// Returns the amount of available memory. UnsT getAvailableMemory(){ // TODO //printf("mem=%lu\n", MEMORY_MAX_AVAILABLE - totalAllocatedMemory); FAILIFWR(availableTotalMemory < totalAllocatedMemory, "Not enough memory.\n"); return availableTotalMemory - totalAllocatedMemory; }
/* The main entry to LSH package. Depending on the command line parameters, the function computes the R-NN data structure optimal parameters and/or construct the R-NN data structure and runs the queries on the data structure. */ int main(int nargs, char **args){ if(nargs < 9){ usage(args[0]); exit(1); } //initializeLSHGlobal(); // Parse part of the command-line parameters. nPoints = atoi(args[1]); IntT nQueries = atoi(args[2]); pointsDimension = atoi(args[3]); successProbability = atof(args[4]); char* endPtr[1]; RealT thresholdR = strtod(args[5], endPtr); if (thresholdR == 0 || endPtr[1] == args[5]){ // The value for R is not specified, instead there is a file // specifying multiple R's. thresholdR = 0; // Read in the file FILE *radiiFile = fopen(args[5], "rt"); FAILIF(radiiFile == NULL); fscanf(radiiFile, "%d\n", &nRadii); ASSERT(nRadii > 0); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++){ FSCANF_REAL(radiiFile, &listOfRadii[i]); ASSERT(listOfRadii[i] > 0); FSCANF_REAL(radiiFile, &memRatiosForNNStructs[i]); ASSERT(memRatiosForNNStructs[i] > 0); } }else{ nRadii = 1; FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); listOfRadii[0] = thresholdR; memRatiosForNNStructs[0] = 1; } DPRINTF("No. radii: %d\n", nRadii); //thresholdR = atof(args[5]); availableTotalMemory = atoll(args[8]); if (nPoints > MAX_N_POINTS) { // 104w points printf("Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); fprintf(ERROR_OUTPUT, "Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); exit(1); } readDataSetFromFile(args[6]); // read points into data structure DPRINTF("Allocated memory (after reading data set): %lld\n", totalAllocatedMemory); Int32T nSampleQueries = N_SAMPLE_QUERY_POINTS; PPointT sampleQueries[nSampleQueries]; Int32T sampleQBoundaryIndeces[nSampleQueries]; if ((nargs < 9) || (strcmp("-c", args[9]) == 0)){ // In this cases, we need to generate a sample query set for // computing the optimal parameters. // Generate a sample query set. FILE *queryFile = fopen(args[7], "rt"); if (strcmp(args[7], ".") == 0 || queryFile == NULL || nQueries <= 0){ // Choose several data set points for the sample query points. for(IntT i = 0; i < nSampleQueries; i++){ sampleQueries[i] = dataSetPoints[genRandomInt(0, nPoints - 1)]; } }else{ // Choose several actual query points for the sample query points. nSampleQueries = MIN(nSampleQueries, nQueries); Int32T sampleIndeces[nSampleQueries]; for(IntT i = 0; i < nSampleQueries; i++){ sampleIndeces[i] = genRandomInt(0, nQueries - 1); } qsort(sampleIndeces, nSampleQueries, sizeof(*sampleIndeces), compareInt32T); //printIntVector("sampleIndeces: ", nSampleQueries, sampleIndeces); Int32T j = 0; for(Int32T i = 0; i < nQueries; i++){ if (i == sampleIndeces[j]){ sampleQueries[j] = readPoint(queryFile); j++; while (i == sampleIndeces[j]){ sampleQueries[j] = sampleQueries[j - 1]; j++; } }else{ fscanf(queryFile, "%[^\n]", sBuffer); fscanf(queryFile, "\n"); } } nSampleQueries = j; fclose(queryFile); } // Compute the array sampleQBoundaryIndeces that specifies how to // segregate the sample query points according to their distance // to NN. sortQueryPointsByRadii(pointsDimension, nSampleQueries, sampleQueries, nPoints, dataSetPoints, nRadii, listOfRadii, sampleQBoundaryIndeces); } RNNParametersT *algParameters = NULL; PRNearNeighborStructT *nnStructs = NULL; if (nargs > 9) { // Additional command-line parameter is specified. if (strcmp("-c", args[9]) == 0) { // Only compute the R-NN DS parameters and output them to stdout. printf("%d\n", nRadii); transformMemRatios(); for(IntT i = 0; i < nRadii; i++){ // which sample queries to use Int32T segregatedQStart = (i == 0) ? 