本文整理汇总了C++中Tree::LOOCVBuildTree方法的典型用法代码示例。如果您正苦于以下问题：C++ Tree::LOOCVBuildTree方法的具体用法？C++ Tree::LOOCVBuildTree怎么用？C++ Tree::LOOCVBuildTree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Tree的用法示例。
在下文中一共展示了Tree::LOOCVBuildTree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: main
//.........这里部分代码省略.........
printf("Ungapped Alignment\n");
else
printf("Gap open = %.3lf, gap extend = %.3lf\n", gapOpen, gapExtend);
}
}else{
printf("No input motifs provided!\n\n");
}
if(genRandMotifs){
//Generate some random matrices
RandPSSMGen* RPG = new RandPSSMGen(Plat->inputMotifs, Plat->GetMatCount(), 10000, randMatOut);
RPG->RunGenerator();
}
if(genRandScores){
//Find rand dist
Plat->GetRandDistrib(scoresOut, ALIGN);
}else if(!scoresProvided){
printf("No score distribution file provided!\n\n");
}
if(testing){
PlatformTesting* PT = new PlatformTesting(CC);
//Print the distribution of column depth
// PT->ColumnDepthDist(Plat->inputMotifs, Plat->GetMatCount());
//Print the similarities of all columns against all others
// PT->ColumnScoreDist(Plat->inputMotifs, Plat->GetMatCount(), 0.05);
double z;
for(z=0.25; z<0.8; z+=0.05)
PT->RandColumns(Plat, z);
for(z=0.8; z<=1.0; z+=0.01)
PT->RandColumns(Plat, z);
delete(PT);
}
if(scoresProvided || preAlign){
Plat->ReadScoreDists(scoreDist);
if(!silent && !htmlOutput){printf("Scores read\n");}
if(Plat->GetMatCount()>1){
if(preAlign){
//No alignments or trees built here
pssmAlignment = MA->PreAlignedInput(Plat);
}else{
//Multiple alignment procedure
Plat->PreAlign(ALIGN);
if(pairwiseOnly){
if(!silent && !htmlOutput){printf("\nPairwise alignment scores:\n");}
Plat->PrintPairwise();
}if(!ma_off){
MA->ImportBasics(Plat, ALIGN);
if(!silent && !htmlOutput){printf("Alignments Finished\n");}
if(!testingAcc){
if(tree_loocv && testingTree){
T->LOOCVBuildTree(Plat, testingTree);
}else{
if(testingTree && !silent && !htmlOutput){printf("Calinski & Harabasz:\n\tNumClust\tC&H_Metric\n");}
T->BuildTree(Plat, testingTree);
if(!silent && treeClusts){printf("The Calinski & Harabasz statistic suggests %.0lf clusters in the input motifs\n", T->GetNodesMinCH());}
if(printTreeClusts){
T->PrintLevel(outFileName, int(T->GetNodesMinCH()));
}
}
T->PrintTree(outFileName);
if(!silent && !htmlOutput){printf("Tree Built\n");}
if(!silent){
if(!silent && !htmlOutput){printf("Multiple Alignment:\n");}
pssmAlignment = MA->BuildAlignment(Plat, ALIGN, T);
}
}
}
}
//Experiment with the Protein Domains
if(usingDomains){
PROTS = new ProteinDomains();
PROTS->ReadDomains(inputProteins, Plat->inputMotifs, Plat->GetMatCount());
PROTS->MutualInformation(pssmAlignment, MA->Alignment2Profile(pssmAlignment, "AlignmentMotif"), Plat->inputMotifs, Plat->GetMatCount());
delete PROTS;
}
}
//Similarity match against the database
if(simMatching){
Plat->ReadTransfacFile(matchTFs, famNames, false, false);
Plat->SimilarityMatching(ALIGN, outFileName, famNames, matchTopX);
}
}
if(testingAcc && scoresProvided && inputProvided && Plat->GetMatCount()>1){
PlatformTesting* PT = new PlatformTesting(CC);
PT->PairwisePredictionAccuracy(Plat);
}
delete(MA);
delete(T);
delete(CC);
delete(ALIGN);
}
delete(Plat);
return(0);
}