void Split(const arma::Mat<T>& input,
const arma::Row<U>& inputLabel,
arma::Mat<T>& trainData,
arma::Mat<T>& testData,
arma::Row<U>& trainLabel,
arma::Row<U>& testLabel,
const double testRatio)
{
const size_t testSize = static_cast<size_t>(input.n_cols * testRatio);
const size_t trainSize = input.n_cols - testSize;
trainData.set_size(input.n_rows, trainSize);
testData.set_size(input.n_rows, testSize);
trainLabel.set_size(trainSize);
testLabel.set_size(testSize);
const arma::Col<size_t> order =
arma::shuffle(arma::linspace<arma::Col<size_t>>(0, input.n_cols - 1,
input.n_cols));
for (size_t i = 0; i != trainSize; ++i)
{
trainData.col(i) = input.col(order[i]);
trainLabel(i) = inputLabel(order[i]);
}
for (size_t i = 0; i != testSize; ++i)
{
testData.col(i) = input.col(order[i + trainSize]);
testLabel(i) = inputLabel(order[i + trainSize]);
}
}
arma::Col<double> compute_column_rms(const arma::Mat<double>& data) {
const long n_cols = data.n_cols;
arma::Col<double> rms(n_cols);
for (long i=0; i<n_cols; ++i) {
const double dot = arma::dot(data.col(i), data.col(i));
rms(i) = std::sqrt(dot / (data.col(i).n_rows-1));
}
return std::move(rms);
}
void enforce_positive_sign_by_column(arma::Mat<double>& data) {
for (long i=0; i<long(data.n_cols); ++i) {
const double max = arma::max(data.col(i));
const double min = arma::min(data.col(i));
bool change_sign = false;
if (std::abs(max)>=std::abs(min)) {
if (max<0) change_sign = true;
} else {
if (min<0) change_sign = true;
}
if (change_sign) data.col(i) *= -1;
}
}
void calculateJacobian(const arma::Mat<std::complex<double> >& myOffsets,
arma::Mat<double>& myJacobian,
arma::Col<std::complex<double> >& myTargetsCalculated,
arma::Col<std::complex<double> >& myCurrentGuess,
void myCalculateDependentVariables(const arma::Mat<std::complex<double> >&, const arma::Col<std::complex<double> >&, arma::Col<std::complex<double> >&))
{
//Calculate a temporary, unperturbed target evaluation, such as is needed for solving for the updated guess
//formula
arma::Col<std::complex<double> > unperturbedTargetsCalculated(NUMDIMENSIONS);
unperturbedTargetsCalculated.fill(0.0);
myCalculateDependentVariables(myOffsets, myCurrentGuess, unperturbedTargetsCalculated);
std::complex<double> oldGuessValue(0.0, 0.0);
//Each iteration fills a column in the Jacobian
//The Jacobian takes this form:
//
// dF0/dx0 dF0/dx1
// dF1/dx0 dF1/dx1
//
for(int j = 0; j< NUMDIMENSIONS; j++)
{
//Store old element value, perturb the current value
oldGuessValue = myCurrentGuess[j];
myCurrentGuess[j] += std::complex<double>(0.0, PROBEDISTANCE);
//Evaluate functions for perturbed guess
myCalculateDependentVariables(myOffsets, myCurrentGuess, myTargetsCalculated);
//The column of the Jacobian that goes with the independent variable we perturbed
//can be determined using the finite-difference formula
//The arma::Col allows this to be expressed as a single vector operation
//note slice works as: std::slice(start_index, number_of_elements_to_access, index_interval_between_selections)
//std::cout << "Jacobian column " << j << " with:" << std::endl;
//std::cout << "myTargetsCalculated" << std::endl;
//std::cout << myTargetsCalculated << std::endl;
//std::cout << "unperturbedTargetsCalculated" << std::endl;
//std::cout << unperturbedTargetsCalculated << std::endl;
myJacobian.col(j) = arma::imag(myTargetsCalculated);
myJacobian.col(j) *= pow(PROBEDISTANCE, -1.0);
//std::cout << "The jacobian: " << std::endl;
//std::cout << myJacobian << std::endl;
myCurrentGuess[j] = oldGuessValue;
}
//Reset to unperturbed, so we dont waste a function evaluation
myTargetsCalculated = unperturbedTargetsCalculated;
}
void Backward(const arma::Cube<eT>& input,
const arma::Mat<eT>& gy,
arma::Cube<eT>& g)
{
// Generate a cube using the backpropagated error matrix.
