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]);
}
}
inline ParallelKinematicMachine3PRPR::ParallelKinematicMachine3PRPR() noexcept {
setMinimalActiveJointActuations({0.1, 0.1, 0.1});
setMinimalActiveJointActuations({1.2, 1.2, 1.2});
setEndEffectorJointPositions({
-0.000066580445834, 0.106954081945581,
-0.092751709777083, -0.053477040972790,
0.092818290222917, -0.053477040972790});
setRedundantJointStartPositions({
0.1, 1.0392,
0.0, 0.8,
1.2, 0.8});
setRedundantJointEndPositions({
1.1, 1.0392,
0.0, -0.2,
1.2, -0.2});
redundantJointStartToEndPositions_ = redundantJointEndPositions_ - redundantJointStartPositions_;
redundantJointIndicies_ = arma::find(arma::any(redundantJointStartToEndPositions_));
redundantJointAngleSines_.set_size(redundantJointIndicies_.n_elem);
redundantJointAngleCosines_.set_size(redundantJointIndicies_.n_elem);
for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; ++n) {
const double redundantJointAngle = std::atan2(redundantJointStartToEndPositions_(1, n), redundantJointStartToEndPositions_(0, n));
redundantJointAngleSines_(n) = std::sin(redundantJointAngle);
redundantJointAngleCosines_(n) = std::cos(redundantJointAngle);
}
}
std::vector<double> extract_row_vector(const arma::Mat<double>& data, long index) {
if (index<0 || index >= long(data.n_rows))
throw std::range_error(join("Index out of range: ", index));
const arma::Row<double> row(data.row(index));
const double* memptr = row.memptr();
std::vector<double> result(memptr, memptr + row.n_elem);
return std::move(result);
}
void AdaBoost<MatType, WeakLearner>::Classify(
const MatType& test,
arma::Row<size_t>& predictedLabels)
{
arma::Row<size_t> tempPredictedLabels(test.n_cols);
arma::mat cMatrix(classes, test.n_cols);
cMatrix.zeros();
predictedLabels.set_size(test.n_cols);
for (size_t i = 0; i < wl.size(); i++)
{
wl[i].Classify(test, tempPredictedLabels);
for (size_t j = 0; j < tempPredictedLabels.n_cols; j++)
cMatrix(tempPredictedLabels(j), j) += (alpha[i] * tempPredictedLabels(j));
}
arma::colvec cMRow;
arma::uword maxIndex;
for (size_t i = 0; i < predictedLabels.n_cols; i++)
{
cMRow = cMatrix.unsafe_col(i);
cMRow.max(maxIndex);
predictedLabels(i) = maxIndex;
}
}
void SoftmaxRegression<OptimizerType>::Predict(const arma::mat& testData,
arma::Row<size_t>& predictions)
const
{
if (testData.n_rows != FeatureSize())
{
std::ostringstream oss;
oss << "SoftmaxRegression::Predict(): test data has " << testData.n_rows
<< " dimensions, but model has " << FeatureSize() << "dimensions";
throw std::invalid_argument(oss.str());
}
// Calculate the probabilities for each test input.
arma::mat hypothesis, probabilities;
if (fitIntercept)
{
// In order to add the intercept term, we should compute following matrix:
// [1; data] = arma::join_cols(ones(1, data.n_cols), data)
// hypothesis = arma::exp(parameters * [1; data]).
//
// Since the cost of join maybe high due to the copy of original data,
// split the hypothesis computation to two components.
hypothesis = arma::exp(
arma::repmat(parameters.col(0), 1, testData.n_cols) +
parameters.cols(1, parameters.n_cols - 1) * testData);
}
else
{
hypothesis = arma::exp(parameters * testData);
}
probabilities = hypothesis / arma::repmat(arma::sum(hypothesis, 0),
numClasses, 1);
// Prepare necessary data.
predictions.zeros(testData.n_cols);
double maxProbability = 0;
// For each test input.
for (size_t i = 0; i < testData.n_cols; i++)
{
// For each class.
for (size_t j = 0; j < numClasses; j++)
{
// If a higher class probability is encountered, change prediction.
if (probabilities(j, i) > maxProbability)
{
maxProbability = probabilities(j, i);
predictions(i) = j;
}
}
// Set maximum probability to zero for the next input.
maxProbability = 0;
}
}
arma::Cube<double> MultiLevelStewartPlatform::getModelImplementation(
const arma::Col<double>& endEffectorPose,
const arma::Row<double>& redundantJointsActuation) const {
assert(redundantJointsActuation.n_elem == numberOfRedundantJoints_);
assert(arma::all(redundantJointsActuation >= 0) && arma::all(redundantJointsActuation <= 1));
arma::Cube<double> model = platformLevels_.at(0).getModel(endEffectorPose, {});
for (arma::uword n = 1; n < platformLevels_.size(); ++n) {
arma::join_slices(model, platformLevels_.at(n).getModel(redundantJointsActuation.subvec(6 * n - 6, 6 * n - 1), {}));
}
return model;
}
void NaiveBayesClassifier<MatType>::Classify(const MatType& data,
arma::Row<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) // log(exp(value)) == value
exponents(j) = arma::accu(diffs.col(j) % rhs.unsafe_col(j));
// Calculate probability as sum of logarithm to decrease floating point
// errors.
