#include <boost/functional/hash.hpp>
#include "storm/storage/sparse/StateType.h"
#include "storm/storage/SparseMatrix.h"
#include "storm/adapters/RationalFunctionAdapter.h"
#include "storm/storage/BitVector.h"
#include "storm/utility/constants.h"
#include "storm/utility/ConstantsComparator.h"
#include "storm/utility/vector.h"
#include "storm/exceptions/InvalidStateException.h"
#include "storm/exceptions/NotImplementedException.h"
#include "storm/exceptions/NotSupportedException.h"
#include "storm/exceptions/InvalidArgumentException.h"
#include "storm/exceptions/OutOfRangeException.h"
#include "storm/utility/macros.h"
#include <iterator>
namespace storm {
namespace storage {
template<typename IndexType, typename ValueType>
MatrixEntry<IndexType, ValueType>::MatrixEntry(IndexType column, ValueType value) : entry(column, value) {
// Intentionally left empty.
}
template<typename IndexType, typename ValueType>
MatrixEntry<IndexType, ValueType>::MatrixEntry(std::pair<IndexType, ValueType>&& pair) : entry(std::move(pair)) {
// Intentionally left empty.
}
template<typename IndexType, typename ValueType>
IndexType const& MatrixEntry<IndexType, ValueType>::getColumn() const {
return this->entry.first;
}
template<typename IndexType, typename ValueType>
void MatrixEntry<IndexType, ValueType>::setColumn(IndexType const& column) {
this->entry.first = column;
}
template<typename IndexType, typename ValueType>
ValueType const& MatrixEntry<IndexType, ValueType>::getValue() const {
return this->entry.second;
}
template<typename IndexType, typename ValueType>
void MatrixEntry<IndexType, ValueType>::setValue(ValueType const& value) {
this->entry.second = value;
}
template<typename IndexType, typename ValueType>
std::pair<IndexType, ValueType> const& MatrixEntry<IndexType, ValueType>::getColumnValuePair() const {
return this->entry;
}
template<typename IndexType, typename ValueType>
MatrixEntry<IndexType, ValueType> MatrixEntry<IndexType, ValueType>::operator*(value_type factor) const {
return MatrixEntry(this->getColumn(), this->getValue() * factor);
}
template<typename IndexType, typename ValueType>
bool MatrixEntry<IndexType, ValueType>::operator==(MatrixEntry<IndexType, ValueType> const& other) const {
return this->entry.first == other.entry.first && this->entry.second == other.entry.second;
}
template<typename IndexType, typename ValueType>
bool MatrixEntry<IndexType, ValueType>::operator!=(MatrixEntry<IndexType, ValueType> const& other) const {
return !(*this == other);
}
template<typename IndexTypePrime, typename ValueTypePrime>
std::ostream& operator<<(std::ostream& out, MatrixEntry<IndexTypePrime, ValueTypePrime> const& entry) {
out << "(" << entry.getColumn() << ", " << entry.getValue() << ")";
return out;
}
template<typename ValueType>
SparseMatrixBuilder<ValueType>::SparseMatrixBuilder(index_type rows, index_type columns, index_type entries, bool forceDimensions, bool hasCustomRowGrouping, index_type rowGroups) : initialRowCountSet(rows != 0), initialRowCount(rows), initialColumnCountSet(columns != 0), initialColumnCount(columns), initialEntryCountSet(entries != 0), initialEntryCount(entries), forceInitialDimensions(forceDimensions), hasCustomRowGrouping(hasCustomRowGrouping), initialRowGroupCountSet(rowGroups != 0), initialRowGroupCount(rowGroups), rowGroupIndices(), columnsAndValues(), rowIndications(), currentEntryCount(0), lastRow(0), lastColumn(0), highestColumn(0), currentRowGroupCount(0) {
// Prepare the internal storage.