arma::Cube<eT> mappedError = arma::zeros<arma::cube>(input.n_rows,
input.n_cols, input.n_slices);
for (size_t s = 0, j = 0; s < mappedError.n_slices; s+= gy.n_cols, j++)
{
for (size_t i = 0; i < gy.n_cols; i++)
{
arma::Col<eT> temp = gy.col(i).subvec(
j * input.n_rows * input.n_cols,
(j + 1) * input.n_rows * input.n_cols - 1);
mappedError.slice(s + i) = arma::Mat<eT>(temp.memptr(),
input.n_rows, input.n_cols);
}
}
arma::Cube<eT> derivative;
Deriv(input, derivative);
g = mappedError % derivative;
}
inline ParallelKinematicMachine3PUPS::ParallelKinematicMachine3PUPS() noexcept {
setMinimalActiveJointActuations({0.39, 0.39, 0.39});
setMaximalActiveJointActuations({0.95, 0.95, 0.95});
setEndEffectorJointPositions({
-0.025561381023353, 0.086293776138137, 0.12,
0.087513292835791, -0.021010082747031, 0.12,
-0.061951911812438, -0.065283693391106, 0.12});
setRedundantJointStartPositions({
-0.463708870031622, 0.417029254828353, -0.346410161513775,
0.593012363818459, 0.193069033993384, -0.346410161513775,
-0.129303493786837, -0.610098288821738, -0.346410161513775});
setRedundantJointEndPositions({
-0.247202519085512, 0.292029254828353, 0.086602540378444,
0.376506012872349, 0.068069033993384, 0.086602540378444,
-0.129303493786837, -0.360098288821738, 0.086602540378444});
redundantJointStartToEndPositions_ = redundantJointEndPositions_ - redundantJointStartPositions_;
redundantJointIndicies_ = arma::find(arma::any(redundantJointStartToEndPositions_));
redundantJointAngles_.set_size(3, redundantJointIndicies_.n_elem);
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; ++n) {
const double& redundantJointXAngle = std::atan2(redundantJointStartToEndPositions_(1, n), redundantJointStartToEndPositions_(0, n));
const double& redundantJointYAngle = std::atan2(redundantJointStartToEndPositions_(2, n), redundantJointStartToEndPositions_(1, n));
redundantJointAngles_.col(n) = arma::Col<double>::fixed<3>({std::cos(redundantJointXAngle) * std::cos(redundantJointYAngle), std::sin(redundantJointXAngle) * std::cos(redundantJointYAngle), std::sin(redundantJointYAngle)});
}
}
arma::Col<double> compute_column_means(const arma::Mat<double>& data) {
const long n_cols = data.n_cols;
arma::Col<double> means(n_cols);
for (long i=0; i<n_cols; ++i)
means(i) = arma::mean(data.col(i));
return std::move(means);
}
void FeedBackward(const arma::Cube<eT>& inputActivation,
const arma::Mat<eT>& error,
arma::Cube<eT>& delta)
{
delta = delta % mask * scale;
// Generate a cube from the error matrix.
arma::Cube<eT> mappedError = arma::zeros<arma::cube>(inputActivation.n_rows,
inputActivation.n_cols, inputActivation.n_slices);
for (size_t s = 0, j = 0; s < mappedError.n_slices; s+= error.n_cols, j++)
{
for (size_t i = 0; i < error.n_cols; i++)
{
arma::Col<eT> temp = error.col(i).subvec(
j * inputActivation.n_rows * inputActivation.n_cols,
(j + 1) * inputActivation.n_rows * inputActivation.n_cols - 1);
mappedError.slice(s + i) = arma::Mat<eT>(temp.memptr(),
inputActivation.n_rows, inputActivation.n_cols);
}
}
delta = mappedError;
}
arma::Row<T> column_apply(const arma::Mat<T>& matrix, Functor functor) {
arma::Row<T> result(matrix.n_cols);
std::size_t index = 0;
std::generate(result.begin(), result.end(), [&matrix, &index, &functor] {
return functor(matrix.col(index++));
});
return result;
}
void normalize_by_column(arma::Mat<double>& data, const arma::Col<double>& rms) {
if (data.n_cols != rms.n_elem)
throw std::range_error("Number of elements of rms is not equal to the number of columns of data");
for (long i=0; i<long(data.n_cols); ++i) {
if (rms(i)==0)
throw std::runtime_error("At least one of the entries of rms equals to zero");
data.col(i) *= 1./rms(i);
}
}
// Predict the rating for a group of user/item combinations.
