int main(void)
{
char letter;
int i, ch, flag = 0, letterFound = 0, isInput = 0, num = 0, numFirst = 0;
unsigned int letterSet = 0, glasSet = 0, tmpSet = 0;
for (i = 0; i < 26; i++) addToSet(&letterSet, 'a' + i);
addToSet(&glasSet, 'a');
addToSet(&glasSet, 'o');
addToSet(&glasSet, 'e');
addToSet(&glasSet, 'i');
addToSet(&glasSet, 'u');
addToSet(&glasSet, 'y');
letterSet = setSubtract(letterSet, glasSet);
tmpSet = glasSet;
while ((ch = getchar()) != EOF)
{
ch = toLower(ch);
if (isLetter(ch))
{
isInput = 1;
if (isKeyExists(letterSet, ch)) continue;
if (isKeyExists(glasSet, ch)) removeFromSet(&glasSet, ch);
else if (!letterFound)
{
letterFound = 1;
numFirst = num + 1;
}
}
else if (isSpace(ch))
{
if (isInput) num++;
isInput = 0;
glasSet = tmpSet;
}
else isInput = 1;
}
if (letterFound) printf("Слово #%d - содержит одинаковые гласные\n", numFirst);
else printf("Не найдено ни одного слова с повторяющимися гласными\n");
return 0;
}
int DataLoader::loadMultiplePhenotype(const std::string& multiplePhenotype,
const std::string& pheno,
const std::string& covar) {
this->FLAG_pheno = pheno;
this->FLAG_cov = covar;
this->FLAG_multiplePheno = multiplePhenotype;
if (covar.empty()) {
this->FLAG_cov = this->FLAG_pheno;
}
// read in analysis template
TextMatrix textMat;
textMat.readFile(FLAG_multiplePheno, TextMatrix::HAS_HEADER);
if (textMat.nrow() == 0 || which(textMat.header(), "pheno") < 0 ||
which(textMat.header(), "covar") < 0) {
logger->warn(
"Wrong multiple phenotype analysis file (no correct headers: \"pheno "
"covar\")");
exit(1);
}
const int nTests = textMat.nrow();
const int phenoCol = which(textMat.getColName(), "pheno");
const int covCol = which(textMat.getColName(), "covar");
for (int i = 0; i < nTests; ++i) {
formula.add(textMat[i][phenoCol], textMat[i][covCol]);
}
logger->info("Load [ %d ] test formulae", (int)formula.size());
std::vector<std::string> phenoLabel = formula.extractResponse();
std::vector<std::string> covarLabel =
formula.extractPredictor(FormulaVector::NO_INTERCEPT);
// std::vector<std::string> phenoLabel = textMat.extractCol("pheno");
// std::vector<std::string> covarLabel =
// extractCovariate(textMat.extractCol("covar"));
// read in ped
TextMatrix pedMat;
if (pedMat.readFile(FLAG_pheno, TextMatrix::HAS_HEADER)) {
logger->error("Failed to load phenotype file [ %s ]!", FLAG_pheno.c_str());
exit(1);
}
if (pedMat.nrow() == 0 || pedMat.header()[0] != "fid" ||
pedMat.header()[1] != "iid") {
logger->warn("Wrong phenotype file [ %s ]", pheno.c_str());
exit(1);
}
pedMat.setRowNameByCol("iid");
pedMat.keepCol(phenoLabel);
if (pedMat.ncol() != (int)phenoLabel.size()) {
logger->error(
"Required responses [ %s ] cannot be found in [ %s ]",
stringJoin(setSubtract(phenoLabel, pedMat.getColName()), ' ').c_str(),
FLAG_pheno.c_str());
exit(1);
}
// read in cov
TextMatrix covMat;
if (covMat.readFile(FLAG_cov, TextMatrix::HAS_HEADER)) {
logger->error("Failed to load covariate file [ %s ]!", FLAG_cov.c_str());
exit(1);
}
if (covMat.nrow() == 0 || tolower(covMat.header()[0]) != "fid" ||
tolower(covMat.header()[1]) != "iid") {
logger->warn(
"Wrong covariate file - empty or unrecognized header line [ %s ]",
covar.c_str());
exit(1);
}
covMat.setRowNameByCol("iid");
covMat.keepCol(covarLabel);
if (covMat.ncol() != (int)covarLabel.size()) {
logger->error(
"Required covariates [ %s ] cannot be found in [ %s ]",
stringJoin(setSubtract(covarLabel, covMat.getColName()), ' ').c_str(),
FLAG_cov.c_str());
exit(1);
}
// orangize ped/cov
pedMat.convert(&phenotype);
covMat.convert(&covariate);
// make sure covarite and phenotype have the sample sets of samples
std::vector<std::string> commonSample =
intersect((phenotype.getRowName()), (covariate.getRowName()));
phenotype.keepRow(commonSample);
covariate.keepRow(commonSample);
// drop all missing rows
std::vector<int> missing =
intersect((phenotype.allMissingRows()), (covariate.allMissingRows()));
phenotype.dropRow(missing);
covariate.dropRow(missing);
// NOTE: do not take imputePheno, imputeCov
// NOTE: do not center, scale ...
