double StochasticThresholdForInitiationOfBedloadMotion::calculateNotConst (const RegularRiverReachProperties& regularRiverReachProperties)
{
double result = std::numeric_limits<double>::quiet_NaN();
std::map<double,std::vector<double> >::iterator currentValues = mapFromWidthToValues.find(regularRiverReachProperties.activeWidth);
if(currentValues == mapFromWidthToValues.end())
{
double currentMiu (miu);
double currentBeta (beta);
std::map<double,std::pair<double,double> >::iterator currentSpecialValues = mapFromWidthToSpecialValues.find(regularRiverReachProperties.activeWidth);
if(currentSpecialValues != mapFromWidthToSpecialValues.end())
{
currentMiu = currentSpecialValues->second.first;
currentBeta = currentSpecialValues->second.second;
}
std::vector<double> tmpValues (6,currentMiu); //miu, currentValue, previousValue, prePreviousValue
tmpValues.at(0) = std::numeric_limits<double>::quiet_NaN(); //discharge
tmpValues.at(2) = currentBeta; //beta
mapFromWidthToValues[regularRiverReachProperties.activeWidth] = tmpValues;
currentValues = mapFromWidthToValues.find(regularRiverReachProperties.activeWidth);
}
/*
if(regularRiverReachProperties.discharge == currentValues->second.at(0))
{
result = currentValues->second.at(3);
}
else*/
{
currentValues->second.at(0) = regularRiverReachProperties.discharge;
currentValues->second.at(5) = currentValues->second.at(4); // prePreviousValue = previousValue
currentValues->second.at(4) = currentValues->second.at(3); // previousValue = currentValue
double randomNumber = (1.0+rand()) / (2.0+RAND_MAX); //Uniform Distribution
randomNumber = currentValues->second.at(1) - (currentValues->second.at(2) * log( (-1.0) * log(randomNumber) ) ); //Gumbel Distribution
result = (randomNumber * weightForCurrent) + (currentValues->second.at(4) * weightForPrevious) + (currentValues->second.at(5) * weightForPrePrevious);
result = std::max(result,minimumThresholdValue);
if(correctionForBedloadWeightAtSteepCounterSlopes && regularRiverReachProperties.bedslope < 0.0)
{
result = CorrectionForBedloadWeightAtSteepSlopes::calculateCorrectedDimensionlessCriticalShearStressAtSteepCounterSlopes(result,(regularRiverReachProperties.getOverallParameters())->getAngleOfReposeInRadians(),regularRiverReachProperties.bedslope);
}
currentValues->second.at(3) = result;
}
return result;
}
// format = {Virtual place, Loop closure, level1, level2, l3, l4...}
void BayesFilter::setPredictionLC(const std::string & prediction)
{
std::list<std::string> strValues = uSplit(prediction, ' ');
if(strValues.size() < 2)
{
UERROR("The number of values < 2 (prediction=\"%s\")", prediction.c_str());
}
else
{
std::vector<double> tmpValues(strValues.size());
int i=0;
bool valid = true;
for(std::list<std::string>::iterator iter = strValues.begin(); iter!=strValues.end(); ++iter)
{
tmpValues[i] = std::atof((*iter).c_str());
//UINFO("%d=%e", i, tmpValues[i]);
if(tmpValues[i] < 0.0 || tmpValues[i]>1.0)
{
valid = false;
break;
}
++i;
}
if(!valid)
{
UERROR("The prediction is not valid (values must be between >0 && <=1) prediction=\"%s\"", prediction.c_str());
}
else
{
_predictionLC = tmpValues;
}
}
_totalPredictionLCValues = 0.0f;
for(unsigned int j=0; j<_predictionLC.size(); ++j)
{
_totalPredictionLCValues += _predictionLC[j];
}
}
void merge(std::vector<ValueType> &values, Comparator<ValueType> &comparator,
int left, int mid, int right) {
// Assumption: left, mid and right are valid indices
std::vector<ValueType> tmpValues(values.begin() + left, values.begin() + right + 1);
int i = left;
int j = mid + 1;
int k = left;
while ((i <= mid) && (j <= right)) {
if (comparator(tmpValues[i - left], tmpValues[j - left])) {
values[k++] = tmpValues[(i++) - left];
} else {
values[k++] = tmpValues[(j++) - left];
}
}
while (i <= mid) {
values[k++] = tmpValues[(i++) - left];
}
// There is no need to copy values from positions j to right, if any,
// because the values are already in the correct position
}
