size_t CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::
SortPointSet(arma::Col<size_t>& indices,
arma::vec& distances,
const size_t childFarSetSize,
const size_t childUsedSetSize,
const size_t farSetSize)
{
// We'll use low-level memcpy calls ourselves, just to ensure it's done
// quickly and the way we want it to be. Unfortunately this takes up more
// memory than one-element swaps, but there's not a great way around that.
const size_t bufferSize = std::min(farSetSize, childUsedSetSize);
const size_t bigCopySize = std::max(farSetSize, childUsedSetSize);
// Sanity check: there is no need to sort if the buffer size is going to be
// zero.
if (bufferSize == 0)
return (childFarSetSize + farSetSize);
size_t* indicesBuffer = new size_t[bufferSize];
ElemType* distancesBuffer = new ElemType[bufferSize];
// The start of the memory region to copy to the buffer.
const size_t bufferFromLocation = ((bufferSize == farSetSize) ?
(childFarSetSize + childUsedSetSize) : childFarSetSize);
// The start of the memory region to move directly to the new place.
const size_t directFromLocation = ((bufferSize == farSetSize) ?
childFarSetSize : (childFarSetSize + childUsedSetSize));
// The destination to copy the buffer back to.
const size_t bufferToLocation = ((bufferSize == farSetSize) ?
childFarSetSize : (childFarSetSize + farSetSize));
// The destination of the directly moved memory region.
const size_t directToLocation = ((bufferSize == farSetSize) ?
(childFarSetSize + farSetSize) : childFarSetSize);
// Copy the smaller piece to the buffer.
memcpy(indicesBuffer, indices.memptr() + bufferFromLocation,
sizeof(size_t) * bufferSize);
memcpy(distancesBuffer, distances.memptr() + bufferFromLocation,
sizeof(ElemType) * bufferSize);
// Now move the other memory.
memmove(indices.memptr() + directToLocation,
indices.memptr() + directFromLocation, sizeof(size_t) * bigCopySize);
memmove(distances.memptr() + directToLocation,
distances.memptr() + directFromLocation, sizeof(ElemType) * bigCopySize);
// Now copy the temporary memory to the right place.
memcpy(indices.memptr() + bufferToLocation, indicesBuffer,
sizeof(size_t) * bufferSize);
memcpy(distances.memptr() + bufferToLocation, distancesBuffer,
sizeof(ElemType) * bufferSize);
delete[] indicesBuffer;
delete[] distancesBuffer;
// This returns the complete size of the far set.
return (childFarSetSize + farSetSize);
}
/**
* WARNING: Only use this method if you know what you are doing!
*
* This deallocates any data currently in this matrix.
* It then takes the data from the given arguments;
* this transfers ownership of the data to this class,
* clearing the data from the given arguments.
* This permits piecemeal construction of a CSC matrix without
* unnecessary reallocation.
*
* @param new_row_indices WARNING: These MUST be sorted in increasing
* order (for each column).
*/
void reset_nocopy(size_type m, size_type n,
arma::Col<size_type>& new_col_offsets,
arma::Col<size_type>& new_row_indices,
arma::Col<value_type>& new_values) {
assert(new_col_offsets.size() == n + 1);
assert(new_row_indices.size() == new_values.size());
if (new_row_indices.size() != 0)
assert(new_row_indices[new_row_indices.size()-1] < m);
// TO DO: More validity checks.
n_rows = m;
n_cols = n;
// TO DO: USE ARMA SWAP METHODS ONCE IMPLEMENTED
std::swap(col_offsets_, new_col_offsets);
std::swap(row_indices_, new_row_indices);
std::swap(values_, new_values);
}
inline double ParallelKinematicMachine3PUPS::getEndEffectorPoseError(
const arma::Col<double>::fixed<6>& endEffectorPose,
const arma::Row<double>& redundantJointActuations) const {
const arma::Cube<double>::fixed<3, 3, 2>& model = getModel(endEffectorPose, redundantJointActuations);
const arma::Mat<double>::fixed<3, 3>& baseJoints = model.slice(0);
const arma::Mat<double>::fixed<3, 3>& endEffectorJoints = model.slice(1);
arma::Mat<double>::fixed<3, 3> endEffectorJointsRotated = endEffectorJoints;
endEffectorJointsRotated.each_col() -= endEffectorPose.subvec(0, 1);
const arma::Mat<double>::fixed<3, 3>& baseToEndEffectorJointPositions = endEffectorJoints - baseJoints;
const arma::Row<double>::fixed<3>& baseToEndEffectorJointActuations = arma::sqrt(arma::sum(arma::square(baseToEndEffectorJointPositions)));
if (arma::any(baseToEndEffectorJointActuations < minimalActiveJointActuations_) || arma::any(baseToEndEffectorJointActuations > maximalActiveJointActuations_)) {
return 0.0;
}
arma::Mat<double>::fixed<6, 3> forwardKinematic;
forwardKinematic.head_rows(3) = baseToEndEffectorJointPositions;
for (std::size_t n = 0; n < baseToEndEffectorJointPositions.n_cols; ++n) {
forwardKinematic.submat(3, n, 5, n) = arma::cross(endEffectorJointsRotated.col(n), baseToEndEffectorJointPositions.col(n));
}