if (initialRowCountSet) {
rowIndications.reserve(initialRowCount + 1);
}
if (initialEntryCountSet) {
columnsAndValues.reserve(initialEntryCount);
}
if (hasCustomRowGrouping) {
rowGroupIndices = std::vector<index_type>();
}
if (initialRowGroupCountSet && hasCustomRowGrouping) {
rowGroupIndices.get().reserve(initialRowGroupCount + 1);
}
rowIndications.push_back(0);
}
template<typename ValueType>
SparseMatrixBuilder<ValueType>::SparseMatrixBuilder(SparseMatrix<ValueType>&& matrix) : initialRowCountSet(false), initialRowCount(0), initialColumnCountSet(false), initialColumnCount(0), initialEntryCountSet(false), initialEntryCount(0), forceInitialDimensions(false), hasCustomRowGrouping(!matrix.trivialRowGrouping), initialRowGroupCountSet(false), initialRowGroupCount(0), rowGroupIndices(), columnsAndValues(std::move(matrix.columnsAndValues)), rowIndications(std::move(matrix.rowIndications)), currentEntryCount(matrix.entryCount), currentRowGroupCount() {
lastRow = matrix.rowCount == 0 ? 0 : matrix.rowCount - 1;
lastColumn = columnsAndValues.empty() ? 0 : columnsAndValues.back().getColumn();
highestColumn = matrix.getColumnCount() == 0 ? 0 : matrix.getColumnCount() - 1;
// If the matrix has a custom row grouping, we move it and remove the last element to make it 'open' again.
if (hasCustomRowGrouping) {
rowGroupIndices = std::move(matrix.rowGroupIndices);
if (!rowGroupIndices->empty()) {
rowGroupIndices.get().pop_back();
}
currentRowGroupCount = rowGroupIndices->empty() ? 0 : rowGroupIndices.get().size() - 1;
}
// Likewise, we need to 'open' the row indications again.
if (!rowIndications.empty()) {
rowIndications.pop_back();
}
}
template<typename ValueType>
void SparseMatrixBuilder<ValueType>::addNextValue(index_type row, index_type column, ValueType const& value) {
// Check that we did not move backwards wrt. the row.
STORM_LOG_THROW(row >= lastRow, storm::exceptions::InvalidArgumentException, "Adding an element in row " << row << ", but an element in row " << lastRow << " has already been added.");
STORM_LOG_ASSERT(columnsAndValues.size() == currentEntryCount, "Unexpected size of columnsAndValues vector.");
// Check if a diagonal entry shall be inserted before
if (pendingDiagonalEntry) {
index_type diagColumn = hasCustomRowGrouping ? currentRowGroupCount - 1 : lastRow;
if (row > lastRow || column >= diagColumn) {
ValueType diagValue = std::move(pendingDiagonalEntry.get());
pendingDiagonalEntry = boost::none;
// Add the pending diagonal value now
if (row == lastRow && column == diagColumn) {
// The currently added value coincides with the diagonal entry!
// We add up the values and repeat this call.
addNextValue(row, column, diagValue + value);
// We return here because the above call already did all the work.
return;
} else {
addNextValue(lastRow, diagColumn, diagValue);
}
}
}
// If the element is in the same row, but was not inserted in the correct order, we need to fix the row after
// the insertion.
bool fixCurrentRow = row == lastRow && column < lastColumn;
// If the element is in the same row and column as the previous entry, we add them up...
// unless there is no entry in this row yet, which might happen either for the very first entry or when only a diagonal value has been added
if (row == lastRow && column == lastColumn && rowIndications.back() < currentEntryCount) {
columnsAndValues.back().setValue(columnsAndValues.back().getValue() + value);
} else {
// If we switched to another row, we have to adjust the missing entries in the row indices vector.
if (row != lastRow) {
// Otherwise, we need to push the correct values to the vectors, which might trigger reallocations.
assert(rowIndications.size() == lastRow + 1);
rowIndications.resize(row + 1, currentEntryCount);
lastRow = row;
}
lastColumn = column;
// Finally, set the element and increase the current size.
columnsAndValues.emplace_back(column, value);
highestColumn = std::max(highestColumn, column);
++currentEntryCount;
// If we need to fix the row, do so now.
if (fixCurrentRow) {
// First, we sort according to columns.
std::sort(columnsAndValues.begin() + rowIndications.back(), columnsAndValues.end(), [] (storm::storage::MatrixEntry<index_type, ValueType> const& a, storm::storage::MatrixEntry<index_type, ValueType> const& b) {
return a.getColumn() < b.getColumn();
});
// Then, we eliminate possible duplicate entries.
auto it = std::unique(columnsAndValues.begin() + rowIndications.back(), columnsAndValues.end(), [] (storm::storage::MatrixEntry<index_type, ValueType> const& a, storm::storage::MatrixEntry<index_type, ValueType> const& b) {
return a.getColumn() == b.getColumn();
});
// Finally, remove the superfluous elements.
std::size_t elementsToRemove = std::distance(it, columnsAndValues.end());
if (elementsToRemove > 0) {
STORM_LOG_WARN("Unordered insertion into matrix builder caused duplicate entries.");
currentEntryCount -= elementsToRemove;
columnsAndValues.resize(columnsAndValues.size() - elementsToRemove);
}
}
}
// In case we did not expect this value, we throw an exception.