0 : sampleQBoundaryIndeces[i - 1]; Int32T segregatedQNumber = nSampleQueries - segregatedQStart; if (segregatedQNumber == 0) { // XXX: not the right answer segregatedQNumber = nSampleQueries; segregatedQStart = 0; } ASSERT(segregatedQStart < nSampleQueries); ASSERT(segregatedQStart >= 0); ASSERT(segregatedQStart + segregatedQNumber <= nSampleQueries); ASSERT(segregatedQNumber >= 0); RNNParametersT optParameters = computeOptimalParameters(listOfRadii[i], successProbability, nPoints, pointsDimension, dataSetPoints, segregatedQNumber, sampleQueries + segregatedQStart, (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); printRNNParameters(stdout, optParameters); } exit(0); } else if (strcmp("-p", args[9]) == 0) { // Read the R-NN DS parameters from the given file and run the // queries on the constructed data structure. if (nargs < 10){ usage(args[0]); exit(1); } FILE *pFile = fopen(args[10], "rt"); FAILIFWR(pFile == NULL, "Could not open the params file."); fscanf(pFile, "%d\n", &nRadii); DPRINTF1("Using the following R-NN DS parameters:\n"); DPRINTF("N radii = %d\n", nRadii); FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); FAILIF(NULL == (algParameters = (RNNParametersT*)MALLOC(nRadii * sizeof(RNNParametersT)))); for(IntT i = 0; i < nRadii; i++){ algParameters[i] = readRNNParameters(pFile); printRNNParameters(stderr, algParameters[i]); nnStructs[i] = initLSH_WithDataSet(algParameters[i], nPoints, dataSetPoints); } pointsDimension = algParameters[0].dimension; FREE(listOfRadii); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++){ listOfRadii[i] = algParameters[i].parameterR; } } else{ // Wrong option. usage(args[0]); exit(1); } } else { FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); // Determine the R-NN DS parameters, construct the DS and run the queries. transformMemRatios(); for(IntT i = 0; i < nRadii; i++){ // XXX: segregate the sample queries... nnStructs[i] = initSelfTunedRNearNeighborWithDataSet(listOfRadii[i], successProbability, nPoints, pointsDimension, dataSetPoints, nSampleQueries, sampleQueries, (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); } } DPRINTF1("X\n"); IntT resultSize = nPoints; PPointT *result = (PPointT*)MALLOC(resultSize * sizeof(*result)); PPointT queryPoint; FAILIF(NULL == (queryPoint = (PPointT)MALLOC(sizeof(PointT)))); FAILIF(NULL == (queryPoint->coordinates = (RealT*)MALLOC(pointsDimension * sizeof(RealT)))); FILE *queryFile = fopen(args[7], "rt"); FAILIF(queryFile == NULL); TimeVarT meanQueryTime = 0; PPointAndRealTStructT *distToNN = NULL; for(IntT i = 0; i < nQueries; i++){ RealT sqrLength = 0; // read in the query point. for(IntT d = 0; d < pointsDimension; d++){ FSCANF_REAL(queryFile, &(queryPoint->coordinates[d])); sqrLength += SQR(queryPoint->coordinates[d]); } queryPoint->sqrLength = sqrLength; //printRealVector("Query: ", pointsDimension, queryPoint->coordinates); // get the near neighbors. IntT nNNs = 0; for(IntT r = 0; r < nRadii; r++){ nNNs = getRNearNeighbors(nnStructs[r], queryPoint, result, resultSize); printf("Total time for R-NN query