arma::Mat<double> inverseKinematic(6, 3 + redundantJointIndicies_.n_elem, arma::fill::zeros);
inverseKinematic.diag() = -arma::sqrt(arma::sum(arma::square(baseToEndEffectorJointPositions)));
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; ++n) {
const unsigned int& redundantJointIndex = redundantJointIndicies_(n);
inverseKinematic(n, 3 + n) = arma::dot(baseToEndEffectorJointPositions.col(redundantJointIndex), redundantJointAngles_.col(redundantJointIndex));
}
return -1.0 / arma::cond(arma::solve(forwardKinematic.t(), inverseKinematic));
}
void permuteVector(const arma::Col<ValueType> &original,
arma::Col<ValueType> &permuted) const {
const int dim = original.n_elem;
permuted.set_size(dim);
for (int i = 0; i < dim; ++i)
permuted(m_permutedIndices[i]) = original(i);
}
inline double ParallelKinematicMachine3PRPR::getEndEffectorPoseError(
const arma::Col<double>::fixed<3>& endEffectorPose,
const arma::Row<double>& redundantJointActuations) const {
const arma::Cube<double>::fixed<2, 3, 2>& model = getModel(endEffectorPose, redundantJointActuations);
const arma::Mat<double>::fixed<2, 3>& baseJointPositions = model.slice(1);
const arma::Mat<double>::fixed<2, 3>& endEffectorJointPositions = model.slice(1);
arma::Mat<double>::fixed<2, 3> endEffectorJointPositionsRotated = endEffectorJointPositions;
endEffectorJointPositionsRotated.each_col() -= endEffectorPose.subvec(0, 1);
const arma::Mat<double>::fixed<2, 3>& baseToEndEffectorJointPositions = endEffectorJointPositions - baseJointPositions;
const arma::Row<double>::fixed<3>& baseToEndEffectorJointActuations = arma::sqrt(arma::sum(arma::square(baseToEndEffectorJointPositions)));
if (arma::any(baseToEndEffectorJointActuations < minimalActiveJointActuations_) || arma::any(baseToEndEffectorJointActuations > maximalActiveJointActuations_)) {
return 0.0;
}
arma::Mat<double>::fixed<3, 3> forwardKinematic;
forwardKinematic.head_rows(2) = baseToEndEffectorJointPositions;
forwardKinematic.row(2) = -forwardKinematic.row(0) % endEffectorJointPositionsRotated.row(1) + forwardKinematic.row(1) % endEffectorJointPositionsRotated.row(0);
arma::Mat<double> inverseKinematic(3, 3 + redundantJointIndicies_.n_elem, arma::fill::zeros);
inverseKinematic.diag() = -baseToEndEffectorJointActuations;
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; ++n) {
const unsigned int& redundantJointIndex = redundantJointIndicies_(n);
inverseKinematic(n, 3 + n) = forwardKinematic(redundantJointIndex, 0) * redundantJointAngleCosines_(n) + forwardKinematic(redundantJointIndex, 1) * redundantJointAngleSines_(n);
}
return -1.0 / arma::cond(arma::solve(forwardKinematic.t(), inverseKinematic));
}
void unpermuteVector(const arma::Col<ValueType> &permuted,
arma::Col<ValueType> &original) const {
const int dim = permuted.n_elem;
original.set_size(dim);
for (int i = 0; i < dim; ++i)
original(i) = permuted(m_permutedIndices[i]);
}
double PolyharmonicSplineRadialBasisFunction::getPolynomialTailValueImplementation(
const arma::Col<double>& parameter,
const arma::Col<double>& polynomialCoefficients) const {
if (polynomialOrder_ > 1) {
return arma::dot(polynomialCoefficients.head(numberOfDimensions_), parameter) + polynomialCoefficients(polynomialCoefficients.n_elem - 1);
} else {
return polynomialCoefficients(polynomialCoefficients.n_elem - 1);
}
}
inline void HRectBound<MetricType, ElemType>::Center(
arma::Col<ElemType>& center) const
{
// Set size correctly if necessary.
if (!(center.n_elem == dim))
center.set_size(dim);
for (size_t i = 0; i < dim; i++)
center(i) = bounds[i].Mid();
}
void OptimisationProblem<T>::setParameterTranslation(
const arma::Col<T>& parameterTranslation) {
verify(parameterTranslation.n_elem == numberOfDimensions_, "The number of elements must be equal to the number of dimensions.");
verify(parameterTranslation.is_finite(), "All elements must be finite.");
parameterTranslation_ = parameterTranslation;
// Resets all counters and caches, as the problem could be changed.
reset();
}
Teuchos::RCP<Thyra::DefaultSpmdVector<ValueType> >
wrapInTrilinosVector(arma::Col<ValueType>& col)
{
size_t size = col.n_rows;
Teuchos::ArrayRCP<ValueType> trilinosArray =
Teuchos::arcp(col.memptr(), 0 /* lowerOffset */,
size, false /* doesn't own memory */);
typedef Thyra::DefaultSpmdVector<ValueType> TrilinosVector;
return Teuchos::RCP<TrilinosVector>(new TrilinosVector(
Thyra::defaultSpmdVectorSpace<ValueType>(size),
trilinosArray, 1 /* stride */));
}
double HMM<Distribution>::Predict(const arma::mat& dataSeq,
arma::Col<size_t>& stateSeq) const
{
// This is an implementation of the Viterbi algorithm for finding the most
// probable sequence of states to produce the observed data sequence. We
// don't use log-likelihoods to save that little bit of time, but we'll
// calculate the log-likelihood at the end of it all.