void CF::Predict(const arma::Mat<size_t>& combinations,
arma::vec& predictions) const
{
// First, for nearest neighbor search, stretch the H matrix.
arma::mat l = arma::chol(w.t() * w);
arma::mat stretchedH = l * h; // Due to the Armadillo API, l is L^T.
// Now, we must determine those query indices we need to find the nearest
// neighbors for. This is easiest if we just sort the combinations matrix.
arma::Mat<size_t> sortedCombinations(combinations.n_rows,
combinations.n_cols);
arma::uvec ordering = arma::sort_index(combinations.row(0).t());
for (size_t i = 0; i < ordering.n_elem; ++i)
sortedCombinations.col(i) = combinations.col(ordering[i]);
// Now, we have to get the list of unique users we will be searching for.
arma::Col<size_t> users = arma::unique(combinations.row(0).t());
// Assemble our query matrix from the stretchedH matrix.
arma::mat queries(stretchedH.n_rows, users.n_elem);
for (size_t i = 0; i < queries.n_cols; ++i)
queries.col(i) = stretchedH.col(users[i]);
// Now calculate the neighborhood of these users.
neighbor::KNN a(stretchedH);
arma::mat distances;
arma::Mat<size_t> neighborhood;
a.Search(queries, numUsersForSimilarity, neighborhood, distances);
// Now that we have the neighborhoods we need, calculate the predictions.
predictions.set_size(combinations.n_cols);
size_t user = 0; // Cumulative user count, because we are doing it in order.
for (size_t i = 0; i < sortedCombinations.n_cols; ++i)
{
// Could this be made faster by calculating dot products for multiple items
// at once?
double rating = 0.0;
// Map the combination's user to the user ID used for kNN.
while (users[user] < sortedCombinations(0, i))
++user;
for (size_t j = 0; j < neighborhood.n_rows; ++j)
rating += arma::as_scalar(w.row(sortedCombinations(1, i)) *
h.col(neighborhood(j, user)));
rating /= neighborhood.n_rows;
predictions(ordering[i]) = rating;
}
}
int
DynamicsTrappenberg::update_y(
const arma::Mat< double > x,
const arma::Mat< double > u,
const mxArray *theta,
const mxArray *ptheta,
arma::Mat< double > &y)
{
y = x.col(INDEX_NODES);
return 0;
}
/**
* Given the sufficient statistics of a proposed split, calculate the
* information gain if that split was to be used. The 'counts' matrix should
* contain the number of points in each class in each column, so the size of
* 'counts' is children x classes, where 'children' is the number of child
* nodes in the proposed split.
*
* @param counts Matrix of sufficient statistics.
*/
static double Evaluate(const arma::Mat<size_t>& counts)
{
// Calculate the number of elements in the unsplit node and also in each
// proposed child.
size_t numElem = 0;
arma::vec splitCounts(counts.n_elem);
for (size_t i = 0; i < counts.n_cols; ++i)
{
splitCounts[i] = arma::accu(counts.col(i));
numElem += splitCounts[i];
}
// Corner case: if there are no elements, the gain is zero.
if (numElem == 0)
return 0.0;
arma::Col<size_t> classCounts = arma::sum(counts, 1);
// Calculate the gain of the unsplit node.
double gain = 0.0;
for (size_t i = 0; i < classCounts.n_elem; ++i)
{
const double f = ((double) classCounts[i] / (double) numElem);
if (f > 0.0)
gain -= f * std::log2(f);
}
// Now calculate the impurity of the split nodes and subtract them from the
// overall gain.
