本文整理汇总了C++中ArrayXXd::resize方法的典型用法代码示例。如果您正苦于以下问题：C++ ArrayXXd::resize方法的具体用法？C++ ArrayXXd::resize怎么用？C++ ArrayXXd::resize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ArrayXXd的用法示例。
在下文中一共展示了ArrayXXd::resize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: run
void NestedSampler::run(LivePointsReducer &livePointsReducer, const int NinitialIterationsWithoutClustering,
const int NiterationsWithSameClustering, const int maxNdrawAttempts,
const double maxRatioOfRemainderToCurrentEvidence, string pathPrefix)
{
int startTime = time(0);
double logMeanLiveEvidence;
terminationFactor = maxRatioOfRemainderToCurrentEvidence;
outputPathPrefix = pathPrefix;
if (printOnTheScreen)
{
cerr << "------------------------------------------------" << endl;
cerr << " Bayesian Inference problem has " << Ndimensions << " dimensions." << endl;
cerr << "------------------------------------------------" << endl;
cerr << endl;
}
// Save configuring parameters to an output ASCII file
string fileName = "configuringParameters.txt";
string fullPath = outputPathPrefix + fileName;
File::openOutputFile(outputFile, fullPath);
outputFile << "# List of configuring parameters used for the NSMC." << endl;
outputFile << "# Row #1: Ndimensions" << endl;
outputFile << "# Row #2: Initial(Maximum) NlivePoints" << endl;
outputFile << "# Row #3: Minimum NlivePoints" << endl;
outputFile << "# Row #4: NinitialIterationsWithoutClustering" << endl;
outputFile << "# Row #5: NiterationsWithSameClustering" << endl;
outputFile << "# Row #6: maxNdrawAttempts" << endl;
outputFile << "# Row #7: terminationFactor" << endl;
outputFile << "# Row #8: Niterations" << endl;
outputFile << "# Row #9: Optimal Niterations" << endl;
outputFile << "# Row #10: Final Nclusters" << endl;
outputFile << "# Row #11: Final NlivePoints" << endl;
outputFile << "# Row #12: Computational Time (seconds)" << endl;
outputFile << Ndimensions << endl;
outputFile << initialNlivePoints << endl;
outputFile << minNlivePoints << endl;
outputFile << NinitialIterationsWithoutClustering << endl;
outputFile << NiterationsWithSameClustering << endl;
outputFile << maxNdrawAttempts << endl;
outputFile << terminationFactor << endl;
// Set up the random number generator. It generates integer random numbers
// between 0 and NlivePoints-1, inclusive.
uniform_int_distribution<int> discreteUniform(0, NlivePoints-1);
// Draw the initial sample from the prior PDF. Different coordinates of a point
// can have different priors, so these have to be sampled individually.
if (printOnTheScreen)
{
cerr << "------------------------------------------------" << endl;
cerr << " Doing initial sampling of parameter space..." << endl;
cerr << "------------------------------------------------" << endl;
cerr << endl;
}
nestedSample.resize(Ndimensions, NlivePoints);
int beginIndex = 0;
int NdimensionsOfCurrentPrior;
ArrayXXd priorSample;
for (int i = 0; i < ptrPriors.size(); i++)
{
// Some priors cover one particalar coordinate, others may cover two or more coordinates
// Find out how many dimensions the current prior covers.
NdimensionsOfCurrentPrior = ptrPriors[i]->getNdimensions();
// Draw the subset of coordinates randomly from the current prior
priorSample.resize(NdimensionsOfCurrentPrior, NlivePoints);
ptrPriors[i]->draw(priorSample);
// Insert this random subset of coordinates into the total sample of coordinates of points
nestedSample.block(beginIndex, 0, NdimensionsOfCurrentPrior, NlivePoints) = priorSample;
// Move index to the beginning of the coordinate set of the next prior
beginIndex += NdimensionsOfCurrentPrior;
}
// Compute the log(Likelihood) for each of our points in the live sample
logLikelihood.resize(NlivePoints);
for (int i = 0; i < NlivePoints; ++i)
{
//.........这里部分代码省略.........