at radius %0.6lf (radius no. %d):\t%0.6lf\n", (double)(listOfRadii[r]), r, timeRNNQuery); meanQueryTime += timeRNNQuery; if (nNNs > 0){ printf("Query point %d: found %d NNs at distance %0.6lf (%dth radius). First %d NNs are:\n", i, nNNs, (double)(listOfRadii[r]), r, MIN(nNNs, MAX_REPORTED_POINTS)); // compute the distances to the found NN, and sort according to the distance FAILIF(NULL == (distToNN = (PPointAndRealTStructT*)REALLOC(distToNN, nNNs * sizeof(*distToNN)))); for(IntT p = 0; p < nNNs; p++){ distToNN[p].ppoint = result[p]; distToNN[p].real = distance(pointsDimension, queryPoint, result[p]); } qsort(distToNN, nNNs, sizeof(*distToNN), comparePPointAndRealTStructT); // Print the points for(IntT j = 0; j < MIN(nNNs, MAX_REPORTED_POINTS); j++){ ASSERT(distToNN[j].ppoint != NULL); printf("%09d\tDistance:%0.6lf\n", distToNN[j].ppoint->index, distToNN[j].real); CR_ASSERT(distToNN[j].real <= listOfRadii[r]); //DPRINTF("Distance: %lf\n", distance(pointsDimension, queryPoint, result[j])); //printRealVector("NN: ", pointsDimension, result[j]->coordinates); } break; } } if (nNNs == 0){ printf("Query point %d: no NNs found.\n", i); } } if (nQueries > 0){ meanQueryTime = meanQueryTime / nQueries; printf("Mean query time: %0.6lf\n", (double)meanQueryTime); } for(IntT i = 0; i < nRadii; i++){ freePRNearNeighborStruct(nnStructs[i]); } // XXX: should ideally free the other stuff as well. return 0; }
/* The main entry to LSH package. Depending on the command line parameters, the function computes the R-NN data structure optimal parameters and/or construct the R-NN data structure and runs the queries on the data structure. */ int main_T(int nargs, char **args) { //先分析参数 /* 官方lsh文件:10个参数 1000 9 784 0.9 0.6 mnist1k.dts mnist1k.q bin/LSHMain $nDataSet $nQuerySet $dimension $successProbability "$1" "$2" "$3" $m -c*/ //算参数 bin/LSHMain 1000 9 784 0.9 "0.6" "mnist1k.dts" "mnist1k.q" 1002000000 -c //bin/LSHMain $nDataSet $nQuerySet $dimension $successProbability 1.0 "$1" "$2" $m -p "$3" //匹配 bin/LSHMain 1000 9 784 0.9 1.0 "mnist1k.dts" "mnist1k.q" 1002000000 -p "outputparma.txt" if(nargs < 9) { usage(args[0]); exit(1); } //initializeLSHGlobal(); // Parse part of the command-line parameters. nPoints = atoi(args[1]); IntT nQueries = atoi(args[2]); pointsDimension = atoi(args[3]); successProbability = atof(args[4]); char* endPtr[1]; RealT thresholdR = strtod(args[5], endPtr);//点相邻的距离阈值 //str-to -double 将字符串转换成浮点数的函数 //endPtr 接收数字结尾后非字符串字母 //这个r阈值是什么呢? if (thresholdR == 0 || endPtr[1] == args[5]) {//如果阈值为0,或者第一个字符就不是数字, //表示是用文件保存的 //这大概是用于测试哪个阈值好的 // The value for R is not specified, instead there is a file // specifying multiple R's. thresholdR = 0; // Read in the file FILE *radiiFile = fopen(args[5], "rt"); FAILIF(radiiFile == NULL); fscanf(radiiFile, "%d\n", &nRadii); ASSERT(nRadii > 0); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++) { FSCANF_REAL(radiiFile, &listOfRadii[i]); ASSERT(listOfRadii[i] > 0); FSCANF_REAL(radiiFile, &memRatiosForNNStructs[i]); ASSERT(memRatiosForNNStructs[i] > 0); } } else { nRadii = 1; FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); listOfRadii[0] = thresholdR; memRatiosForNNStructs[0] = 1; }//对阈值R 和Radiii的处理 DPRINTF("No. radii: %d\n", nRadii); //thresholdR = atof(args[5]); availableTotalMemory = atoll(args[8]);//$M表示的是内存空间大小 if (nPoints > MAX_N_POINTS) { printf("Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); fprintf(ERROR_OUTPUT, "Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); exit(1); } readDataSetFromFile(args[6]);//点读到dataSetPoints //这个totalAllocatedMemory初始化为0,但是 //#define MALLOC(amount) ((amount > 0) ? totalAllocatedMemory += amount, malloc(amount) : NULL) //这样,每次申请内存都会统计到了 DPRINTF("Allocated memory (after reading data set): %lld\n", totalAllocatedMemory); Int32T nSampleQueries = N_SAMPLE_QUERY_POINTS; PPointT sampleQueries[N_SAMPLE_QUERY_POINTS]; Int32T sampleQBoundaryIndeces[N_SAMPLE_QUERY_POINTS]; // PPointT sampleQueries[nSampleQueries]; // Int32T sampleQBoundaryIndeces[nSampleQueries]; if ((nargs <= 9) || (strcmp("-c", args[9]) == 0) ) { // In this cases, we need to generate a sample query set for // computing the optimal parameters. // Generate a sample query set. FILE *queryFile = fopen(args[7], "rt"); if (strcmp(args[7], ".") == 0 || queryFile == NULL || nQueries <= 0) {//没有查询文件,就用所有点产生随机点 // Choose several data set points for the sample query points. for(IntT i = 0; i < nSampleQueries; i++){ sampleQueries[i] = dataSetPoints[genRandomInt(0, nPoints - 1)]; } } else { //从查询文件中选取随机的点, // Choose several actual query points for the sample query points. nSampleQueries = MIN(nSampleQueries, nQueries); Int32T sampleIndeces[N_SAMPLE_QUERY_POINTS]; //Int32T sampleIndeces[nSampleQueries]; for(IntT i = 0; i < nSampleQueries; i++) { sampleIndeces[i] = genRandomInt(0, nQueries - 1); } qsort(sampleIndeces, nSampleQueries, sizeof(*sampleIndeces), compareInt32T); //printIntVector("sampleIndeces: ", nSampleQueries, sampleIndeces); Int32T j = 0; for(Int32T i = 0; i < nQueries; i++) { if (i == sampleIndeces[j]) { sampleQueries[j] = readPoint(queryFile); j++; while (i == sampleIndeces[j]) { sampleQueries[j] = sampleQueries[j - 1]; j++; } }else { fscanf(queryFile, "%[^\n]", sBuffer); fscanf(queryFile, "\n"); } } nSampleQueries = j; fclose(queryFile); } //前面那么多,好像就是在申请内存,读文件,读入参数 // Compute the array sampleQBoundaryIndeces that specifies how to // segregate the sample query points according to their distance // to NN. //采用遍历的方法,计算查询点的最近邻(并且距离小于listOfRadii【nRadii】) sortQueryPointsByRadii(pointsDimension, nSampleQueries, sampleQueries, nPoints, dataSetPoints, nRadii, listOfRadii, sampleQBoundaryIndeces); }//if ((nargs < 9) || (strcmp("-c", args[9]) == 0)) RNNParametersT *algParameters = NULL; PRNearNeighborStructT *nnStructs = NULL; if (nargs > 9) {/* 官方lsh文件:10个参数 bin/LSHMain $nDataSet $nQuerySet $dimension $successProbability "$1" "$2" "$3" $m -c */ // Additional command-line parameter is specified. if (strcmp("-c", args[9]) == 0) //-c表示参数优化 { // Only compute the R-NN DS parameters and output them to stdout. printf("%d\n", nRadii); transformMemRatios(); for(IntT i = 0; i < nRadii; i++) { // which sample queries to use Int32T segregatedQStart = (i == 0) ? 0 : sampleQBoundaryIndeces[i - 1]; Int32T segregatedQNumber = nSampleQueries - segregatedQStart; if (segregatedQNumber == 0) { // XXX: not the right answer segregatedQNumber = nSampleQueries; segregatedQStart = 0; } ASSERT(segregatedQStart < nSampleQueries); ASSERT(segregatedQStart >= 0); ASSERT(segregatedQStart + segregatedQNumber <= nSampleQueries); ASSERT(segregatedQNumber >= 0); //从文件读取点,然后计算优化后的参数 RNNParametersT optParameters = computeOptimalParameters(listOfRadii[i], successProbability, nPoints, pointsDimension, dataSetPoints, segregatedQNumber, sampleQueries + segregatedQStart, /*对内存的约束,就体现在这里, availableTotalMemory总共的内存(传入) - totalAllocatedMemory(使用mallloc分配的)*1=内存上限 然后(L * nPoints > memoryUpperBound / 12 来约束 */ (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); printRNNParameters(stdout, optParameters); } exit(0); } else