本文整理汇总了C++中Holder::print方法的典型用法代码示例。如果您正苦于以下问题：C++ Holder::print方法的具体用法？C++ Holder::print怎么用？C++ Holder::print使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Holder的用法示例。
在下文中一共展示了Holder::print方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: selfOrganizingMap
void selfOrganizingMap(GArgReader& args){
// Load the file
GMatrix* pData = loadData(args.pop_string());
Holder<GMatrix> hData(pData);
// Parse arguments
std::vector<double> netDims;
unsigned numNodes = 1;
while(args.next_is_uint()){
unsigned dim = args.pop_uint();
netDims.push_back(dim);
numNodes *= dim;
}
if(netDims.size() < 1){
throw Ex("No dimensions specified for self organizing map. ",
"A map must be at least 1 dimensional.");
}
Holder<SOM::ReporterChain> reporters(new SOM::ReporterChain);
Holder<SOM::TrainingAlgorithm> alg(NULL);
Holder<GDistanceMetric> weightDist(new GRowDistance);
Holder<GDistanceMetric> nodeDist(new GRowDistance);
Holder<SOM::NodeLocationInitialization> topology(new SOM::GridTopology);
Holder<SOM::NodeWeightInitialization> weightInit
(new SOM::NodeWeightInitializationTrainingSetSample(NULL));
Holder<SOM::NeighborhoodWindowFunction>
windowFunc(new SOM::GaussianWindowFunction());
//Loading and saving
string loadFrom = "";
string saveTo = "";
//Parameters for different training algorithms
string algoName = "batch";
double startWidth = -1;//Start width - set later if still negative
double endWidth = -1;//End width - set later if still negative
double startRate = -1;//Start learning rate
double endRate = -1;//End learning rate
unsigned numIter = 100;//Total iterations
unsigned numConverge = 1;//#steps for batch to converge
while(args.next_is_flag()){
if(args.if_pop("-tofile")){
saveTo = args.pop_string();
}else if(args.if_pop("-fromfile")){
loadFrom = args.pop_string();
}else if(args.if_pop("-seed")){
GRand::global().setSeed(args.pop_uint());
}else if(args.if_pop("-neighborhood")){
string name = args.pop_string();
if(name == "gaussian"){
windowFunc.reset(new SOM::GaussianWindowFunction());
}else if(name == "uniform"){
windowFunc.reset(new SOM::UniformWindowFunction());
}else{
throw Ex("Only gaussian and uniform are acceptible ",
"neighborhood types");
}
}else if(args.if_pop("-printMeshEvery")){
using namespace SOM;
unsigned interval = args.pop_uint();
string baseFilename = args.pop_string();
unsigned xDim = args.pop_uint();
unsigned yDim = args.pop_uint();
bool showTrain = false;
if(args.if_pop("showTrain") || args.if_pop("showtrain")){
showTrain = true;
}
smart_ptr<Reporter> weightReporter
(new SVG2DWeightReporter(baseFilename, xDim, yDim, showTrain));
Holder<IterationIntervalReporter> intervalReporter
(new IterationIntervalReporter(weightReporter, interval));
reporters->add(intervalReporter.release());
}else if(args.if_pop("-batchTrain")){
algoName = "batch";
startWidth = args.pop_double();
endWidth = args.pop_double();
numIter = args.pop_uint();
numConverge = args.pop_uint();
}else if(args.if_pop("-stdTrain")){
algoName = "standard";
startWidth = args.pop_double();
endWidth = args.pop_double();
startRate = args.pop_double();
endRate = args.pop_double();
numIter = args.pop_uint();
}else{
throw Ex("Invalid option: ", args.peek());
}
}
//Create the training algorithm
Holder<SOM::TrainingAlgorithm> algo;
if(algoName == "batch"){
double netRadius = *std::max_element(netDims.begin(), netDims.end());
if(startWidth < 0){ startWidth = 2*netRadius; }
if(endWidth < 0){ endWidth = 1; }
algo.reset( new SOM::BatchTraining
(startWidth, endWidth, numIter, numConverge,
weightInit.release(), windowFunc.release(),
//.........这里部分代码省略.........