ResultVector cluster(vector<LocalFeatures*> &locfeat, bool clusterWithPositions, string splitMode, uint maxSplit, uint stopWithNClusters, string disturbMode, string poolMode, uint dontSplitBelow, uint iterationsBetweenSplits, uint minObservationsPerCluster, double epsilon, string distanceMaker, string saveModelTo, bool saveBeforeSplits, string loadModelFrom)
{
BaseClusterer *clusterer = new EM();
if(loadModelFrom!="")
{
clusterer->loadModel(loadModelFrom);
}
setupClusterer(dynamic_cast<EM*>(clusterer), splitMode, maxSplit, stopWithNClusters, disturbMode, poolMode, dontSplitBelow,
iterationsBetweenSplits, minObservationsPerCluster, epsilon, distanceMaker, saveBeforeSplits);
ResultVector clusterinformation;
if (clusterWithPositions)
{
DBG(10) << "clustering with position information included" << endl;
}
DoubleVectorVector localFeaturesData;
for (uint j = 0; j < locfeat.size(); j++)
{
LocalFeatures* localFeatures = locfeat[j];
int xSize = localFeatures->imageSizeX();
int ySize = localFeatures->imageSizeY();
for (uint i = 0; i < localFeatures->numberOfFeatures(); i++)
{
DoubleVector* lfvector = &((*localFeatures)[i]);
if (clusterWithPositions)
{
pair<double, double> pos = localFeatures->relativePosition(i);
lfvector->push_back((double) localFeatures->position(i).x / (double) xSize);
lfvector->push_back((double) localFeatures->position(i).y / (double) ySize);
}
localFeaturesData.push_back( lfvector );
}
}
if(saveModelTo!="")
{
dynamic_cast<EM*>(clusterer)->run(localFeaturesData, clusterinformation, saveModelTo);
clusterer->saveModel(saveModelTo);
}
else
{
clusterer->run(localFeaturesData, clusterinformation);
}
delete clusterer;
return clusterinformation;
}
double predict(size_t steps) {
if (points_.empty()) {
return 0;
}
if (points_.size() != nPoints_ - 1 + nDim_) {
return points_.back();
}
for (size_t i = 0; i < nDim_; ++i) {
for (size_t j = 0; j < nPoints_; ++j) {
x_(i, j) = points_[i + j];
}
}
auto c = x_ * x_.transpose() / nPoints_;
SelfAdjointEigenSolver<MatrixXd> esC(c);
auto v = esC.eigenvectors();
// LOG(INFO) << OUTLN(esC.eigenvalues());
// LOG(INFO) << OUTLN(v);
for (size_t i = 0; i < nDim_ - 1; ++i) {
for (size_t j = 0; j < nEigen_; ++j) {
vStar_(i, j) = v(i, nDim_ - 1 - j);
}
}
for (size_t i = 0; i < nEigen_; ++i) {
vTau_(0, i) = v(nDim_ - 1, nDim_ - 1 - i);
}
auto predictionM = (vTau_ * vStar_.transpose()) / (1.0 - (vTau_ * vTau_.transpose())(0, 0));
// cout << OUTLN(predictionM) << OUTLN(predictionM.sum());
DoubleVector computed;
auto getPoint = [&](size_t index) {
if (index < points_.size()) {
return points_[index];
}
return computed[index - points_.size()];
};
for (size_t iStep = 0; iStep < steps; ++iStep) {
for (size_t i = 0; i < nDim_ - 1; ++i) {
q_(i, 0) = getPoint((nPoints_ - 1 + nDim_) + i - (nDim_ - 1) + iStep);
}
computed.emplace_back((predictionM * q_)(0, 0));
}
return computed.back();
}
double _weightedSumOfLenSq(const RDGeom::Point3DConstPtrVect &points,
const DoubleVector &weights) {
PRECONDITION(points.size() == weights.size(), "");
double res = 0.0;
RDGeom::Point3DConstPtrVect_CI pti;
const double *wData = weights.getData();
unsigned int i = 0;
for (pti = points.begin(); pti != points.end(); pti++) {
res += (wData[i] * ((*pti)->lengthSq()));
i++;
}
return res;
}
void Pattern::configure(ConfigurationParameters& params, QString prefix) {
//--- get all parameters with the prefix 'cluster:'
QStringList clusterList = params.getParametersWithPrefixList( prefix, "cluster:" );
foreach( QString cluster, clusterList ) {