if (forceInitialDimensions) {
STORM_LOG_THROW(!initialRowCountSet || lastRow < initialRowCount, storm::exceptions::OutOfRangeException, "Cannot insert value at illegal row " << lastRow << ".");
STORM_LOG_THROW(!initialColumnCountSet || lastColumn < initialColumnCount, storm::exceptions::OutOfRangeException, "Cannot insert value at illegal column " << lastColumn << ".");
STORM_LOG_THROW(!initialEntryCountSet || currentEntryCount <= initialEntryCount, storm::exceptions::OutOfRangeException, "Too many entries in matrix, expected only " << initialEntryCount << ".");
}
}
template<typename ValueType>
void SparseMatrixBuilder<ValueType>::newRowGroup(index_type startingRow) {
STORM_LOG_THROW(hasCustomRowGrouping, storm::exceptions::InvalidStateException, "Matrix was not created to have a custom row grouping.");
STORM_LOG_THROW(startingRow >= lastRow, storm::exceptions::InvalidStateException, "Illegal row group with negative size.");
// If there still is a pending diagonal entry, we need to add it now (otherwise, the correct diagonal column will be unclear)
if (pendingDiagonalEntry) {
STORM_LOG_ASSERT(currentRowGroupCount > 0, "Diagonal entry was set before opening the first row group.");
index_type diagColumn = currentRowGroupCount - 1;
ValueType diagValue = std::move(pendingDiagonalEntry.get());
pendingDiagonalEntry = boost::none; // clear now, so addNextValue works properly
addNextValue(lastRow, diagColumn, diagValue);
}
rowGroupIndices.get().push_back(startingRow);
++currentRowGroupCount;
// Handle the case where the previous row group ends with one or more empty rows
if (lastRow + 1 < startingRow) {
// Close all rows from the most recent one to the starting row.
assert(rowIndications.size() == lastRow + 1);
rowIndications.resize(startingRow, currentEntryCount);
// Reset the most recently seen row/column to allow for proper insertion of the following elements.
lastRow = startingRow - 1;
lastColumn = 0;
}
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrixBuilder<ValueType>::build(index_type overriddenRowCount, index_type overriddenColumnCount, index_type overriddenRowGroupCount) {
// If there still is a pending diagonal entry, we need to add it now
if (pendingDiagonalEntry) {
index_type diagColumn = hasCustomRowGrouping ? currentRowGroupCount - 1 : lastRow;
ValueType diagValue = std::move(pendingDiagonalEntry.get());
pendingDiagonalEntry = boost::none; // clear now, so addNextValue works properly
addNextValue(lastRow, diagColumn, diagValue);
}
bool hasEntries = currentEntryCount != 0;
uint_fast64_t rowCount = hasEntries ? lastRow + 1 : 0;
// If the last row group was empty, we need to add one more to the row count, because otherwise this empty row is not counted.
if (hasCustomRowGrouping) {
if (lastRow < rowGroupIndices->back()) {
++rowCount;
}
}
if (initialRowCountSet && forceInitialDimensions) {
STORM_LOG_THROW(rowCount <= initialRowCount, storm::exceptions::InvalidStateException, "Expected not more than " << initialRowCount << " rows, but got " << rowCount << ".");
rowCount = std::max(rowCount, initialRowCount);
}
rowCount = std::max(rowCount, overriddenRowCount);
// If the current row count was overridden, we may need to add empty rows.
for (index_type i = lastRow + 1; i < rowCount; ++i) {
rowIndications.push_back(currentEntryCount);
}
// We put a sentinel element at the last position of the row indices array. This eases iteration work,
// as now the indices of row i are always between rowIndications[i] and rowIndications[i + 1], also for
// the first and last row.
if (rowCount > 0) {
rowIndications.push_back(currentEntryCount);
}
STORM_LOG_ASSERT(rowCount == rowIndications.size() - 1, "Wrong sizes of vectors: " << rowCount << " != " << (rowIndications.size() - 1) << ".");
uint_fast64_t columnCount = hasEntries ? highestColumn + 1 : 0;
if (initialColumnCountSet && forceInitialDimensions) {
STORM_LOG_THROW(columnCount <= initialColumnCount, storm::exceptions::InvalidStateException, "Expected not more than " << initialColumnCount << " columns, but got " << columnCount << ".");
columnCount = std::max(columnCount, initialColumnCount);
}
columnCount = std::max(columnCount, overriddenColumnCount);
uint_fast64_t entryCount = currentEntryCount;
if (initialEntryCountSet && forceInitialDimensions) {
STORM_LOG_THROW(entryCount == initialEntryCount, storm::exceptions::InvalidStateException, "Expected " << initialEntryCount << " entries, but got " << entryCount << ".");
}
// Check whether row groups are missing some entries.