// Actual regression model will center phenotype and covariate
return 0;
}
/**
* Rearrange phenotype rows in the order given by @param names
* @param droppedNamed will store sample names who are in @param names but are
* not listed in phenotype.
*
* NOTE: @param names usually is the VCF sample names
* NOTE: Current phenotype data may have NAN values
*/
int DataLoader::arrangePhenotype(const std::vector<std::string>& names,
std::vector<std::string>* droppedNames) {
if (!isUnique(names)) {
logger->error("VCF file have duplicated sample ids. Quitting!");
abort();
}
// not impute phentoype
if (!FLAG_imputePheno) {
std::vector<std::string> noGenotypeSamples =
setSubtract(phenotype.getRowName(), names);
*droppedNames = setSubtract(names, phenotype.getRowName());
const int n = noGenotypeSamples.size();
if (n) {
logger->info("Discard [ %d ] samples as they do not have genotypes", n);
}
phenotype.dropRow(noGenotypeSamples);
phenotype.reorderRow(names);
// TODO: print some info here
return 0;
}
// imputation
std::vector<std::string> noPhenotypeSamples =
setSubtract(names, phenotype.getRowName());
const int n = noPhenotypeSamples.size();
if (n) {
logger->info(
"Impute [ %d ] phenotypes of [ %d ] samples to the mean values",
phenotype.ncol(), n);
phenotype.addRow(noPhenotypeSamples, NAN);
phenotype.imputeMissingToMeanByCol();
}
phenotype.reorderRow(names);
return 0;
#if 0
// phenotype names (vcf sample names) arranged in the same order as in VCF
std::vector<std::string> phenotypeNameInOrder;
std::vector<double>
phenotypeValueInOrder; // phenotype arranged in the same order as in VCF
// TODO: better support for multiple phenotypes
std::map<std::string, double> phenoDict;
for (int i = 0; i < phenotype.nrow(); ++i) {
phenoDict[phenotype.getRowName()[i]] = phenotype[i][0];
}
rearrange(phenoDict, names, droppedNames, &phenotypeNameInOrder,
&phenotypeValueInOrder, FLAG_imputePheno);
// rearrange(phenoDict, vcfSampleNames, &vcfSampleToDrop,
// &phenotypeNameInOrder,
// &phenotypeInOrder, FLAG_imputePheno);
phenotype.resize(phenotypeNameInOrder.size(), 1);
phenotype.setRowName(phenotypeNameInOrder);
phenotype.setCol(0, phenotypeValueInOrder);
if (phenotypeValueInOrder.size() != phenoDict.size()) {
logger->warn(
"Drop [ %d ] samples from phenotype file due to missing genotypes from "
"VCF files",
(int)(phenoDict.size() - phenotypeValueInOrder.size()));
// We may output these samples by comparing keys of phenotype and
// phenotypeNameInOrder
}
#endif
return 0;
}