stateSeq.set_size(dataSeq.n_cols);
arma::mat logStateProb(transition.n_rows, dataSeq.n_cols);
arma::mat stateSeqBack(transition.n_rows, dataSeq.n_cols);
// Store the logs of the transposed transition matrix. This is because we
// will be using the rows of the transition matrix.
arma::mat logTrans(log(trans(transition)));
// The calculation of the first state is slightly different; the probability
// of the first state being state j is the maximum probability that the state
// came to be j from another state.
logStateProb.col(0).zeros();
for (size_t state = 0; state < transition.n_rows; state++)
{
logStateProb(state, 0) = log(initial[state] *
emission[state].Probability(dataSeq.unsafe_col(0)));
stateSeqBack(state, 0) = state;
}
// Store the best first state.
arma::uword index;
for (size_t t = 1; t < dataSeq.n_cols; t++)
{
// Assemble the state probability for this element.
// Given that we are in state j, we use state with the highest probability
// of being the previous state.
for (size_t j = 0; j < transition.n_rows; j++)
{
arma::vec prob = logStateProb.col(t - 1) + logTrans.col(j);
logStateProb(j, t) = prob.max(index) +
log(emission[j].Probability(dataSeq.unsafe_col(t)));
stateSeqBack(j, t) = index;
}
}
// Backtrack to find the most probable state sequence.
logStateProb.unsafe_col(dataSeq.n_cols - 1).max(index);
stateSeq[dataSeq.n_cols - 1] = index;
for (size_t t = 2; t <= dataSeq.n_cols; t++)
stateSeq[dataSeq.n_cols - t] =
stateSeqBack(stateSeq[dataSeq.n_cols - t + 1], dataSeq.n_cols - t + 1);
return logStateProb(stateSeq(dataSeq.n_cols - 1), dataSeq.n_cols - 1);
}
void NaiveBayesClassifier<MatType>::Classify(const MatType& data,
arma::Col<size_t>& results)
{
// Check that the number of features in the test data is same as in the
// training data.
Log::Assert(data.n_rows == means.n_rows);
arma::vec probs = arma::log(probabilities);
arma::mat invVar = 1.0 / variances;
arma::mat testProbs = arma::repmat(probs.t(), data.n_cols, 1);
results.set_size(data.n_cols); // No need to fill with anything yet.
Log::Info << "Running Naive Bayes classifier on " << data.n_cols
<< " data points with " << data.n_rows << " features each." << std::endl;
// Calculate the joint probability for each of the data points for each of the
// means.n_cols.
// Loop over every class.
for (size_t i = 0; i < means.n_cols; i++)
{
// This is an adaptation of gmm::phi() for the case where the covariance is
// a diagonal matrix.
arma::mat diffs = data - arma::repmat(means.col(i), 1, data.n_cols);
arma::mat rhs = -0.5 * arma::diagmat(invVar.col(i)) * diffs;
arma::vec exponents(diffs.n_cols);
for (size_t j = 0; j < diffs.n_cols; ++j)
exponents(j) = std::exp(arma::accu(diffs.col(j) % rhs.unsafe_col(j)));
testProbs.col(i) += log(pow(2 * M_PI, (double) data.n_rows / -2.0) *
pow(det(arma::diagmat(invVar.col(i))), -0.5) * exponents);
}
// Now calculate the label.
for (size_t i = 0; i < data.n_cols; ++i)
{
// Find the index of the class with maximum probability for this point.
arma::uword maxIndex = 0;
arma::vec pointProbs = testProbs.row(i).t();
pointProbs.max(maxIndex);
results[i] = maxIndex;
}
return;
}
bool CmpParser::readData(arma::Col<double> &data,short code){
unsigned int dim=hdr.sampSize/4;
data.resize(dim);
if (code==0){
float *p=new float;
for (int i=0;i<dim;i++){
if (file.read((char*)p,sizeof(float))==0)
return false;
SwapInt32((int*)p);
data[i]=*p;
}
delete p;
}
return true;
}
//ReadHTKHeader();
bool CmpParser::getNextFrame(arma::Col<double> &frame, int derivate){
if (file.is_open()==false)
return false;
if (hdr.sampKind&1024){//compressed mode. TODO to be implemented
std::cerr<<"HTK compressed mode is not yet supported"<<std::endl;
return false;
}
else{
unsigned int dim=hdr.sampSize/4;
arma::Col<double> data;
if (!this->readData(data)){
return false;
}
if (prev1.n_rows!=data.n_rows){
prev2=data;
if (!this->readData(prev1)){
return false;
}
if (!this->readData(data)){
return false;
}
}
if (derivate==2){
frame.resize(3*dim);
frame.subvec(0, dim-1)=prev1;
frame.subvec(dim, 2*dim-1)=(data-prev2)/2;
frame.subvec(2*dim, 3*dim-1)=prev2-2*prev1+data;
}
else if (derivate==1){
frame.resize(2*dim);
frame.subvec(0, dim-1)=prev1;
frame.subvec(dim, 2*dim-1)=(data-prev2)/2;
}
else{
frame.resize(dim);
frame=data;
}
prev2=prev1;
prev1=data;
//std::cout<<"data:"<<std::endl;
//std::cout<<frame<<std::endl;
return true;
}
}
//This function is specific to a single problem
void calculateDependentVariables(const arma::Mat<std::complex<double> >& myOffsets,
const arma::Col<std::complex<double> >& myCurrentGuess,
arma::Col<std::complex<double> >& targetsCalculated)
{
//Evaluate a dependent variable for each iteration
//The arma::Col allows this to be expressed as a vector operation
for(int i = 0; i < NUMDIMENSIONS; i++)
{
targetsCalculated[i] = arma::sum(pow(myCurrentGuess.subvec(0,1) - myOffsets.row(i).subvec(0,1).t(),2.0));
targetsCalculated[i] = targetsCalculated[i] + myCurrentGuess[2]*pow(-1.0, i) - myOffsets.row(i)[2];
//std::cout << targetsCalculated[i] << std::endl;
}
//std::cout << "model evaluated *************************" << std::endl;
//std::cout << targetsCalculated << std::endl;
//std::cout << myOffsets << std::endl;
}
void HMM<Distribution>::Generate(const size_t length,
arma::mat& dataSequence,
arma::Col<size_t>& stateSequence,
const size_t startState) const
{
// Set vectors to the right size.