for (size_t i = 0; i < counts.n_cols; ++i)
{
if (splitCounts[i] > 0)
{
double splitGain = 0.0;
for (size_t j = 0; j < counts.n_rows; ++j)
{
const double f = ((double) counts(j, i) / (double) splitCounts[i]);
if (f > 0.0)
splitGain += f * std::log2(f);
}
gain += ((double) splitCounts[i] / (double) numElem) * splitGain;
}
}
return gain;
}
static double Evaluate(const arma::Mat<size_t>& counts)
{
// We need to sum over the difference between the un-split node and the
// split nodes. First we'll calculate the number of elements in each split
// and total.
size_t numElem = 0;
arma::vec splitCounts(counts.n_cols);
for (size_t i = 0; i < counts.n_cols; ++i)
{
splitCounts[i] = arma::accu(counts.col(i));
numElem += splitCounts[i];
}
// Corner case: if there are no elements, the impurity is zero.
if (numElem == 0)
return 0.0;
arma::Col<size_t> classCounts = arma::sum(counts, 1);
// Calculate the Gini impurity of the un-split node.
double impurity = 0.0;
for (size_t i = 0; i < classCounts.n_elem; ++i)
{
const double f = ((double) classCounts[i] / (double) numElem);
impurity += f * (1.0 - f);
}
// Now calculate the impurity of the split nodes and subtract them from the
// overall impurity.
for (size_t i = 0; i < counts.n_cols; ++i)
{
if (splitCounts[i] > 0)
{
double splitImpurity = 0.0;
for (size_t j = 0; j < counts.n_rows; ++j)
{
const double f = ((double) counts(j, i) / (double) splitCounts[i]);
splitImpurity += f * (1.0 - f);
}
impurity -= ((double) splitCounts[i] / (double) numElem) *
splitImpurity;
}
}
return impurity;
}
arma::Mat< double >
DynamicsTrappenberg::sigma(
const arma::Mat< double > x,
const arma::Mat< double > u,
const mxArray *theta,
const mxArray *ptheta)
{
arma::Mat< double > beta(mxGetPr(mxGetField(theta, 0, "beta")),
x.n_rows, 1, 1);
arma::Mat< double > baseline(mxGetPr(mxGetField(theta, 0, "theta")),
x.n_rows, 1, 1);
arma::Mat< double > dx = 1/(1 + exp(-x.col(INDEX_NODES) % beta +
baseline));
return dx;
}
/**
* Convert the features of images into histogram
* @param features features of the image
* @param code_book code book of the data sets
* @return histogram of bovw
*/
Hist describe(arma::Mat<T> const &features,
arma::Mat<T> const &code_book) const
{
arma::Mat<T> dist(features.n_cols, code_book.n_cols);
for(arma::uword i = 0; i != features.n_cols; ++i){
dist.row(i) = euclidean_dist(features.col(i),
code_book);
}
//dist.print("dist");
Hist hist = create_hist(dist.n_cols,
armd::is_two_dim<Hist>::type());
for(arma::uword i = 0; i != dist.n_rows; ++i){
arma::uword min_idx;
dist.row(i).min(min_idx);
++hist(min_idx);
}
//hist.print("\nhist");
return hist;
}
void DefaultLocalAssemblerForOperatorsOnSurfacesUtilities<BasisFunctionType>::
precalculateElementSizesAndCentersForSingleGrid(
const RawGridGeometry<CoordinateType> &rawGeometry,
std::vector<CoordinateType> &elementSizesSquared,
arma::Mat<CoordinateType> &elementCenters,
CoordinateType &averageElementSize) {
const size_t elementCount = rawGeometry.elementCount();
const int worldDim = rawGeometry.worldDimension();
averageElementSize = 0.; // We will store here temporarily
// the sum of element sizes
elementSizesSquared.resize(elementCount);
for (int e = 0; e < elementCount; ++e) {
elementSizesSquared[e] = elementSizeSquared(e, rawGeometry);
averageElementSize += sqrt(elementSizesSquared[e]);
}
averageElementSize /= elementCount;
elementCenters.set_size(worldDim, elementCount);
for (int e = 0; e < elementCount; ++e)
elementCenters.col(e) = elementCenter(e, rawGeometry);
}
void Backward(const arma::Cube<eT>& /* unused */,
const arma::Mat<eT>& gy,
arma::Cube<eT>& g)
{
// Generate a cube from the error matrix.