if (strcmp("-p", args[9]) == 0) {//-p表示从文件读入参数,然后建立结构体 // Read the R-NN DS parameters from the given file and run the // queries on the constructed data structure. if (nargs < 10) { usage(args[0]); exit(1); } FILE *pFile = fopen(args[10], "rt"); FAILIFWR(pFile == NULL, "Could not open the params file."); fscanf(pFile, "%d\n", &nRadii); DPRINTF1("Using the following R-NN DS parameters:\n"); DPRINTF("N radii = %d\n", nRadii); FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); FAILIF(NULL == (algParameters = (RNNParametersT*)MALLOC(nRadii * sizeof(RNNParametersT)))); for(IntT i = 0; i < nRadii; i++) {//默认i=1 algParameters[i] = readRNNParameters(pFile);//从文件读参数 printRNNParameters(stderr, algParameters[i]); nnStructs[i] = initLSH_WithDataSet(algParameters[i], nPoints, dataSetPoints); //核心 //初始化整个数据结构 包括整体+l个hash表 +点映射到桶 } pointsDimension = algParameters[0].dimension; FREE(listOfRadii); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++) { listOfRadii[i] = algParameters[i].parameterR; } } else { // Wrong option. usage(args[0]); exit(1); } }//if (nargs > 9) else { FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); // Determine the R-NN DS parameters, construct the DS and run the queries. transformMemRatios(); for(IntT i = 0; i < nRadii; i++) { // XXX: segregate the sample queries... //建立查询结构,自动优化参数 nnStructs[i] = initSelfTunedRNearNeighborWithDataSet(listOfRadii[i], successProbability, nPoints, pointsDimension, dataSetPoints, nSampleQueries, sampleQueries, (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); } } // if (nargs <= 9) //上面都是根据不同配置,对参数的优化,建立查询结构 DPRINTF1("X\n"); IntT resultSize = nPoints; PPointT *result = (PPointT*)MALLOC(resultSize * sizeof(*result)); PPointT queryPoint; FAILIF(NULL == (queryPoint = (PPointT)MALLOC(sizeof(PointT)))); FAILIF(NULL == (queryPoint->coordinates = (RealT*)MALLOC(pointsDimension * sizeof(RealT)))); //读取查询点的文件 FILE *queryFile = fopen(args[7], "rt"); FAILIF(queryFile == NULL); TimeVarT meanQueryTime = 0; PPointAndRealTStructT *distToNN = NULL; for(IntT i = 0; i < nQueries; i++) {//对于每一个要查询的点 RealT sqrLength = 0; // read in the query point. for(IntT d = 0; d < pointsDimension; d++) { FSCANF_REAL(queryFile, &(queryPoint->coordinates[d])); sqrLength += SQR(queryPoint->coordinates[d]); /*//test if (d >150 && d<160) { printf(" %lf ",queryPoint->coordinates[d]); } if ( d==160) { printf("原始的文件数据\n"); } */ } queryPoint->sqrLength = sqrLength; //printRealVector("Query: ", pointsDimension, queryPoint->coordinates); // get the near neighbors. IntT nNNs = 0; for(IntT r = 0; r < nRadii; r++) {//查询n个近邻点,并计算距离 //查询核心 nNNs = getRNearNeighbors(nnStructs[r], queryPoint, result, resultSize); printf("Total time for R-NN query at radius %0.6lf (radius no. %d):\t%0.6lf\n", (double)(listOfRadii[r]), r, timeRNNQuery); meanQueryTime += timeRNNQuery; if (nNNs > 0) { printf("Query point %d: found %d NNs at distance %0.6lf (%dth radius). First %d NNs are:\n", i, nNNs, (double)(listOfRadii[r]), r, MIN(nNNs, MAX_REPORTED_POINTS)); // compute the distances to the found NN, and sort according to the distance //计算近邻点和查询点的距离 FAILIF(NULL == (distToNN = (PPointAndRealTStructT*)REALLOC(distToNN, nNNs * sizeof(*distToNN)))); for(IntT p = 0; p < nNNs; p++) { distToNN[p].ppoint = result[p]; distToNN[p].real = distance(pointsDimension, queryPoint, result[p]); } qsort(distToNN, nNNs, sizeof(*distToNN), comparePPointAndRealTStructT); // Print the points for(IntT j = 0; j < MIN(nNNs, MAX_REPORTED_POINTS); j++) { ASSERT(distToNN[j].ppoint != NULL); printf("%09d\tDistance:%0.6lf\n", distToNN[j].ppoint->index, distToNN[j].real); CR_ASSERT(distToNN[j].real <= listOfRadii[r]); //DPRINTF("Distance: %lf\n", distance(pointsDimension, queryPoint, result[j])); //printRealVector("NN: ", pointsDimension, result[j]->coordinates); } break; } } if (nNNs == 0) { printf("Query point %d: no NNs found.