stateSequence.set_size(length);
dataSequence.set_size(dimensionality, length);
// Set start state (default is 0).
stateSequence[0] = startState;
// Choose first emission state.
double randValue = math::Random();
// We just have to find where our random value sits in the probability
// distribution of emissions for our starting state.
dataSequence.col(0) = emission[startState].Random();
// Now choose the states and emissions for the rest of the sequence.
for (size_t t = 1; t < length; t++)
{
// First choose the hidden state.
randValue = math::Random();
// Now find where our random value sits in the probability distribution of
// state changes.
double probSum = 0;
for (size_t st = 0; st < transition.n_rows; st++)
{
probSum += transition(st, stateSequence[t - 1]);
if (randValue <= probSum)
{
stateSequence[t] = st;
break;
}
}
// Now choose the emission.
dataSequence.col(t) = emission[stateSequence[t]].Random();
}
}
inline arma::Cube<double>::fixed<2, 3, 2> ParallelKinematicMachine3PRPR::getModel(
const arma::Col<double>::fixed<3>& endEffectorPose,
const arma::Row<double>& redundantJointActuations) const {
verify(arma::any(arma::vectorise(redundantJointActuations < 0)) || arma::any(arma::vectorise(redundantJointActuations > 1)), "All values for the actuation of redundantion joints must be between [0, 1].");
verify(redundantJointActuations.n_elem == redundantJointIndicies_.n_elem, "The number of redundant actuations must be equal to the number of redundant joints.");
arma::Cube<double>::fixed<2, 3, 2> model;
const arma::Col<double>::fixed<2>& endEffectorPosition = endEffectorPose.subvec(0, 1);
const double& endEffectorAngle = endEffectorPose(2);
model.slice(0) = redundantJointStartPositions_;
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; n++) {
const unsigned int& redundantJointIndex = redundantJointIndicies_(n);
model.slice(0).col(redundantJointIndex) += redundantJointActuations(redundantJointIndex) * redundantJointStartToEndPositions_.col(redundantJointIndex);
}
model.slice(1) = get2DRotation(endEffectorAngle) * endEffectorJointPositions_;
model.slice(1).each_col() += endEffectorPosition;
return model;
}
void GMM<FittingType>::Classify(const arma::mat& observations,
arma::Col<size_t>& labels) const
{
// This is not the best way to do this!
// We should not have to fill this with values, because each one should be
// overwritten.
labels.set_size(observations.n_cols);
for (size_t i = 0; i < observations.n_cols; ++i)
{
// Find maximum probability component.
double probability = 0;
for (size_t j = 0; j < gaussians; ++j)
{
double newProb = Probability(observations.unsafe_col(i), j);
if (newProb >= probability)
{
probability = newProb;
labels[i] = j;
}
}
}
}
double PellegMooreKMeans<MetricType, MatType>::Iterate(
const arma::mat& centroids,
arma::mat& newCentroids,
arma::Col<size_t>& counts)
{
newCentroids.zeros(centroids.n_rows, centroids.n_cols);
counts.zeros(centroids.n_cols);
// Create rules object.
typedef PellegMooreKMeansRules<MetricType, TreeType> RulesType;
RulesType rules(dataset, centroids, newCentroids, counts, metric);
// Use single-tree traverser.
typename TreeType::template SingleTreeTraverser<RulesType> traverser(rules);
// Now, do a traversal with a fake query index (since the query index is
// irrelevant; we are checking each node with all clusters.
traverser.Traverse(0, *tree);
distanceCalculations += rules.DistanceCalculations();
// Now, calculate how far the clusters moved, after normalizing them.
double residual = 0.0;
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (counts[c] > 0)
{
newCentroids.col(c) /= counts(c);
residual += std::pow(metric.Evaluate(centroids.col(c),
newCentroids.col(c)), 2.0);
}
}
distanceCalculations += centroids.n_cols;
return std::sqrt(residual);
}
inline void
CoverTree<MetricType, StatisticType, MatType, RootPointPolicy>::CreateChildren(
arma::Col<size_t>& indices,
arma::vec& distances,
size_t nearSetSize,
size_t& farSetSize,
size_t& usedSetSize)
{
// Determine the next scale level. This should be the first level where there
// are any points in the far set. So, if we know the maximum distance in the
// distances array, this will be the largest i such that
// maxDistance > pow(base, i)
// and using this for the scale factor should guarantee we are not creating an
// implicit node. If the maximum distance is 0, every point in the near set
// will be created as a leaf, and a child to this node. We also do not need
// to change the furthestChildDistance or furthestDescendantDistance.
const ElemType maxDistance = max(distances.rows(0,
nearSetSize + farSetSize - 1));
if (maxDistance == 0)
{
// Make the self child at the lowest possible level.