arma::Cube<eT> mappedError = arma::zeros<arma::cube>(outputParameter.n_rows,
outputParameter.n_cols, outputParameter.n_slices);
for (size_t s = 0, j = 0; s < mappedError.n_slices; s+= gy.n_cols, j++)
{
for (size_t i = 0; i < gy.n_cols; i++)
{
arma::Col<eT> temp = gy.col(i).subvec(
j * outputParameter.n_rows * outputParameter.n_cols,
(j + 1) * outputParameter.n_rows * outputParameter.n_cols - 1);
mappedError.slice(s + i) = arma::Mat<eT>(temp.memptr(),
outputParameter.n_rows, outputParameter.n_cols);
}
}
Backward(inputParameter, mappedError, g);
}
void RAModel<SortPolicy>::Search(arma::mat&& querySet,
const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
// Apply the random basis if necessary.
if (randomBasis)
querySet = q * querySet;
Log::Info << "Searching for " << k << " approximate nearest neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree rank-approximate " << TreeName() << " search...";
else if (!Naive())
Log::Info << "single-tree rank-approximate " << TreeName() << " search...";
else
Log::Info << "brute-force (naive) rank-approximate search...";
Log::Info << std::endl;
switch (treeType)
{
case KD_TREE:
if (!kdTreeRA->Naive() && !kdTreeRA->SingleMode())
{
// Build a second tree and search.
Timer::Start("tree_building");
Log::Info << "Building query tree..." << std::endl;
std::vector<size_t> oldFromNewQueries;
typename RAType<tree::KDTree>::Tree queryTree(std::move(querySet),
oldFromNewQueries, leafSize);
Log::Info << "Tree built." << std::endl;
Timer::Stop("tree_building");
arma::Mat<size_t> neighborsOut;
arma::mat distancesOut;
kdTreeRA->Search(&queryTree, k, neighborsOut, distancesOut);
// Unmap the query points.
distances.set_size(distancesOut.n_rows, distancesOut.n_cols);
neighbors.set_size(neighborsOut.n_rows, neighborsOut.n_cols);
for (size_t i = 0; i < neighborsOut.n_cols; ++i)
{
neighbors.col(oldFromNewQueries[i]) = neighborsOut.col(i);
distances.col(oldFromNewQueries[i]) = distancesOut.col(i);
}
}
else
{
// Search without building a second tree.
kdTreeRA->Search(querySet, k, neighbors, distances);
}
break;
case COVER_TREE:
// No mapping necessary.
coverTreeRA->Search(querySet, k, neighbors, distances);
break;
case R_TREE:
// No mapping necessary.
rTreeRA->Search(querySet, k, neighbors, distances);
break;
case R_STAR_TREE:
// No mapping necessary.
rStarTreeRA->Search(querySet, k, neighbors, distances);
break;
case X_TREE:
// No mapping necessary.