QString id = cluster.split(':')[1];
if ( id.isNull() || id.isEmpty() ) continue;
//--- now, it check if there is a inputs and outputs parameter and load it
QString str = params.getValue( prefix + "inputs:" + id );
DoubleVector inputs;
if (!str.isNull()) {
QStringList list = str.split(QRegExp("\\s+"), QString::SkipEmptyParts);
for( int i=0; i<list.size(); i++) {
inputs.append( list[i].toDouble() );
}
}
str = params.getValue( prefix + "outputs:" + id );
DoubleVector outputs;
if (!str.isNull()) {
QStringList list = str.split(QRegExp("\\s+"), QString::SkipEmptyParts);
for( int i=0; i<list.size(); i++) {
outputs.append( list[i].toDouble() );
}
}
if ( inputs.size() == 0 && outputs.size() == 0 ) continue;
Cluster* cl = params.getObjectFromParameter<Cluster>( prefix+cluster, false, true );
if ( inputs.size() > 0 ) {
setInputsOf( cl, inputs );
}
if ( outputs.size() > 0 ) {
setOutputsOf( cl, outputs );
}
}
DoubleVector RowDoubleMatrix::conjugateGradient(const DoubleVector &B, double epsilon, unsigned int niter, bool printMessages, unsigned int messageStep) const
{
DoubleVector X(size_, 0.0); // начальное приближение - вектор нулей
DoubleVector resid(size_); // невязка
DoubleVector direction; // направление поиска
DoubleVector temp(size_); // ременное хранилище для обмена данными
double resid_norm; // норма невязки
double alpha;
double beta;
double resid_resid, resid_resid_new;
residual(X, B, resid);
direction = resid;
resid_norm = resid.norm_2();
if (printMessages) std::cout << "Начальная невязка: " << resid_norm << std::endl;
if (resid_norm > epsilon)
{
resid_resid = resid * resid;
for (unsigned int i = 0; i < niter; i++)
{
product(direction, temp);
// std::cout << direction.norm_2() << " " << temp.norm_2() << std::endl;
alpha = (resid_resid) / (direction * temp);
X += alpha * direction;
resid -= alpha * temp;
resid_resid_new = resid * resid;
resid_norm = sqrt(resid_resid_new);
if (resid_norm <= epsilon)
{
if (printMessages)
std::cout << "Решение найдено. Итераций: " << i << ", невязка: " << resid_norm << std::endl;
break;
}
if (printMessages && (i % messageStep == 0))
std::cout << i << ", невязка: " << resid_norm << std::endl;
beta = (resid_resid_new) / (resid_resid);
// d = r + d*beta
direction.scale(beta);
direction += resid;
//
resid_resid = resid_resid_new;
}
}
return X;
}
void MainWindow::gradient()
{
DoubleVector x;
x.push_back(-1.0);
x.push_back(+1.2);
SampleGradient gm;
connect(&gm, SIGNAL(showCoordinares(const DoubleVector &)), this, SLOT(showCoordinares(const DoubleVector &)));
gm.setEpsilon1(0.00001);
gm.setEpsilon2(0.00001);
gm.setEpsilon3(0.00001);
gm.setR1MinimizeEpsilon(0.01, 0.00001);
gm.calculate(x);
}
void LowessSmoothing::smoothData(const DoubleVector& input_x, const DoubleVector& input_y, DoubleVector& smoothed_output)
{
if (input_x.size() != input_y.size())
{
throw Exception::InvalidValue(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION,
"Sizes of x and y values not equal! Aborting... ", String(input_x.size()));
}
// unable to smooth over 2 or less data points (we need at least 3)
if (input_x.size() <= 2)
{
smoothed_output = input_y;
return;
}
Size input_size = input_y.size();
// const Size q = floor( input_size * alpha );
const Size q = (window_size_ < input_size) ? static_cast<Size>(window_size_) : input_size - 1;
DoubleVector distances(input_size, 0.0);
DoubleVector sortedDistances(input_size, 0.0);
for (Size outer_idx = 0; outer_idx < input_size; ++outer_idx)
{
// Compute distances.