testProbs.col(i) += (data.n_rows / -2.0 * log(2 * M_PI) - 0.5 *
log(arma::det(arma::diagmat(variances.col(i)))) + 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;
}
DecisionStump<MatType>::DecisionStump(const MatType& data,
const arma::Row<size_t>& labels,
const size_t classes,
size_t inpBucketSize)
{
numClass = classes;
bucketSize = inpBucketSize;
// If classLabels are not all identical, proceed with training.
int bestAtt = 0;
double entropy;
const double rootEntropy = CalculateEntropy<size_t>(
labels.subvec(0, labels.n_elem - 1));
double gain, bestGain = 0.0;
for (int i = 0; i < data.n_rows; i++)
{
// Go through each attribute of the data.
if (IsDistinct<double>(data.row(i)))
{
// For each attribute with non-identical values, treat it as a potential
// splitting attribute and calculate entropy if split on it.
entropy = SetupSplitAttribute(data.row(i), labels);
// Rcpp::Rcout << "Entropy for attribute " << i << " is " << entropy << ".\n";
gain = rootEntropy - entropy;
// Find the attribute with the best entropy so that the gain is
// maximized.
// if (entropy < bestEntropy)
// Instead of the above rule, we are maximizing gain, which was
// what is returned from SetupSplitAttribute.
if (gain < bestGain)
{
bestAtt = i;
bestGain = gain;
}
}
}
splitAttribute = bestAtt;
// Once the splitting column/attribute has been decided, train on it.
TrainOnAtt<double>(data.row(splitAttribute), labels);
}
void DecisionStump<MatType>::Classify(const MatType& test,
arma::Row<size_t>& predictedLabels)
{
predictedLabels.set_size(test.n_cols);
for (size_t i = 0; i < test.n_cols; i++)
{
// Determine which bin the test point falls into.
// Assume first that it falls into the first bin, then proceed through the
// bins until it is known which bin it falls into.
size_t bin = 0;
const double val = test(splitDimension, i);
while (bin < split.n_elem - 1)
{
if (val < split(bin + 1))
break;
++bin;
}
predictedLabels(i) = binLabels(bin);
}
}
void RefinedStart::Cluster(const MatType& data,
const size_t clusters,
arma::Row<size_t>& assignments) const
{
// Perform the Bradley-Fayyad refined start algorithm, and get initial
// centroids back.
arma::mat centroids;
Cluster(data, clusters, centroids);
// Turn the final centroids into assignments.
assignments.set_size(data.n_cols);
for (size_t i = 0; i < data.n_cols; ++i)
{
// Find the closest centroid to this point.
double minDistance = std::numeric_limits<double>::infinity();
size_t closestCluster = clusters;
for (size_t j = 0; j < clusters; ++j)
{
// This is restricted to the L2 distance, and unfortunately it would take
// a lot of refactoring and redesign to make this more general... we would
// probably need to have KMeans take a template template parameter for the
// initial partition policy. It's not clear how to best do this.
const double distance = metric::EuclideanDistance::Evaluate(data.col(i),
centroids.col(j));
if (distance < minDistance)
{
minDistance = distance;
closestCluster = j;
}
}
// Assign the point to its closest cluster.
assignments[i] = closestCluster;
}
}
double MultiLevelStewartPlatform::getEndEffectorPoseErrorImplementation(
const arma::Col<double>& endEffectorPose,
const arma::Row<double>& redundantJointsActuation) const {
assert(redundantJointsActuation.n_elem == numberOfRedundantJoints_);
assert(arma::all(redundantJointsActuation >= 0) && arma::all(redundantJointsActuation <= 1));
double endEffectorPoseError = platformLevels_.at(0).getEndEffectorPoseError(endEffectorPose, {});
for (arma::uword n = 1; n < platformLevels_.size(); ++n) {
const double partialEndEffectorPoseError = platformLevels_.at(n).getEndEffectorPoseError(redundantJointsActuation.subvec(6 * n - 6, 6 * n - 1), {});
assert(partialEndEffectorPoseError >= 0);
endEffectorPoseError += partialEndEffectorPoseError;
}
return endEffectorPoseError;
}
arma::Row<double> MultiLevelStewartPlatform::getActuationImplementation(
const arma::Col<double>& endEffectorPose,
const arma::Row<double>& redundantJointsActuation) const {
assert(redundantJointsActuation.n_elem == numberOfRedundantJoints_);
assert(arma::all(redundantJointsActuation >= 0) && arma::all(redundantJointsActuation <= 1));
arma::Row<double> actuation = platformLevels_.at(0).getActuation(endEffectorPose, {});
for (arma::uword n = 1; n < platformLevels_.size(); ++n) {
const arma::Row<double>& partialActuation = platformLevels_.at(n).getActuation(redundantJointsActuation.subvec(6 * n - 6, 6 * n - 1), {});
assert(partialActuation.n_elem == numberOfActiveJoints_);
assert(arma::all(partialActuation >= platformLevels_.at(n).minimalActiveJointsActuation_) && arma::all(partialActuation <= platformLevels_.at(n).maximalActiveJointsActuation_));
actuation = arma::join_rows(actuation, partialActuation);
}
return actuation;
}
