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;
}
// parallel version with arbitrary dimensions
int gridSearchParallel(DoublePyArrayVector const &controlGridArray, MaximizerCallParams ¶ms, double &rMaxVal, DoubleVector &rArgMaxArray) {
int nGrids = controlGridArray.size();
int i;
IntVector lenArray(nGrids);
for (i=0; i<nGrids; i++) {
// check that all grids are 1-dimensional
assert(controlGridArray[i].ndim() == 1);
lenArray[i] = controlGridArray[i].dims()[0];
}
unsigned int totalGridSize = 1;
double totalGridSize2 = 1.0;
for (i=0; i<nGrids; i++) {
totalGridSize *= lenArray[i];
totalGridSize2 *= double(lenArray[i]);
}
// check that total grid space isn't too large to be indexed
assert(totalGridSize2 < double(UINT_MAX));
MaxIndexFnObj2 fnObj(controlGridArray, params);
parallel_reduce( blocked_range<size_t>(0, totalGridSize), fnObj);
rMaxVal = fnObj.m_value_of_max;
rArgMaxArray.resize(fnObj.m_argmax.size());
copy(fnObj.m_argmax.begin(), fnObj.m_argmax.end(), rArgMaxArray.begin());
return 1;
}
// must be mt-safe. don't use boost::python handles!
double objectiveFunction(DoubleVector const &args) const {
bpl::list args2;
for (DoubleVector::const_iterator iter=args.begin(); iter != args.end(); iter++) {
args2.append(*iter);
}
bpl::object result = m_CallbackFn(args2);
return bpl::extract<double>(result);
}
void RateMeyerHaeseler::getRates(DoubleVector &rates) {
rates.clear();
if (empty()) {
rates.resize(phylo_tree->aln->size(), 1.0);
} else {
rates.insert(rates.begin(), begin(), end());
}
}
void RRSchedule::print_DoubleVector(DoubleVector _invect, std::ostream &stream = std::cout)
{
for (DoubleVector::const_iterator row = _invect.begin(); row != _invect.end(); ++row)
{
print_Vector(*row, stream);
stream << '\n' << std::flush;
}
}
int RRSchedule::DVectMax(DoubleVector _invect)
{
int global_max = 0;
for (DoubleVector::const_iterator tslot = _invect.begin(); tslot != _invect.end(); ++tslot)
{
global_max = std::max(VectMax(*tslot), global_max);
}
return global_max;
}
int RRSchedule::DVectMin(DoubleVector _invect)
{
int global_min = max_per_time;
for (DoubleVector::const_iterator tslot = _invect.begin(); tslot != _invect.end(); ++tslot)
{
global_min = std::min(VectMin(*tslot), global_min);
}
return global_min;
}
static void initialiseIndexScore ()
{
int numEntries;
char* info = getFileInfo ( MsparamsDir::instance ().getParamPath ( "indicies.txt" ), '>', 1, true, &numEntries );
for ( int i = 0 ; i < numEntries ; i++ ) {
names.push_back ( ( i == 0 ) ? strtok ( info, "\n" ) : strtok ( NULL, "\n" ) );
DoubleVector dv (52);
indexV.push_back ( dv );
fill ( dv.begin (), dv.end (), 0.0 );
for ( ; ; ) {
char aa;
double value;
char* line = strtok ( NULL, "\n" );
if ( !strcmp ( line, ">" ) ) break;
sscanf ( line, "%c %lf", &aa, &value );
indexV [i][aa-'A'] = value;
}
}
initialised = true;
}
Vector RRSchedule::compute_team_waits_for_week(DoubleVector _week)
// Compute how long each team will wait in a week
{
DoubleVector team_played_counts;
Vector team_wait;
init2DEmpty(team_played_counts, max_teams, max_per_night);
// First determine in which timeslot each team played
int tslotNum = 0;
for (DoubleVector::const_iterator _tslot = _week.begin(); _tslot != _week.end(); ++_tslot)
{
for (Vector::const_iterator team = _tslot->begin(); team != _tslot->end(); ++team)
{
team_played_counts[*team].push_back(tslotNum);
}
tslotNum ++;
}
// From that, determine how long a team will wait that week
for (DoubleVector::const_iterator _team = team_played_counts.begin(); _team != team_played_counts.end(); ++_team)
{
if (_team->size() > 0)
{
team_wait.push_back(VectMax(*_team) - VectMin(*_team) - int(_team->size()) + 1);
}
else
{
team_wait.push_back(0);
}
}
return team_wait;
}
// single-threaded grid search with an arbitrary number of dimensions
int gridSearch(DoublePyArrayVector const &controlGridArray, MaximizerCallParams ¶ms, double &rMaxVal, DoubleVector &rArgMaxArray) {
int nGrids = controlGridArray.size();
int i;
IntVector lenArray(nGrids), strideArray(nGrids), dataIndexArray(nGrids);
for (i=0; i<nGrids; i++) {
// check that all grids are 1-dimensional
assert(controlGridArray[i].ndim() == 1);
lenArray[i] = controlGridArray[i].dims()[0];