// Size inner_idx = 0;
for (Size inner_idx = 0; inner_idx < input_size; ++inner_idx)
{
distances[inner_idx] = std::fabs(input_x[outer_idx] - input_x[inner_idx]);
sortedDistances[inner_idx] = distances[inner_idx];
}
// Sort distances in order from smallest to largest.
std::sort(sortedDistances.begin(), sortedDistances.end());
// Compute weigths.
std::vector<double> weigths(input_size, 0);
for (Size inner_idx = 0; inner_idx < input_size; ++inner_idx)
{
weigths.at(inner_idx) = tricube_(distances[inner_idx], sortedDistances[q]);
}
//calculate regression
Math::QuadraticRegression qr;
std::vector<double>::const_iterator w_begin = weigths.begin();
qr.computeRegressionWeighted(input_x.begin(), input_x.end(), input_y.begin(), w_begin);
//smooth y-values
double rt = input_x[outer_idx];
smoothed_output.push_back(qr.eval(rt));
}
return;
}
DoubleVector MSMSFragmentation::getLossMasses ()
{
DoubleVector dv;
dv.push_back ( 0.0 );
for ( int i = 0 ; i < ionTypes.size () ; i++ ) {
string it = ionTypes [i];
if ( isPrefix ( it, "M+" ) || isPrefix ( it, "M-" ) ) {
string loss = it.substr ( 1 );
if ( loss.length () > 1 && isdigit ( loss [1] ) ) {
dv.push_back ( atof ( loss.c_str () ) );
}
}
}
return dv;
}
RDGeom::Point3D _weightedSumOfPoints(const RDGeom::Point3DConstPtrVect &points,
const DoubleVector &weights) {
PRECONDITION(points.size() == weights.size(), "");
RDGeom::Point3DConstPtrVect_CI pti;
RDGeom::Point3D tmpPt, res;
const double *wData = weights.getData();
unsigned int i = 0;
for (pti = points.begin(); pti != points.end(); pti++) {
tmpPt = (*(*pti));
tmpPt *= wData[i];
res += tmpPt;
i++;
}
return res;
}
void TimeWarp::calculateCausalChromaSimilarityMatrix(DoubleMatrix& firstChromaMatrix, DoubleMatrix& secondChromaMatrix, DoubleMatrix& simMatrix){
//calculates the chroma only similarity matrix
//but we have already done some, so is extending it...
int size = 0;
if (simMatrix.size() > 0){
size = simMatrix[0].size();
}
if (secondChromaMatrix.size() > size ){
for (int x = 0;x < firstChromaMatrix.size();x++){
if (x < simMatrix.size()){
//i.e. entries already exist
for (int y = (int)simMatrix[x].size();y < secondChromaMatrix.size();y++){
double distance;
if (useDotProduct)
distance = getChromaSimilarity(x, y, &firstChromaMatrix, &secondChromaMatrix);
else
distance = getEuclideanDistance(x, y, &firstChromaMatrix, &secondChromaMatrix);
printf("putting one back X %i Y %i dist %f \n", x, y, distance);
simMatrix[x].push_back(distance);
}
}
else {
DoubleVector d;
for (int y = 0;y < secondChromaMatrix.size();y++){
double distance;
if (useDotProduct)
distance = getChromaSimilarity(x, y, &firstChromaMatrix, &secondChromaMatrix);
else
distance = getEuclideanDistance(x, y, &firstChromaMatrix, &secondChromaMatrix);
printf("new row X %i Y %i dist %f\n", x, y, distance);
d.push_back( distance);
}
simMatrix.push_back(d);
}
}
}
if (size > 0)
printf("Causial CHROMA ONLY SIM SIZE %i x %i; ", (int)simMatrix.size(), (int) simMatrix[0].size());
printf("First matrix SIZE %i , SECOND size %i\n", (int)firstChromaMatrix.size(), (int) secondChromaMatrix.size());