// This should not modify farSetSize or usedSetSize.
size_t tempSize = 0;
children.push_back(new CoverTree(*dataset, base, point, INT_MIN, this, 0,
indices, distances, 0, tempSize, usedSetSize, *metric));
distanceComps += children.back()->DistanceComps();
// Every point in the near set should be a leaf.
for (size_t i = 0; i < nearSetSize; ++i)
{
// farSetSize and usedSetSize will not be modified.
children.push_back(new CoverTree(*dataset, base, indices[i],
INT_MIN, this, distances[i], indices, distances, 0, tempSize,
usedSetSize, *metric));
distanceComps += children.back()->DistanceComps();
usedSetSize++;
}
// The number of descendants is just the number of children, because each of
// them are leaves and contain one point.
numDescendants = children.size();
// Re-sort the dataset. We have
// [ used | far | other used ]
// and we want
// [ far | all used ].
SortPointSet(indices, distances, 0, usedSetSize, farSetSize);
return;
}
const int nextScale = std::min(scale,
(int) ceil(log(maxDistance) / log(base))) - 1;
const ElemType bound = pow(base, nextScale);
// First, make the self child. We must split the given near set into the near
// set and far set for the self child.
size_t childNearSetSize =
SplitNearFar(indices, distances, bound, nearSetSize);
// Build the self child (recursively).
size_t childFarSetSize = nearSetSize - childNearSetSize;
size_t childUsedSetSize = 0;
children.push_back(new CoverTree(*dataset, base, point, nextScale, this, 0,
indices, distances, childNearSetSize, childFarSetSize, childUsedSetSize,
*metric));
// Don't double-count the self-child (so, subtract one).
numDescendants += children[0]->NumDescendants();
// The self-child can't modify the furthestChildDistance away from 0, but it
// can modify the furthestDescendantDistance.
furthestDescendantDistance = children[0]->FurthestDescendantDistance();
// Remove any implicit nodes we may have created.
RemoveNewImplicitNodes();
distanceComps += children[0]->DistanceComps();
// Now the arrays, in memory, look like this:
// [ childFar | childUsed | far | used ]
// but we need to move the used points past our far set:
// [ childFar | far | childUsed + used ]
// and keeping in mind that childFar = our near set,
// [ near | far | childUsed + used ]
// is what we are trying to make.
SortPointSet(indices, distances, childFarSetSize, childUsedSetSize,
farSetSize);
// Update size of near set and used set.
nearSetSize -= childUsedSetSize;
usedSetSize += childUsedSetSize;
// Now for each point in the near set, we need to make children. To save
// computation later, we'll create an array holding the points in the near
// set, and then after each run we'll check which of those (if any) were used
// and we will remove them. ...if that's faster. I think it is.
while (nearSetSize > 0)
{
size_t newPointIndex = nearSetSize - 1;
// Swap to front if necessary.
if (newPointIndex != 0)
{
const size_t tempIndex = indices[newPointIndex];
const ElemType tempDist = distances[newPointIndex];
indices[newPointIndex] = indices[0];
distances[newPointIndex] = distances[0];
indices[0] = tempIndex;
distances[0] = tempDist;
}
// Will this be a new furthest child?
if (distances[0] > furthestDescendantDistance)
furthestDescendantDistance = distances[0];
// If there's only one point left, we don't need this crap.
if ((nearSetSize == 1) && (farSetSize == 0))
{
size_t childNearSetSize = 0;
children.push_back(new CoverTree(*dataset, base, indices[0], nextScale,
this, distances[0], indices, distances, childNearSetSize, farSetSize,
usedSetSize, *metric));
distanceComps += children.back()->DistanceComps();
numDescendants += children.back()->NumDescendants();
// Because the far set size is 0, we don't have to do any swapping to
// move the point into the used set.
++usedSetSize;
--nearSetSize;
// And we're done.
break;
}
// Create the near and far set indices and distance vectors. We don't fill
// in the self-point, yet.
arma::Col<size_t> childIndices(nearSetSize + farSetSize);
childIndices.rows(0, (nearSetSize + farSetSize - 2)) = indices.rows(1,
nearSetSize + farSetSize - 1);
arma::vec childDistances(nearSetSize + farSetSize);
// Build distances for the child.
ComputeDistances(indices[0], childIndices, childDistances, nearSetSize
+ farSetSize - 1);
// Split into near and far sets for this point.
childNearSetSize = SplitNearFar(childIndices, childDistances, bound,
nearSetSize + farSetSize - 1);
childFarSetSize = PruneFarSet(childIndices, childDistances,
base * bound, childNearSetSize,
(nearSetSize + farSetSize - 1));
// Now that we know the near and far set sizes, we can put the used point
// (the self point) in the correct place; now, when we call
// MoveToUsedSet(), it will move the self-point correctly. The distance
// does not matter.
childIndices(childNearSetSize + childFarSetSize) = indices[0];
childDistances(childNearSetSize + childFarSetSize) = 0;
// Build this child (recursively).
childUsedSetSize = 1; // Mark self point as used.
children.push_back(new CoverTree(*dataset, base, indices[0], nextScale,
this, distances[0], childIndices, childDistances, childNearSetSize,
childFarSetSize, childUsedSetSize, *metric));
numDescendants += children.back()->NumDescendants();
// Remove any implicit nodes.