\n", i); } }// for(IntT i = 0; i < nQueries; i++)每个点查询 // if (nQueries > 0) { meanQueryTime = meanQueryTime / nQueries; printf("Mean query time: %0.6lf\n", (double)meanQueryTime); } for(IntT i = 0; i < nRadii; i++) { freePRNearNeighborStruct(nnStructs[i]); } // XXX: should ideally free the other stuff as well. return 0; }
/* The main entry to LSH package. Depending on the command line parameters, the function computes the R-NN data structure optimal parameters and/or construct the R-NN data structure and runs the queries on the data structure. */ int main(int nargs, char **args){ if(nargs < 9){ usage(args[0]); exit(1); } //initializeLSHGlobal(); // Parse part of the command-line parameters. nPoints = atoi(args[1]); IntT nQueries = atoi(args[2]); pointsDimension = atoi(args[3]); successProbability = atof(args[4]); char* endPtr[1]; RealT thresholdR = strtod(args[5], endPtr); //strtod将字符串转换成浮点数 //r=0.6 //strtod()会扫描参数nptr字符串,跳过前面的空格字符,直到遇上数字或正负符号才开始做转换 //,到出现非数字或字符串结束时('')才结束转换, 并将结果返回。 //若endptr不为NULL,则会将遇到不合条件而终止的nptr中的字符指针由endptr传回。 if (thresholdR == 0 || endPtr[1] == args[5]){ //确保阈值合法 // The value for R is not specified, instead there is a file // specifying multiple R's. thresholdR = 0; // Read in the file FILE *radiiFile = fopen(args[5], "rt"); FAILIF(radiiFile == NULL); fscanf(radiiFile, "%d\n", &nRadii); ASSERT(nRadii > 0); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++){ FSCANF_REAL(radiiFile, &listOfRadii[i]); ASSERT(listOfRadii[i] > 0); FSCANF_REAL(radiiFile, &memRatiosForNNStructs[i]); ASSERT(memRatiosForNNStructs[i] > 0); } }else{ nRadii = 1; //半径的个数为1个 FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); FAILIF(NULL == (memRatiosForNNStructs = (RealT*)MALLOC(nRadii * sizeof(RealT)))); listOfRadii[0] = thresholdR; memRatiosForNNStructs[0] = 1; } DPRINTF("No. radii: %d\n", nRadii); //thresholdR = atof(args[5]); availableTotalMemory = atoll(args[8]); if (nPoints > MAX_N_POINTS) { printf("Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); fprintf(ERROR_OUTPUT, "Error: the structure supports at most %d points (%d were specified).\n", MAX_N_POINTS, nPoints); exit(1); } readDataSetFromFile(args[6]); //数据集的文件名 DPRINTF("Allocated memory (after reading data set): %lld\n", totalAllocatedMemory); Int32T nSampleQueries = N_SAMPLE_QUERY_POINTS; //样本查询点的个数,100 PPointT sampleQueries[nSampleQueries]; //对查询点编号 Int32T sampleQBoundaryIndeces[nSampleQueries]; //第一个大于半径的点的编号,如果有多个半径的话,就会记录更多 if ((nargs < 9) || (strcmp("-c", args[9]) == 0)){ //计算最优参数 // In this cases, we need to generate a sample query set for // computing the optimal parameters. // Generate a sample query set. FILE *queryFile = fopen(args[7], "rt"); //打开查询集,以只读文本方式打开 if (strcmp(args[7], ".") == 0 || queryFile == NULL || nQueries <= 0){ // Choose several data set points for the sample query points. //如果没有查询点就随机选择几个数据集点作为查询点 for(IntT i = 0; i < nSampleQueries; i++){ sampleQueries[i] = dataSetPoints[genRandomInt(0, nPoints - 1)]; } }else{ // Choose several actual query points for the sample query points. nSampleQueries = MIN(nSampleQueries, nQueries); //MIN(100,9) Int32T sampleIndeces[nSampleQueries]; //定义了一个查询点样本索引数组 for(IntT i = 0; i < nSampleQueries; i++){ ////为什么要对查询点索引进行随机变化? 