}
DoubleVector& get_cleaved_masses ( const string& protein, const IntVector& cleavageIndex )
{
static DoubleVector cleavedMassArray ( 36000 );
StringSizeType numAA = protein.length ();
if ( numAA > cleavedMassArray.size () ) cleavedMassArray.resize ( numAA );
for ( StringSizeType i = 0, j = 0 ; i < numAA ; ) {
double mass = 0.0;
StringSizeType index = cleavageIndex [j];
while ( i <= index ) {
mass += amino_acid_wt [protein[i++]];
}
cleavedMassArray [j++] = mass;
}
return cleavedMassArray;
}
void madara::knowledge::containers::FlexMap::to_container(
DoubleVector& target) const
{
if (context_)
{
ContextGuard context_guard(*context_);
MADARA_GUARD_TYPE guard(mutex_);
// get all children
KnowledgeBase knowledge;
knowledge.facade_for(*context_);
target.set_delimiter(delimiter_);
target.set_name(name_, knowledge);
}
}
void PlotFunction::drawVector(DoubleVector& energyVec, int minIndex, int maxIndex, const WindowRegion& window, const double& maxNumberOfIndexPoints, const double& maxValue){
float screenHeight = window.height;
float screenWidth = window.width;
double numberOfIndexPoints = min(maxNumberOfIndexPoints, (double) maxIndex - minIndex);
double indicesPerStep = (maxIndex - minIndex) / numberOfIndexPoints;
double pixelsPerStep = window.width / numberOfIndexPoints;
int i, j;
double heightScalar = window.height / (1.1*maxValue);
int lastHeight = window.y + screenHeight - (energyVec[minIndex]*heightScalar);;
int newHeight;
int xPosition;
int lastXposition = window.x;
double exactIndex;
for (exactIndex = minIndex; exactIndex < maxIndex; exactIndex += indicesPerStep){
j = round(exactIndex);
i = j - minIndex;
if (j < energyVec.size()){
xPosition = window.x + i*pixelsPerStep;
newHeight = window.y + screenHeight - (energyVec[j]*heightScalar);
ofLine(lastXposition, lastHeight, xPosition, newHeight);
lastHeight = newHeight;
lastXposition = xPosition;
}
}
}
int RateGammaInvar::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
//cout << "Computing Gamma site rates by empirical Bayes..." << endl;
phylo_tree->computePatternLhCat(WSL_RATECAT);
int npattern = phylo_tree->aln->getNPattern();
pattern_rates.resize(npattern);
pattern_cat.resize(npattern);
double *lh_cat = phylo_tree->_pattern_lh_cat;
for (int i = 0; i < npattern; i++) {
double sum_rate = 0.0, sum_lh = phylo_tree->ptn_invar[i];
int best = 0;
double best_lh = phylo_tree->ptn_invar[i];
for (int c = 0; c < ncategory; c++) {
sum_rate += rates[c] * lh_cat[c];
sum_lh += lh_cat[c];
if (lh_cat[c] > best_lh || (lh_cat[c] == best_lh && random_double()<0.5)) { // break tie at random
best = c+1;
best_lh = lh_cat[c];
}
}
pattern_rates[i] = sum_rate / sum_lh;
pattern_cat[i] = best;
lh_cat += ncategory;
}
return ncategory+1;
}
void StandardModel<Two_scale>::set(const DoubleVector& y)
{
int i, j, k = 0;
for (i = 1; i <= 3; i++)
for (j = 1; j <= 3; j++) {
k++;
yu(i, j) = y.display(k);
yd(i, j) = y.display(k + 9);
ye(i, j) = y.display(k + 18);
}
k = 27;
for (i = 1; i <= 3; i++) {
k++;
g(i) = y.display(k);
}
}
DoubleVector RRSchedule::reconfigureWeek(DoubleVector _week, Vector _permute)