RemoveNewImplicitNodes();
distanceComps += children.back()->DistanceComps();
// Now with the child created, it returns the childIndices and
// childDistances vectors in this form:
// [ childFar | childUsed ]
// For each point in the childUsed set, we must move that point to the used
// set in our own vector.
MoveToUsedSet(indices, distances, nearSetSize, farSetSize, usedSetSize,
childIndices, childFarSetSize, childUsedSetSize);
}
// Calculate furthest descendant.
for (size_t i = (nearSetSize + farSetSize); i < (nearSetSize + farSetSize +
usedSetSize); ++i)
if (distances[i] > furthestDescendantDistance)
furthestDescendantDistance = distances[i];
}
SCENARIO("bbob::WeierstrassFunction.objectiveFunction_", "[bbob::WeierstrassFunction][bbob::WeierstrassFunction.objectiveFunction_]") {
GIVEN("Some parameters") {
// All expected objective values where generated for a 40-dimensional problem instance.
const arma::uword numberOfDimensions = 40;
CAPTURE(numberOfDimensions);
TestWeierstrassFunction optimisationProblem(numberOfDimensions);
REQUIRE(optimisationProblem.rotationR_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_1.input"));
REQUIRE(optimisationProblem.rotationQ_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_2.input"));
arma::Mat<double> parameters;
REQUIRE(parameters.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_parameters_40x100.input"));
THEN("Return their objective value") {
arma::Col<double> expectedObjectiveValues;
REQUIRE(expectedObjectiveValues.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/weierstrassFunction_dim40_1x100.expected"));
for (arma::uword n = 0; n < parameters.n_cols; ++n) {
CHECK(optimisationProblem.objectiveFunction_(parameters.col(n)) == Approx(expectedObjectiveValues(n)));
}
}
}
}
SCENARIO("bbob::WeierstrassFunction.getNormalisedObjectiveValue", "[bbob::WeierstrassFunction][bbob::WeierstrassFunction.getNormalisedObjectiveValue]") {
GIVEN("A parameter") {
const arma::uword numberOfDimensions = SYNCHRONISED(1 + discreteRandomNumber());
CAPTURE(numberOfDimensions);
const arma::Col<double>& parameter = arma::normalise(continuousRandomNumbers(numberOfDimensions));
arma::Col<T> OptimisationProblem<T>::getDiversifiedParameter(
const arma::Col<T>& parameter) const noexcept {
assert(parameter.n_elem == numberOfDimensions_);
return parameterRotation_ * (parameterScaling_ % parameter.elem(parameterPermutation_) - parameterTranslation_);
}
arma::Col<double> polynomial(
const arma::Col<double>& parameter,
const arma::uword polynomialOrder) {
arma::Col<double> polynomial(polynomialSize(parameter.n_elem, polynomialOrder));
// For any polynomial, all constant terms are 1.
switch (polynomialOrder) {
case 0: { // Constant polynomials
// Constant term
polynomial(0) = 1.0;
}
break;
case 1: { // Linear polynomials
// Linear term
polynomial.head(parameter.n_elem) = parameter;
// Constant term
polynomial(parameter.n_elem) = 1;
}
break;
case 2: { // Quadratic polynomials
// Quadratic term
arma::uword n = 0;
for (arma::uword k = 0; k < parameter.n_elem; ++k) {
for (arma::uword l = k; l < parameter.n_elem; ++l) {
polynomial(n++) = parameter(k) * parameter(l);
}
}
// Linear term
polynomial.subvec(n, n + parameter.n_elem - 1) = parameter;
// Constant term
polynomial(polynomial.n_elem - 1) = 1;
}
break;
case 3: { // Cubic polynomials
// Cubic term
arma::uword n = 0;
for (arma::uword k = 0; k < parameter.n_elem; ++k) {
for (arma::uword l = k; l < parameter.n_elem; ++l) {
for (arma::uword m = l; m < parameter.n_elem; ++m) {
polynomial(n++) = parameter(k) * parameter(l) * parameter(m);
}
}
}
// Quadratic term
for (arma::uword k = 0; k < parameter.n_elem; ++k) {
for (arma::uword l = k; l < parameter.n_elem; ++l) {
polynomial(n++) = parameter(k) * parameter(l);
}
}
// Linear term
polynomial.subvec(n, n + parameter.n_elem - 1) = parameter;
// Constant term
polynomial(polynomial.n_elem - 1) = 1;
}
break;
default: { // Polynomials of degree >= 4
// All terms, expect the linear and constant one.
arma::uword n = 0;
// Generates the term for all degrees > 1
for (arma::uword d = 2; d <= polynomialOrder; ++d) {
for (const auto& multicombination : multicombinations(parameter.n_elem, d)) {
polynomial(n++) = arma::prod(parameter.elem(multicombination));
}
}
// Linear term
polynomial.subvec(n, n + parameter.n_elem - 1) = parameter;
// Constant term
polynomial(polynomial.n_elem - 1) = 1;
}
break;
}
return polynomial;
}
bool SpillTree<MetricType, StatisticType, MatType, HyperplaneType, SplitType>::
SplitPoints(const double tau,
const double rho,
const arma::Col<size_t>& points,
arma::Col<size_t>& leftPoints,
arma::Col<size_t>& rightPoints)
{
arma::vec projections(points.n_elem);
size_t left = 0, right = 0, leftFrontier = 0, rightFrontier = 0;
// Count the number of points to the left/right of the splitting hyperplane.