想把样本查询点控制在一定的范围内,如果查询点过多,则样本点最多取100个查询点。 sampleIndeces[i] = genRandomInt(0, nQueries - 1); //对查询点做了一下顺序的变化,对查询点的索引做随机处理。 } // 根据你给的比较条件进行快速排序,通过指针的移动实验排序,排序之后的结果仍然放在原数组中,必须自己写一个比较函数 //http://www.slyar.com/blog/stdlib-qsort.html qsort(数组起始地址,数组元素大小,每个元素的大小,函数指针指向比较函数) qsort(sampleIndeces, nSampleQueries, sizeof(*sampleIndeces), compareInt32T); //qsort,C语言标准库函数,对样本查询点的索引值进行排序 //printIntVector("sampleIndeces: ", nSampleQueries, sampleIndeces); Int32T j = 0; for(Int32T i = 0; i < nQueries; i++){ if (i == sampleIndeces[j]){ //如果样本查询点的索引值与实际查询点的索引值一致,读入点 sampleQueries[j] = readPoint(queryFile); j++; while (i == sampleIndeces[j]){ //如果样本查询点之后的索引值与实践查询点的索引值一致,则直接将此点的值赋给后面一点的值 sampleQueries[j] = sampleQueries[j - 1]; //覆盖之后索引点的值 j++; //取后面的点 } }else{ fscanf(queryFile, "%[^\n]", sBuffer); fscanf(queryFile, "\n"); } } nSampleQueries = j; fclose(queryFile); } // Compute the array sampleQBoundaryIndeces that specifies how to // segregate the sample query points according to their distance // to NN. //边界sampleQBoundaryIndeces只会存取一个点的索引,该点的大小为第一个大于半径点的值 sortQueryPointsByRadii(pointsDimension, nSampleQueries, //查询集的点的个数 sampleQueries, //查询点的集合,函数运行完成后,点的值将以距离数据集合的距离由小到大的顺序排序 nPoints, //数据集点的个数 dataSetPoints, //数据集集合 nRadii, //半径的个数 listOfRadii, //半径的值 sampleQBoundaryIndeces); } //之前的东西-c运行的,-p是不会运行的 RNNParametersT *algParameters = NULL; PRNearNeighborStructT *nnStructs = NULL; if (nargs > 9) { // Additional command-line parameter is specified. if (strcmp("-c", args[9]) == 0) { // Only compute the R-NN DS parameters and output them to stdout. // 如果是-c,就只计算数据集参数,然后输出 printf("%d\n", nRadii); //打印出半径的个数:1个。 将写入到参数文件中, transformMemRatios(); //memRatiosForNNstructs,转换内存使用率。假设每个结构为1,每个半径占用的总内存的比率,用于计算内存 for(IntT i = 0; i < nRadii; i++){ //看使用哪个样本查询点 // which sample queries to use Int32T segregatedQStart = (i == 0) ? 0 : sampleQBoundaryIndeces[i - 1]; //起始点的位置 Int32T segregatedQNumber = nSampleQueries - segregatedQStart; //查询点的个数 if (segregatedQNumber == 0) { //如果计算所得点的个数为0,就查询所有的点,从0到最后 // XXX: not the right answer segregatedQNumber = nSampleQueries; segregatedQStart = 0; } ASSERT(segregatedQStart < nSampleQueries); ASSERT(segregatedQStart >= 0); ASSERT(segregatedQStart + segregatedQNumber <= nSampleQueries); ASSERT(segregatedQNumber >= 0); RNNParametersT optParameters = computeOptimalParameters(listOfRadii[i], //计算最优的运行时间, successProbability, nPoints, pointsDimension, dataSetPoints, segregatedQNumber, sampleQueries + segregatedQStart, (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); //比率 ////memRatioForNNStructs[i]:近邻结构体每个半径所占用的内存比率,计算能用多少内存 printRNNParameters(stdout, optParameters); //将参数打印出来 } exit(0); } else if (strcmp("-p", args[9]) == 0) { // Read the R-NN DS parameters from the given file and run the // queries on the constructed data structure. if (nargs < 10){ usage(args[0]); exit(1); } FILE *pFile = fopen(args[10], "rt"); //读取参数文件,由lsh_computeParas产生 FAILIFWR(pFile == NULL, "Could not open the params file."); fscanf(pFile, "%d\n", &nRadii); //这里只取了参数文件中的半径,那参数文件中的其他数据怎样被取用的?? DPRINTF1("Using the following R-NN DS parameters:\n"); //使用R-NN DS(DateSet)参数 DPRINTF("N radii = %d\n", nRadii); //不知道将数据输出到哪里了?? // printf("Using the following R-NN DS parameters:\n"); // printf("N radii=%d\n",nRadii); FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); FAILIF(NULL == (algParameters = (RNNParametersT*)MALLOC(nRadii * sizeof(RNNParametersT)))); for(IntT