// Creates a new week based on the current week and a permutation
{
DoubleVector newWeek;
for (Vector::const_iterator tSlotNum = _permute.begin(); tSlotNum != _permute.end(); ++tSlotNum)
{
// For week0, there are not enough timeslots
if ((week0 and *tSlotNum < max_times - skip_first) or (not week0))
{
newWeek.push_back(_week[*tSlotNum]);
}
}
return newWeek;
}
bool ThermochromicGlazing_Impl::setThickness(double value) {
GlazingVector glazings = mf_glazings();
DoubleVector rollbackValues;
for (unsigned i = 0,n = glazings.size(); i < n; ++ i) {
rollbackValues.push_back(glazings[i].thickness());
bool ok = glazings[i].setThickness(value);
if (!ok) {
// rollback previous values
for (int j = i-1; j >= 0; --j) {
glazings[j].setThickness(rollbackValues[j]);
}
return false;
}
}
return true;
}
void CosSimilarityList::printDelimited ( ostream& os, const DoubleVector& c ) const
{
for ( int i = 0 ; i < c.size () ; i++ ) {
if ( c [i] == -100.0 ) delimitedEmptyCell ( os );
else delimitedCellSigFig ( os, c [i], 3 );
}
}
void gaugegravityBcs1( MssmSoftsusy & m,
const DoubleVector & inputParameters ) {
double M_moduli_local, M_gauge_local, M_mess_local ;
M_moduli_local = inputParameters.display(1) ;
double m1, m2, m3 ;
m.setGaugeCoupling( 1, global_g1 );
m.setGaugeCoupling( 2, global_g2 );
m.setGaugeCoupling( 3, global_g3 );
m1 = l1 * M_moduli_local ;
m2 = l2 * M_moduli_local ;
m3 = l3 * M_moduli_local ;
m.setGauginoMass( 1, m1 ) ;
m.setGauginoMass( 2, m2 ) ;
m.setGauginoMass( 3, m3 ) ;
double mqlsq , mllsq, mursq, mdrsq, mersq ;
double mhusq , mhdsq;
double M_moduli_sqr_local;
M_moduli_sqr_local = M_moduli_local*M_moduli_local ;
mqlsq = ( 1 - nQ ) * M_moduli_sqr_local ;
mllsq = ( 1 - nL ) * M_moduli_sqr_local ;
mursq = ( 1 - nU ) * M_moduli_sqr_local ;
mdrsq = ( 1 - nD ) * M_moduli_sqr_local ;
mersq = ( 1 - nE ) * M_moduli_sqr_local ;
mhusq = ( 1 - nHu) * M_moduli_sqr_local ;
mhdsq = ( 1 - nHd) * M_moduli_sqr_local ;
DoubleMatrix id(3, 3);
id(1, 1) = 1.0; id(2, 2) = 1.0; id(3, 3) = 1.0;
m.setSoftMassMatrix(mQl, mqlsq * id);
m.setSoftMassMatrix(mUr, mursq * id);
m.setSoftMassMatrix(mDr, mdrsq * id);
m.setSoftMassMatrix(mLl, mllsq * id);
m.setSoftMassMatrix(mEr, mersq * id);
m.setMh1Squared(mhdsq);
m.setMh2Squared(mhusq);
double A_HuQU , A_HdQD, A_HdLE ;
A_HuQU = ( 3 - nHu - nQ - nU ) * M_moduli_local ;
A_HdQD = ( 3 - nHd - nQ - nD ) * M_moduli_local ;
A_HdLE = ( 3 - nHd - nL - nE ) * M_moduli_local ;
m.setTrilinearElement(UA, 1, 1, m.displayYukawaElement(YU, 1, 1) * A_HuQU);
m.setTrilinearElement(UA, 2, 2, m.displayYukawaElement(YU, 2, 2) * A_HuQU);
m.setTrilinearElement(UA, 3, 3, m.displayYukawaElement(YU, 3, 3) * A_HuQU);
m.setTrilinearElement(DA, 1, 1, m.displayYukawaElement(YD, 1, 1) * A_HdQD);
m.setTrilinearElement(DA, 2, 2, m.displayYukawaElement(YD, 2, 2) * A_HdQD);
m.setTrilinearElement(DA, 3, 3, m.displayYukawaElement(YD, 3, 3) * A_HdQD);
m.setTrilinearElement(EA, 1, 1, m.displayYukawaElement(YE, 1, 1) * A_HdLE);
m.setTrilinearElement(EA, 2, 2, m.displayYukawaElement(YE, 2, 2) * A_HdLE);