for (size_t i = 0; i < points.n_elem; i++)
{
// Store projection value for future use.
projections[i] = hyperplane.Project(dataset->col(points[i]));
if (projections[i] <= 0)
{
left++;
if (projections[i] > -tau)
leftFrontier++;
}
else
{
right++;
if (projections[i] < tau)
rightFrontier++;
}
}
const double p1 = double (left + rightFrontier) / points.n_elem;
const double p2 = double (right + leftFrontier) / points.n_elem;
if ((p1 <= rho || rightFrontier == 0) &&
(p2 <= rho || leftFrontier == 0))
{
// Perform the actual splitting considering the overlapping buffer. Points
// with projection value in the range (-tau, tau) are included in both,
// leftPoints and rightPoints.
leftPoints.resize(left + rightFrontier);
rightPoints.resize(right + leftFrontier);
for (size_t i = 0, rc = 0, lc = 0; i < points.n_elem; i++)
{
if (projections[i] < tau || projections[i] <= 0)
leftPoints[lc++] = points[i];
if (projections[i] > -tau)
rightPoints[rc++] = points[i];
}
// Return true, because it is a overlapping node.
return true;
}
// Perform the actual splitting ignoring the overlapping buffer. Points
// with projection value less than or equal to zero are included in leftPoints
// and points with projection value greater than zero are included in
// rightPoints.
leftPoints.resize(left);
rightPoints.resize(right);
for (size_t i = 0, rc = 0, lc = 0; i < points.n_elem; i++)
{
if (projections[i] <= 0)
leftPoints[lc++] = points[i];
else
rightPoints[rc++] = points[i];
}
// Return false, because it isn't a overlapping node.
return false;
}
double DualTreeKMeans<MetricType, MatType, TreeType>::Iterate(
const arma::mat& centroids,
arma::mat& newCentroids,
arma::Col<size_t>& counts)
{
newCentroids.zeros(centroids.n_rows, centroids.n_cols);
counts.zeros(centroids.n_cols);
if (clusterDistances.n_elem != centroids.n_cols + 1)
{
clusterDistances.set_size(centroids.n_cols + 1);
clusterDistances.fill(DBL_MAX / 2.0); // To prevent overflow.
}
// Build a tree on the centroids.
std::vector<size_t> oldFromNewCentroids;
TreeType* centroidTree = BuildTree<TreeType>(
const_cast<typename TreeType::Mat&>(centroids), oldFromNewCentroids);
// Now run the dual-tree algorithm.
typedef DualTreeKMeansRules<MetricType, TreeType> RulesType;
RulesType rules(dataset, centroids, newCentroids, counts, oldFromNewCentroids,
iteration, clusterDistances, distances, assignments, distanceIteration,
metric);
// Use the dual-tree traverser.
//typename TreeType::template DualTreeTraverser<RulesType> traverser(rules);
typename TreeType::template BreadthFirstDualTreeTraverser<RulesType>
traverser(rules);
traverser.Traverse(*centroidTree, *tree);
distanceCalculations += rules.DistanceCalculations();
// Now, calculate how far the clusters moved, after normalizing them.
double residual = 0.0;
clusterDistances.zeros();
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (counts[c] == 0)
{
newCentroids.col(c).fill(DBL_MAX); // Should have happened anyway I think.
}
else
{
const size_t oldCluster = oldFromNewCentroids[c];
newCentroids.col(oldCluster) /= counts(oldCluster);
const double dist = metric.Evaluate(centroids.col(c),
newCentroids.col(oldCluster));
if (dist > clusterDistances[centroids.n_cols])
clusterDistances[centroids.n_cols] = dist;
clusterDistances[oldCluster] = dist;
residual += std::pow(dist, 2.0);
}
}
Log::Info << clusterDistances.t();
delete centroidTree;
++iteration;
return std::sqrt(residual);
}
// Increases the visibility of the internal objective function, to bypass general modification, made by the parent class.
using mant::bbob::RosenbrockFunctionRotated::objectiveFunction_;
};
SCENARIO("bbob::RosenbrockFunctionRotated.objectiveFunction_", "[bbob::RosenbrockFunctionRotated][bbob::RosenbrockFunctionRotated.objectiveFunction_]") {
GIVEN("Some parameters") {
// All expected objective values where generated for a 40-dimensional problem instance.
const arma::uword numberOfDimensions = 40;
CAPTURE(numberOfDimensions);
TestRosenbrockFunctionRotated optimisationProblem(numberOfDimensions);
arma::Mat<double> parameters;
REQUIRE(parameters.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_parameters_40x100.input"));
THEN("Return their objective value") {
arma::Col<double> expectedObjectiveValues;
REQUIRE(expectedObjectiveValues.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/rosenbrockFunctionRotated_dim40_1x100.expected"));
for (arma::uword n = 0; n < parameters.n_cols; ++n) {
CHECK(optimisationProblem.objectiveFunction_(parameters.col(n)) == Approx(expectedObjectiveValues(n)));
}
}
}
}
SCENARIO("bbob::RosenbrockFunctionRotated.getNormalisedObjectiveValue", "[bbob::RosenbrockFunctionRotated][bbob::RosenbrockFunctionRotated.getNormalisedObjectiveValue]") {
GIVEN("A parameter") {
const arma::uword numberOfDimensions = SYNCHRONISED(1 + discreteRandomNumber());
CAPTURE(numberOfDimensions);
const arma::Col<double>& parameter = arma::normalise(continuousRandomNumbers(numberOfDimensions));
// Evaluate the function at the point "point" and store result in
// the array "result"
// Note that the source is located inside the inner sphere
inline void evaluate(const arma::Col<CoordinateType>& point,
arma::Col<ValueType>& result) const {
result.fill(0.);
}
};
SCENARIO("bbob::SchaffersF7FunctionIllConditioned.objectiveFunction_", "[bbob::SchaffersF7FunctionIllConditioned][bbob::SchaffersF7FunctionIllConditioned.objectiveFunction_]") {
GIVEN("Some parameters") {
// All expected objective values where generated for a 40-dimensional problem instance.