strideArray[i] = controlGridArray[i].strides()[0];
dataIndexArray[i] = 0;
}
DoubleVector argArray(nGrids);
int nIter = 0;
int nMaxMultiplicity = 0;
bool bDone = false;
// check for zero size, if one grid has zero size then there's nothing to do
for (i=0; i<nGrids; i++) {
if (lenArray[i] == 0) {
bDone = true;
break;
}
}
double max = -DBL_MAX;
while (!bDone) {
double result;
// get double array args from char* data
for (i=0; i<nGrids; i++) {
char *pData = (char*) (controlGridArray[i].array().data());
char *pArg = pData + (strideArray[i] * dataIndexArray[i]);
argArray[i] = *(double*) pArg;
}
result = params.objectiveFunction(argArray);
if (nIter == 0) { // first iteration
max = result;
rArgMaxArray.resize(argArray.size());
copy(argArray.begin(), argArray.end(), rArgMaxArray.begin());
nMaxMultiplicity = 1;
} else {
if (result > max) {
max = result;
rArgMaxArray.resize(argArray.size());
copy(argArray.begin(), argArray.end(), rArgMaxArray.begin());
nMaxMultiplicity = 1;
} else if (result == max) {
nMaxMultiplicity++;
}
}
nIter++;
// increment indices for next iteration
bDone = true;
for (i=nGrids-1; i>=0; i--) {
// increment index i
dataIndexArray[i] += 1;
if (dataIndexArray[i] == lenArray[i]) {
// cycle this index
dataIndexArray[i] = 0;
} else {
bDone = false;
break;
}
// if we make it here, all indices have cycled, therefore we are done
}
}
rMaxVal = max;
return nMaxMultiplicity;
}
int RateMeyerDiscrete::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
pattern_rates.insert(pattern_rates.begin(), begin(), end());
pattern_cat.insert(pattern_cat.begin(), ptn_cat, ptn_cat + size());
return ncategory;
}
bool
RandomNumberInputHandler::handleInput(const yarp::os::Bottle & input,
const YarpString & senderChannel,
yarp::os::ConnectionWriter * replyMechanism,
const size_t numBytes)
{
#if (! defined(ODL_ENABLE_LOGGING_))
# if MAC_OR_LINUX_
# pragma unused(senderChannel,replyMechanism,numBytes)
# endif // MAC_OR_LINUX_
#endif // ! defined(ODL_ENABLE_LOGGING_)
ODL_OBJENTER(); //####
ODL_S2s("senderChannel = ", senderChannel, "got ", input.toString()); //####
ODL_P1("replyMechanism = ", replyMechanism); //####
ODL_I1("numBytes = ", numBytes); //####
bool result = true;
try
{
if (0 < input.size())
{
BaseChannel * theOutput = _shared.getOutput();
RandomNumberClient * theClient = (RandomNumberClient *) _shared.getClient();
if (theClient && theOutput)
{
int count;
yarp::os::Value argValue(input.get(0));
if (argValue.isInt())
{
count = argValue.asInt();
}
else if (argValue.isDouble())
{
count = static_cast<int>(argValue.asDouble());
}
else
{
count = 1;
}
if (0 > count)
{
count = 1;
}
if (1 < count)
{
DoubleVector randResult;
if (theClient->getRandomNumbers(count, randResult))
{
yarp::os::Bottle message;
if (0 < randResult.size())
{
for (DoubleVector::const_iterator it(randResult.begin());
randResult.end() != it; ++it)
{
message.addDouble(*it);
}
}
_shared.lock();
if (! theOutput->write(message))
{
ODL_LOG("(! theOutput->write(message))"); //####
#if defined(MpM_StallOnSendProblem)
Stall();
#endif // defined(MpM_StallOnSendProblem)
}
_shared.unlock();
}
else
{
ODL_LOG("! (theClient->getRandomNumbers(count, randResult))"); //####
}
}
else if (0 < count)
{
double randResult;
if (theClient->getOneRandomNumber(randResult))
{
yarp::os::Bottle message;
message.addDouble(randResult);
_shared.lock();
if (! theOutput->write(message))
{
ODL_LOG("(! theOutput->write(message))"); //####
#if defined(MpM_StallOnSendProblem)
Stall();
#endif // defined(MpM_StallOnSendProblem)
}
_shared.unlock();
}
else
{
ODL_LOG("! (theClient->getOneRandomNumber(randResult))"); //####
}
}
else
{
_shared.deactivate();
}
}
}
}
catch (...)
{
ODL_LOG("Exception caught"); //####
throw;
}
ODL_OBJEXIT_B(result); //####
return result;
} // RandomNumberInputHandler::handleInput
void RateMeyerHaeseler::setRates(DoubleVector &rates) {
clear();
insert(begin(), rates.begin(), rates.end());
}
int RateMeyerHaeseler::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
pattern_rates.insert(pattern_rates.begin(), begin(), end());
return size();
}
bool RRSchedule::isPresent(DoubleVector _vect, Vector item)
{
return (std::find(_vect.begin(), _vect.end(), item) != _vect.end());
}
