本文整理汇总了C++中Visualizer::visualize方法的典型用法代码示例。如果您正苦于以下问题：C++ Visualizer::visualize方法的具体用法？C++ Visualizer::visualize怎么用？C++ Visualizer::visualize使用的例子？那么恭喜您, 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Visualizer的用法示例。
在下文中一共展示了Visualizer::visualize方法的2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: main
int main()
{
FileReader* pointCloudReader = new FileReader("Jiangtailgong.pcd");
Visualizer* visualizer = new Visualizer(pointCloudReader->getPointCloud());
visualizer->visualize();
return 0;
}示例2: plotLearningCurves
void plotLearningCurves(NNModel* model, DataParser* trainingSet, DataParser* cvSet, double lambda){
/* getting training set and cross validation set */
mat& xTrain = trainingSet->getExampleSet();
mat& yTrain = trainingSet->getLabelSet();
mat& xCross = cvSet->getExampleSet();
mat& yCross = cvSet->getLabelSet();
/* Initializing some usefull variables */
int m = xTrain.n_rows;
int numEvaluations = ((m-stepSize) / stepSize)+1;
/* Initializing data containers */
double* errorCV = new double[numEvaluations];
double* errorT = new double[numEvaluations];
double* foldSizes = new double[numEvaluations];
int currentFoldSize = stepSize;
mat predictions;
for (int i = 0; i < numEvaluations; i++)
{
/* Initializing example fold(size is dependent on iteration) */
mat xFold = xTrain.head_rows(currentFoldSize);
mat yFold = yTrain.head_rows(currentFoldSize);
/* Training model over current example fold */
NNBackPropagation * backPropagation
= new NNBackPropagation(model, xFold, yFold, lambda);
backPropagation->optimize(numTrainingIterations);
model = backPropagation->getUpdatedModel();
/* Getting error over current fold and cross validation set */
predictions = model->predict(xFold);
errorT[i] = model->getCostOver(predictions, yFold);
predictions = model->predict(xCross);
errorCV[i] = model->getCostOver(predictions, yCross);
foldSizes[i] = currentFoldSize;
/* Showing the results of iteration */
cout << " Cost for crossVal. set: " << errorCV[i] << endl;
cout << " Cost for training fold: " << errorT[i] << endl;
cout << " Training finished for fold of: " << currentFoldSize << endl << endl;
/* Changing variables */
currentFoldSize += stepSize;
model->randomlyInitialize();
delete backPropagation;
}
cout << " == Computation finished." << endl;
cout << " == Visualizing data." << endl;
/* ================== Plotting the results ================== */
ostringstream plotNameStream;
plotNameStream << "\"" << "Learning curves: lambda = " << lambda << "\"";
string plotName = plotNameStream.str();
Visualizer *visualizer = new Visualizer("");
visualizer->addSeries("\"Crossvalidation set\"", numEvaluations, foldSizes, errorCV);
visualizer->addSeries("\"Training set\"", numEvaluations, foldSizes, errorT);
visualizer->visualize("\"Learning curves\"", (char*)plotName.c_str(), "\"Fold size\"", "error");
}