m.setTrilinearElement(EA, 3, 3, m.displayYukawaElement(YE, 3, 3) * A_HdLE);
}
void DataPartitions::doFrequency(DoubleVector dimData){
m_partitions.clear();
PairVector dimOrData;
initPairs(dimOrData, dimData);
pairSort(dimOrData);
int dimsize = dimData.size();
int nBins = this->m_rddtClust->m_rddtOp->m_nBins;
if(nBins == 0 ){
nBins = 1;
}
if(nBins>dimsize){
nBins = dimsize;
}
for(int i = 0; i< nBins; i++){
DataPartition* partition = new DataPartition();
partition->setPartitions(this);
partition->m_partitionIdx = i;
this->m_partitions.push_back(partition);
}
int partitionsize = (int)ceil((double)dimsize/(double)nBins);
for(unsigned i = 0; i< dimOrData.size(); i++){
int p = (int)((double)i/(double)partitionsize);
if(p>(int)m_partitions.size())
p=m_partitions.size();
int first = (dimOrData[i]).first;
(m_partitions[p])->m_parIndices.push_back(first);
}
}
void CosSimilarityList::printHTML ( ostream& os, const DoubleVector& c, const string& styleID ) const
{
for ( int i = 0 ; i < c.size () ; i++ ) {
if ( c [i] == -100.0 ) tableEmptyCell ( os, styleID );
else tableCellSigFig ( os, c [i], 3, false, styleID );
}
}
int RateGamma::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
//cout << "Computing Gamma site rates by empirical Bayes..." << endl;
phylo_tree->computePatternLhCat(WSL_RATECAT);
int npattern = phylo_tree->aln->getNPattern();
pattern_rates.resize(npattern);
pattern_cat.resize(npattern);
double *lh_cat = phylo_tree->_pattern_lh_cat;
for (int i = 0; i < npattern; i++) {
double sum_rate = 0.0, sum_lh = 0.0;
int best = 0;
for (int c = 0; c < ncategory; c++) {
sum_rate += rates[c] * lh_cat[c];
sum_lh += lh_cat[c];
if (lh_cat[c] > lh_cat[best] || (lh_cat[c] == lh_cat[best] && random_double()<0.5)) // break tie at random
best = c;
}
pattern_rates[i] = sum_rate / sum_lh;
pattern_cat[i] = best;
lh_cat += ncategory;
}
return ncategory;
// pattern_rates.clear();
// pattern_rates.insert(pattern_rates.begin(), ptn_rates, ptn_rates + npattern);
// pattern_cat.resize(npattern, 0);
// for (int i = 0; i < npattern; i++)
// for (int j = 1; j < ncategory; j++)
// if (fabs(rates[j] - ptn_rates[i]) < fabs(rates[pattern_cat[i]] - ptn_rates[i]))
// pattern_cat[i] = j;
// delete [] ptn_rates;
}
QVariant LinearFunction_Impl::toVariant() const {
QVariantMap linearFunctionData = AnalysisObject_Impl::toVariant().toMap();
linearFunctionData["function_type"] = QString("LinearFunction");
QVariantList variablesList;
DoubleVector coeffs = coefficients();
int index(0), coeffsN(coeffs.size());
Q_FOREACH(const Variable& var,variables()) {
QVariantMap varMap = var.toVariant().toMap();
varMap["variable_index"] = index;
if (index < coeffsN) {
varMap["coefficient"] = coeffs[index];
}
variablesList.push_back(QVariant(varMap));
++index;
}
DoubleVectorVector QuanMSMSXMLData::getFormulaPurityCoefficients ( const string& name ) const
{
DoubleVectorVector dvv;
StringVector f = getQuanMSMSInfo ( name ).formulae;
DoubleVector m = getQuanMSMSInfo ( name ).masses;
for ( int i = 0 ; i < f.size () ; i++ ) {
DoubleVector dv;
if ( f [i] != "" ) {