xTreeRA->Search(querySet, k, neighbors, distances);
break;
}
}
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));
}
inline arma::Col<double>::fixed<6> ParallelKinematicMachine3PUPS::getEndEffectorPose(
const arma::Row<double>::fixed<3>& actuations,
const arma::Row<double>::fixed<3>& endEffectorRotation,
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.");
const double& endEffectorRollAngle = endEffectorRotation(0);
const double& endEffectorPitchAngle = endEffectorRotation(1);
const double& endEffectorYawAngle = endEffectorRotation(2);
arma::Mat<double> baseJointPositions = redundantJointStartPositions_;
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; n++) {
const unsigned int& redundantJointIndex = redundantJointIndicies_(n);
baseJointPositions.col(redundantJointIndex) += redundantJointActuations(redundantJointIndex) * redundantJointStartToEndPositions_.col(redundantJointIndex);
}
baseJointPositions -= get3DRotation(endEffectorRollAngle, endEffectorPitchAngle, endEffectorYawAngle) * endEffectorJointPositions_;
return arma::join_cols(getTriangulation(baseJointPositions.col(0), actuations(0), baseJointPositions.col(1), actuations(1), baseJointPositions.col(2), actuations(2)), endEffectorRotation);
}
inline arma::Cube<double>::fixed<3, 3, 2> ParallelKinematicMachine3PUPS::getModel(
const arma::Col<double>::fixed<6>& 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<3, 3, 2> model;
const arma::Col<double>::fixed<3>& endEffectorPosition = endEffectorPose.subvec(0, 2);
const double& endEffectorRollAngle = endEffectorPose(3);
const double& endEffectorPitchAngle = endEffectorPose(4);
const double& endEffectorYawAngle = endEffectorPose(5);
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) = get3DRotation(endEffectorRollAngle, endEffectorPitchAngle, endEffectorYawAngle) * endEffectorJointPositions_;
model.slice(1).each_col() += endEffectorPosition;
return model;
}
void NaiveBayes<T>::Train(const arma::Mat<T>& trainingData, const arma::Row<int>& classLabels)
{
//Reset prob states
priorProbs.zeros();
featureMeans.zeros();
featureVariances.zeros();
//Mean sum. Sum the feature values for each class label instance
for (size_t j = 0; j < trainingData.n_cols; ++j)
{
const auto label = classLabels[j];
//Increment num occurences of current class in priorProbs set.
++priorProbs[label];
featureMeans.col(label) += trainingData.col(j);
}
//Normalise mean sums. Divide class feature sums by number of class occurences.
for (size_t i = 0; i < priorProbs.n_elem; ++i)
{
//Check if probability of each class != 0 due to no instance of class in training set.
//Avoids divide by zero errors.
if (priorProbs[i] != 0.0)
featureMeans.col(i) /= priorProbs[i]; //divide by total num class occurences.
}
//Variances sum
for (size_t j = 0; j < trainingData.n_cols; ++j)
{
const auto label = classLabels[j];
/**
* Should be real-time safe with arma::square not malloc / dynamic allocating
* due to use of expression templates.
*/
featureVariances.col(label) += arma::square(trainingData.col(j) - featureMeans.col(label));
}
//Normalise variance sums
for (size_t i = 0; i < priorProbs.n_elem; ++i)
{
if (priorProbs[i] > 1)
featureVariances.col(i) /= (priorProbs[i] - 1);
}
/** Ensure that the featureVariances can be inverted.
* The distribution calculation takes the standard deviation which
* is equal to sqrt(featureVariances). The standard deviation is then inverted
* i.e. 1/stdDev or equivalently arma::pow(stdDev, -1). So this will cause a
* divide by zero type error if any attribute variances are equal to 0.0
* The stdDev will then contain -inf or NaN values which causes calculation errors.
* Replace 0.0 variances with minimal floating point value.
*/
for (size_t i = 0; i < featureVariances.n_elem; ++i)
{
if (featureVariances[i] == 0.0)
featureVariances[i] = std::numeric_limits<T>::min();
}
//Normalise prior probabilities
priorProbs /= static_cast<T>(trainingData.n_cols);
}
void NeighborSearch<SortPolicy, MetricType, TreeType>::Search(
const size_t k,
arma::Mat<size_t>& resultingNeighbors,
arma::mat& distances)
{
Timer::Start("computing_neighbors");
// If we have built the trees ourselves, then we will have to map all the
// indices back to their original indices when this computation is finished.
// To avoid an extra copy, we will store the neighbors and distances in a
// separate matrix.
arma::Mat<size_t>* neighborPtr = &resultingNeighbors;
arma::mat* distancePtr = &distances;
if (ownQueryTree || (ownReferenceTree && !queryTree))
distancePtr = new arma::mat; // Query indices need to be mapped.
if (ownReferenceTree || ownQueryTree)
neighborPtr = new arma::Mat<size_t>; // All indices need mapping.