i = 0; i < nRadii; i++){ algParameters[i] = readRNNParameters(pFile); //将参数信息,输出到屏幕上 // printRNNParameters(stderr, algParameters[i]);@727 //printRNNParameters(stdout,algParameters[i]); nnStructs[i] = initLSH_WithDataSet(algParameters[i], nPoints, dataSetPoints); //根据用户输入的参数,初始化结构 } pointsDimension = algParameters[0].dimension; FREE(listOfRadii); FAILIF(NULL == (listOfRadii = (RealT*)MALLOC(nRadii * sizeof(RealT)))); for(IntT i = 0; i < nRadii; i++){ listOfRadii[i] = algParameters[i].parameterR; } } else{ // Wrong option. usage(args[0]); exit(1); } } else { FAILIF(NULL == (nnStructs = (PRNearNeighborStructT*)MALLOC(nRadii * sizeof(PRNearNeighborStructT)))); // Determine the R-NN DS parameters, construct the DS and run the queries. transformMemRatios(); for(IntT i = 0; i < nRadii; i++){ // XXX: segregate the sample queries... nnStructs[i] = initSelfTunedRNearNeighborWithDataSet(listOfRadii[i], successProbability, nPoints, pointsDimension, dataSetPoints, nSampleQueries, sampleQueries, (MemVarT)((availableTotalMemory - totalAllocatedMemory) * memRatiosForNNStructs[i])); } } // DPRINTF1("X\n");@ printf("X\n"); IntT resultSize = nPoints; PPointT *result = (PPointT*)MALLOC(resultSize * sizeof(*result)); PPointT queryPoint; FAILIF(NULL == (queryPoint = (PPointT)MALLOC(sizeof(PointT)))); FAILIF(NULL == (queryPoint->coordinates = (RealT*)MALLOC(pointsDimension * sizeof(RealT)))); FILE *queryFile = fopen(args[7], "rt"); FAILIF(queryFile == NULL); TimeVarT meanQueryTime = 0; PPointAndRealTStructT *distToNN = NULL; for(IntT i = 0; i < nQueries; i++){ RealT sqrLength = 0; // read in the query point. for(IntT d = 0; d < pointsDimension; d++){ FSCANF_REAL(queryFile, &(queryPoint->coordinates[d])); sqrLength += SQR(queryPoint->coordinates[d]); //向量到原点的距离 } queryPoint->sqrLength = sqrLength; //printRealVector("Query: ", pointsDimension, queryPoint->coordinates); // get the near neighbors. IntT nNNs = 0; for(IntT r = 0; r < nRadii; r++){ nNNs = getRNearNeighbors(nnStructs[r], queryPoint, result, resultSize); printf("Total time for R-NN query at radius %0.6lf (radius no. %d):\t%0.6lf\n", (double)(listOfRadii[r]), r, timeRNNQuery); meanQueryTime += timeRNNQuery; if (nNNs > 0){ printf("Query point %d: found %d NNs at distance %0.6lf (%dth radius). First %d NNs are:\n", i, nNNs, (double)(listOfRadii[r]), r, MIN(nNNs, MAX_REPORTED_POINTS)); // compute the distances to the found NN, and sort according to the distance FAILIF(NULL == (distToNN = (PPointAndRealTStructT*)REALLOC(distToNN, nNNs * sizeof(*distToNN)))); for(IntT p = 0; p < nNNs; p++){ distToNN[p].ppoint = result[p]; distToNN[p].real = distance(pointsDimension, queryPoint, result[p]); } qsort(distToNN, nNNs, sizeof(*distToNN), comparePPointAndRealTStructT); //C语言标准的函数 // Print the points for(IntT j = 0; j < MIN(nNNs, MAX_REPORTED_POINTS); j++){ ASSERT(distToNN[j].ppoint != NULL); printf("%09d\tDistance:%0.6lf\n", distToNN[j].ppoint->index, distToNN[j].real); //打印点的坐标 CR_ASSERT(distToNN[j].real <= listOfRadii[r]); //DPRINTF("Distance: %lf\n", distance(pointsDimension, queryPoint, result[j])); //printRealVector("NN: ", pointsDimension, result[j]->coordinates); } break; } } if (nNNs == 0){ printf("Query point %d: no NNs found.\n", i); } } if (nQueries > 0){ meanQueryTime = meanQueryTime / nQueries; printf("Mean query time: %0.6lf\n", (double)meanQueryTime); } for(IntT i = 0; i < nRadii; i++){ freePRNearNeighborStruct(nnStructs[i]); } // XXX: should ideally free the other stuff as well. return 0; }