], vvDs, vvFr);
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vector<string> vAbstract; // 读取abs1, 然后分割成一个一个的单词
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GetAbstract(prhs[0], vAbstract);
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/* 将摘要信息分割成一个一个的词汇 */
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// begin = clock();
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unordered_set<char> usWordChars; // 能组成单词的字符，要不要考虑数字？原版matlab是提取了数字的
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for (int i = 65; i <= 90; i++) usWordChars.insert(char(i)); // A - Z
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for (int i = 97; i <= 122; i++) usWordChars.insert(char(i)); // a - z
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for (int i = 48; i <= 57; i++) usWordChars.insert(char(i)); // 0 - 9
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usWordChars.insert('/'); usWordChars.insert('+'); usWordChars.insert('-');
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vector<vector<string> > vvWordMtx(vAbstract.size()); // 初始大小为文章的个数
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vector<unordered_set<string> > vusAbsWord(vAbstract.size()); // 将每篇文章摘要的单词放入hash表
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for (int i = 0; i < vAbstract.size(); i++) {
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auto& strAbs = vAbstract[i];
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// 遍历摘要字符串的每一个字符，取出每一个单词
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vector<string>& vWord = vvWordMtx[i];
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if (strAbs.size() == 0) continue; // 摘要信息为空，跳过（一般不会出现这个情况）
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int wordStartPos = 0;
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while (wordStartPos < strAbs.size() && usWordChars.find(strAbs[wordStartPos]) == usWordChars.end())
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wordStartPos++;
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for (int curPos = wordStartPos + 1; curPos < strAbs.size(); ++curPos) {
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if (usWordChars.find(strAbs[curPos]) == usWordChars.end()) { // 找到了分割符
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vWord.push_back(strAbs.substr(wordStartPos, curPos - wordStartPos));
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wordStartPos = curPos + 1; // 找下一个词语起始位置
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while (wordStartPos < strAbs.size() && usWordChars.find(strAbs[wordStartPos]) == usWordChars.end())
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wordStartPos++;
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curPos = wordStartPos; // 循环会自动加1
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}
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}
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// 将处理摘要之后的每个词语放入hash表
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for (auto& word : vWord) {
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string upWord(word);
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transform(upWord.begin(), upWord.end(), upWord.begin(), ::toupper);
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vusAbsWord[i].insert(upWord);
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}
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}
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// finish = clock();
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// cout << "Split abstract time: " << (double)(finish - begin) / CLOCKS_PER_SEC << " s" << endl;
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// 存放结果，用一维数组存放二维数据
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vector<double> hs;
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vector<double> hr;
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const int numLiterature = vusAbsWord.size(); // pubmed 文件中包含的文献数量
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const int numGroup = vvDs.size(); // ds包含的组数
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hs.resize(numGroup * numLiterature);
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hr.resize(numLiterature * numGroup);
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for (int groupIdx = 0; groupIdx < numGroup; ++groupIdx) { // 遍历知识颗粒中的每一组
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vector<string>& vDs = vvDs[groupIdx]; // 这一组ds
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vector<double>& vFr = vvFr[groupIdx]; // frequency
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const int numWord = vDs.size(); // 这一组数据中包含的单词数量
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vector<vector<int> > vX(numLiterature, vector<int>(numWord, 0));
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// 检查知识颗粒中的词语是否出现在pubmed摘要的词语中
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for (int i = 0; i < numLiterature; ++i) {
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for (int j = 0; j < numWord; ++j) {
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if (vusAbsWord[i].find(vDs[j]) != vusAbsWord[i].end()) { // 这一组单词中的j索引位置的单词在第i个文献中出现过
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vX[i][j] = 1;
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}
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}
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}
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// 找词汇的最高频率
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double maxFr = *max_element(vFr.begin(), vFr.end());
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// 将fr的数值规范化到（0，0.368）之间
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const double normalMax = 0.368;
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for (auto& frVal : vFr) frVal = frVal * normalMax / maxFr;
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maxFr = normalMax;
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// 对每个知识颗粒每一组数据，计算信息熵
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for (int i = 0; i < numLiterature; ++i) {
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for (int j = 0; j < numWord; ++j) {
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if (vX[i][j] == 1) {
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hs[Get1DIndex(numLiterature, groupIdx, i)] -= vFr[j] * log2(vFr[j]);
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}
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}
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}
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// 找最高频词汇所在的索引位置
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vector<int> vMaxPos;
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int idx = 0;
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for_each(vFr.begin(), vFr.end(), [&idx, maxFr, &vMaxPos](double val) {
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if (val == maxFr) vMaxPos.push_back(idx);
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idx++;
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});
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for (int i = 0; i < numLiterature; ++i) {
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int cumulateX = 0; // 计算在最高频词汇处，x值的累加结果
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for (int j = 0; j < vMaxPos.size(); ++j) cumulateX += vX[i][vMaxPos[j]];
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if (cumulateX == vMaxPos.size()) { // 如果频率最高的词汇都出现在了文献中
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hr[Get1DIndex(numGroup, i, groupIdx)] = 1; // 应该是表示知识颗粒的这一组数据跟这篇文献相关性比较高
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}
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}
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}
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/* 将结果写入返回值 */
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if (nlhs > 0) {
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int datasize = numGroup * numLiterature;
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double* mtxData = new double[datasize];//待存储数据转为double格式
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mxArray* pWriteArray = NULL;//matlab格式矩阵
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//创建一个rowNum*colNum的矩阵
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pWriteArray = mxCreateDoubleMatrix(numGroup, numLiterature, mxREAL);
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for (int i = 0; i < numGroup; i++) {
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for (int j = 0; j < numLiterature; j++) {
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mtxData[j * numGroup + i] = hs[i * numLiterature + j];
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}
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}
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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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plhs[0] = pWriteArray; // 赋值给返回值
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delete[]mtxData;
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}
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finish = clock();
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// cout << "CalcEntropy Total time: " << (double)(finish - begin) / CLOCKS_PER_SEC << " s" << endl;
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}
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/* 供main调试调用 */
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void MexCalcEntropy(int nlhs, mxArray* plhs[], int nrhs, const mxArray* prhs[]) {
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mexFunction(nlhs, plhs, nrhs, prhs);
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} |