// Set the size of the neighbor and distance matrices.
neighborPtr->set_size(k, querySet.n_cols);
distancePtr->set_size(k, querySet.n_cols);
distancePtr->fill(SortPolicy::WorstDistance());
size_t numPrunes = 0;
if (singleMode)
{
// Create the helper object for the tree traversal.
typedef NeighborSearchRules<SortPolicy, MetricType, TreeType> RuleType;
RuleType rules(referenceSet, querySet, *neighborPtr, *distancePtr, metric);
// Create the traverser.
typename TreeType::template SingleTreeTraverser<RuleType> traverser(rules);
// Now have it traverse for each point.
for (size_t i = 0; i < querySet.n_cols; ++i)
traverser.Traverse(i, *referenceTree);
numPrunes = traverser.NumPrunes();
}
else // Dual-tree recursion.
{
// Create the helper object for the tree traversal.
typedef NeighborSearchRules<SortPolicy, MetricType, TreeType> RuleType;
RuleType rules(referenceSet, querySet, *neighborPtr, *distancePtr, metric);
typename TreeType::template DualTreeTraverser<RuleType> traverser(rules);
if (queryTree)
traverser.Traverse(*queryTree, *referenceTree);
else
traverser.Traverse(*referenceTree, *referenceTree);
numPrunes = traverser.NumPrunes();
}
Log::Warn << "Pruned " << numPrunes << " nodes." << std::endl;
Timer::Stop("computing_neighbors");
// Now, do we need to do mapping of indices?
if (!ownReferenceTree && !ownQueryTree)
{
// No mapping needed. We are done.
return;
}
else if (ownReferenceTree && ownQueryTree) // Map references and queries.
{
// Set size of output matrices correctly.
resultingNeighbors.set_size(k, querySet.n_cols);
distances.set_size(k, querySet.n_cols);
for (size_t i = 0; i < distances.n_cols; i++)
{
// Map distances (copy a column).
distances.col(oldFromNewQueries[i]) = distancePtr->col(i);
// Map indices of neighbors.
for (size_t j = 0; j < distances.n_rows; j++)
{
resultingNeighbors(j, oldFromNewQueries[i]) =
oldFromNewReferences[(*neighborPtr)(j, i)];
}
}
// Finished with temporary matrices.
delete neighborPtr;
delete distancePtr;
}
else if (ownReferenceTree)
{
if (!queryTree) // No query tree -- map both references and queries.
{
resultingNeighbors.set_size(k, querySet.n_cols);
distances.set_size(k, querySet.n_cols);
for (size_t i = 0; i < distances.n_cols; i++)
{
// Map distances (copy a column).
distances.col(oldFromNewReferences[i]) = distancePtr->col(i);
// Map indices of neighbors.
for (size_t j = 0; j < distances.n_rows; j++)
{
resultingNeighbors(j, oldFromNewReferences[i]) =
oldFromNewReferences[(*neighborPtr)(j, i)];
}
}
}
else // Map only references.
{
// Set size of neighbor indices matrix correctly.
resultingNeighbors.set_size(k, querySet.n_cols);
// Map indices of neighbors.
for (size_t i = 0; i < resultingNeighbors.n_cols; i++)
{
for (size_t j = 0; j < resultingNeighbors.n_rows; j++)
{
resultingNeighbors(j, i) = oldFromNewReferences[(*neighborPtr)(j, i)];
}
}
}
// Finished with temporary matrix.
delete neighborPtr;
}
else if (ownQueryTree)
{
// Set size of matrices correctly.
resultingNeighbors.set_size(k, querySet.n_cols);
distances.set_size(k, querySet.n_cols);
for (size_t i = 0; i < distances.n_cols; i++)
{
// Map distances (copy a column).
distances.col(oldFromNewQueries[i]) = distancePtr->col(i);
// Map indices of neighbors.
resultingNeighbors.col(oldFromNewQueries[i]) = neighborPtr->col(i);
}
// Finished with temporary matrices.
delete neighborPtr;
delete distancePtr;
}
} // Search
void remove_column_means(arma::Mat<double>& data, const arma::Col<double>& means) {
if (data.n_cols != means.n_elem)
throw std::range_error("Number of elements of means is not equal to the number of columns of data");
for (long i=0; i<long(data.n_cols); ++i)
data.col(i) -= means(i);
}