IsotopePeakStats ips ( f [i], 1 );
int index = ips.getProbabilityIndexForMass ( m [i] );
if ( index >= 2 ) dv.push_back ( ips.getProbability ( index - 2 ) );
else dv.push_back ( 0.0 );
if ( index >= 1 ) dv.push_back ( ips.getProbability ( index - 1 ) );
else dv.push_back ( 0.0 );
dv.push_back ( ips.getProbability ( index + 1 ) );
dv.push_back ( ips.getProbability ( index + 2 ) );
}
else { // No formulae so assume no correction
for ( int j = 0 ; j < 4 ; j++ ) {
dv.push_back ( 0.0 );
}
}
dvv.push_back ( dv );
}
return dvv;
}
bool StepFunction::derivate( const DoubleVector& inputs, const DoubleVector&, DoubleVector& derivates ) const {
//--- return the same as if it is a sigmoid with lambda = 1
for( unsigned int i=0; i<inputs.size(); i++ ) {
double y;
y = 1.0/( 1.0 + std::exp( -inputs[i] ) );
derivates[i] = y * ( 1.0 - y );
}
return true;
}
void StopRule::multiple (DoubleVector &vec1, DoubleMatrix &mat2, DoubleVector &proVec) {
int row_, col_;
proVec.resize(mat2[0].size());
for (col_ = 0; col_ < mat2[0].size(); col_ ++) {
proVec[col_] = 0.0;
for (row_ = 0; row_ < mat2.size(); row_ ++)
proVec[col_] += vec1[row_] * mat2[row_][col_];
}
}
void DataPartitions::initPairs(PairVector &dimOrData, DoubleVector dimData){
dimOrData.clear();
for(unsigned i = 0; i< dimData.size(); i++){
int first = (int)i;
double second = dimData[i];
std::pair<int, double> temp(first,second);
dimOrData.push_back(temp);
}
}
bool TimeWarp::extendAlignmentUp(const int& endIndexY, DoubleMatrix *alignmentMatrix){
DoubleVector d;
d = (*alignmentMatrix)[0];//alignmentMatrix[0];//
int heightSize = d.size();
if (heightSize < endIndexY){
//then we haven't finished yet
for (int i = 0;i < (*alignmentMatrix).size();i++){
double value = getDistance(i, heightSize);
value += getRestrictedMinimum(i, heightSize, value, 0, 0);//min values 0
(*alignmentMatrix)[i].push_back(value);//
}
}
if ((*alignmentMatrix)[0].size() == endIndexY)
return true;
else
return false;
}
void RddtClust::getDimData(DoubleVector& dimData, int idx){
using namespace Rcpp;
XmdvToolMainWnd* m_mwd = this->m_rddtOp->m_pm->getMainWnd();
RInside r = m_mwd->getRinstance();
//qDebug()<<"idx qt "<<idx;
r["currentDim"] = idx;
std::string cmd = "currentDim <- as.integer(currentDim)+1;\n"
//"print(currentDim);\n"
"dimData <- M[,currentDim];\n"
"dimData";
SEXP evals = r.parseEval(cmd);
NumericVector rDimData(evals);
dimData.clear();
for(int i = 0; i< rDimData.length(); i++){
dimData.push_back(rDimData[i]);
}
}
// l labels the position the vector index goes in.
// After that, indices are cyclic.
Tensor outerProduct(const DoubleVector &V, const DoubleMatrix & M, int l) {
Tensor temp;
int i, j, k;
for (i=1; i<=3; i++)
for (j=1; j<=3; j++)
for (k=1; k<=3; k++) {
switch(l) {
case 1: temp(i, j, k) = V.display(i) * M.display(j, k); break;
case 2: temp(i, j, k) = V.display(j) * M.display(k, i); break;
case 3: temp(i, j, k) = V.display(k) * M.display(i, j); break;
default:
ostringstream ii;
ii << "Trying to outer product " << l << "th element of tensor";
throw ii.str();
break;
}
}
return temp;
}