const arma::uword numberOfDimensions = 40;
CAPTURE(numberOfDimensions);
TestSchaffersF7FunctionIllConditioned optimisationProblem(numberOfDimensions);
REQUIRE(optimisationProblem.rotationQ_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_2.input"));
arma::Mat<double> parameters;
REQUIRE(parameters.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_parameters_40x100.input"));
THEN("Return their objective value") {
arma::Col<double> expectedObjectiveValues;
REQUIRE(expectedObjectiveValues.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/schaffersF7FunctionIllConditioned_dim40_1x100.expected"));
for (arma::uword n = 0; n < parameters.n_cols; ++n) {
CHECK(optimisationProblem.objectiveFunction_(parameters.col(n)) == Approx(expectedObjectiveValues(n)));
}
}
}
}
SCENARIO("bbob::SchaffersF7FunctionIllConditioned.getNormalisedObjectiveValue", "[bbob::SchaffersF7FunctionIllConditioned][bbob::SchaffersF7FunctionIllConditioned.getNormalisedObjectiveValue]") {
GIVEN("A parameter") {
const arma::uword numberOfDimensions = SYNCHRONISED(1 + discreteRandomNumber());
CAPTURE(numberOfDimensions);
const arma::Col<double>& parameter = arma::normalise(continuousRandomNumbers(numberOfDimensions));
TestWeierstrassFunction weierstrassFunction(40);
SECTION(".getObjectiveValueImplementation") {
SECTION("Returns the objective value") {
arma::Mat<double> rotationR;
REQUIRE(rotationR.load(testDirectory + "/data/optimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_1.input"));
weierstrassFunction.setRotationR(rotationR);
arma::Mat<double> rotationQ;
REQUIRE(rotationQ.load(testDirectory + "/data/optimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_2.input"));
weierstrassFunction.setRotationQ(rotationQ);
arma::Mat<double> parameters;
REQUIRE(parameters.load(testDirectory + "/data/optimisationProblem/blackBoxOptimisationBenchmark/_parameters_40x100.input"));
arma::Col<double> expectedObjectiveValues;
REQUIRE(expectedObjectiveValues.load(testDirectory + "/data/optimisationProblem/blackBoxOptimisationBenchmark/bbob_weierstrassFunction_dim40_1x100.expected"));
for (arma::uword n = 0; n < parameters.n_cols; ++n) {
CHECK(weierstrassFunction.getObjectiveValueImplementation(parameters.col(n)) == Approx(expectedObjectiveValues(n)));
}
}
}
SECTION(".toString") {
SECTION("Returns a (filesystem friendly) name for the class.") {
CHECK(weierstrassFunction.toString() == "bbob_weierstrass_function");
}
}
}
SCENARIO("bbob::GallaghersGaussian101mePeaksFunction.objectiveFunction_", "[bbob::GallaghersGaussian101mePeaksFunction][bbob::GallaghersGaussian101mePeaksFunction.objectiveFunction_]") {
GIVEN("Some parameters") {
// All expected objective values where generated for a 40-dimensional problem instance.
const arma::uword numberOfDimensions = 40;
CAPTURE(numberOfDimensions);
TestGallaghersGaussian101mePeaksFunction optimisationProblem(numberOfDimensions);
REQUIRE(optimisationProblem.rotationQ_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_rotationMatrix_40x40_1.input"));
REQUIRE(optimisationProblem.localParameterConditionings_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_localParameterConditionings_40x101.input"));
REQUIRE(optimisationProblem.localParameterTranslations_.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_localParameterTranslations_40x101.input"));
arma::Mat<double> parameters;
REQUIRE(parameters.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/_parameters_40x100.input"));
THEN("Return their objective value") {
arma::Col<double> expectedObjectiveValues;
REQUIRE(expectedObjectiveValues.load(::rootTestDataDirectory + "/optimisationProblem/benchmarkOptimisationProblem/blackBoxOptimisationBenchmark/gallaghersGaussian101mePeaksFunction_dim40_1x100.expected"));
for (arma::uword n = 0; n < parameters.n_cols; ++n) {
CHECK(optimisationProblem.objectiveFunction_(parameters.col(n)) == Approx(expectedObjectiveValues(n)));
}
}
}
}
SCENARIO("bbob::GallaghersGaussian101mePeaksFunction.getNormalisedObjectiveValue", "[bbob::GallaghersGaussian101mePeaksFunction][bbob::GallaghersGaussian101mePeaksFunction.getNormalisedObjectiveValue]") {
GIVEN("A parameter") {
const arma::uword numberOfDimensions = SYNCHRONISED(1 + discreteRandomNumber());
CAPTURE(numberOfDimensions);
const arma::Col<double>& parameter = arma::normalise(continuousRandomNumbers(numberOfDimensions));