if (hasCustomRowGrouping) {
uint_fast64_t rowGroupCount = currentRowGroupCount;
if (initialRowGroupCountSet && forceInitialDimensions) {
STORM_LOG_THROW(rowGroupCount <= initialRowGroupCount, storm::exceptions::InvalidStateException, "Expected not more than " << initialRowGroupCount << " row groups, but got " << rowGroupCount << ".");
rowGroupCount = std::max(rowGroupCount, initialRowGroupCount);
}
rowGroupCount = std::max(rowGroupCount, overriddenRowGroupCount);
for (index_type i = currentRowGroupCount; i <= rowGroupCount; ++i) {
rowGroupIndices.get().push_back(rowCount);
}
}
return SparseMatrix<ValueType>(columnCount, std::move(rowIndications), std::move(columnsAndValues), std::move(rowGroupIndices));
}
template<typename ValueType>
typename SparseMatrixBuilder<ValueType>::index_type SparseMatrixBuilder<ValueType>::getLastRow() const {
return lastRow;
}
template<typename ValueType>
typename SparseMatrixBuilder<ValueType>::index_type SparseMatrixBuilder<ValueType>::getCurrentRowGroupCount() const {
if (this->hasCustomRowGrouping) {
return currentRowGroupCount;
} else {
return getLastRow() + 1;
}
}
template<typename ValueType>
typename SparseMatrixBuilder<ValueType>::index_type SparseMatrixBuilder<ValueType>::getLastColumn() const {
return lastColumn;
}
// Debug method for printing the current matrix
template<typename ValueType>
void print(std::vector<typename SparseMatrix<ValueType>::index_type> const& rowGroupIndices, std::vector<MatrixEntry<typename SparseMatrix<ValueType>::index_type, typename SparseMatrix<ValueType>::value_type>> const& columnsAndValues, std::vector<typename SparseMatrix<ValueType>::index_type> const& rowIndications) {
typename SparseMatrix<ValueType>::index_type endGroups;
typename SparseMatrix<ValueType>::index_type endRows;
// Iterate over all row groups.
for (typename SparseMatrix<ValueType>::index_type group = 0; group < rowGroupIndices.size(); ++group) {
std::cout << "\t---- group " << group << "/" << (rowGroupIndices.size() - 1) << " ---- " << std::endl;
endGroups = group < rowGroupIndices.size()-1 ? rowGroupIndices[group+1] : rowIndications.size();
// Iterate over all rows in a row group
for (typename SparseMatrix<ValueType>::index_type i = rowGroupIndices[group]; i < endGroups; ++i) {
endRows = i < rowIndications.size()-1 ? rowIndications[i+1] : columnsAndValues.size();
// Print the actual row.
std::cout << "Row " << i << " (" << rowIndications[i] << " - " << endRows << ")" << ": ";
for (typename SparseMatrix<ValueType>::index_type pos = rowIndications[i]; pos < endRows; ++pos) {
std::cout << "(" << columnsAndValues[pos].getColumn() << ": " << columnsAndValues[pos].getValue() << ") ";
}
std::cout << std::endl;
}
}
}
template<typename ValueType>
void SparseMatrixBuilder<ValueType>::replaceColumns(std::vector<index_type> const& replacements, index_type offset) {
index_type maxColumn = 0;
for (index_type row = 0; row < rowIndications.size(); ++row) {
bool changed = false;
auto startRow = std::next(columnsAndValues.begin(), rowIndications[row]);
auto endRow = row < rowIndications.size()-1 ? std::next(columnsAndValues.begin(), rowIndications[row+1]) : columnsAndValues.end();
for (auto entry = startRow; entry != endRow; ++entry) {
if (entry->getColumn() >= offset) {
// Change column
entry->setColumn(replacements[entry->getColumn() - offset]);
changed = true;
}
maxColumn = std::max(maxColumn, entry->getColumn());
}
if (changed) {
// Sort columns in row
std::sort(startRow, endRow,
[](MatrixEntry<index_type, value_type> const& a, MatrixEntry<index_type, value_type> const& b) {
return a.getColumn() < b.getColumn();
});
// Assert no equal elements
STORM_LOG_ASSERT(std::is_sorted(startRow, endRow,
[](MatrixEntry<index_type, value_type> const& a, MatrixEntry<index_type, value_type> const& b) {
return a.getColumn() < b.getColumn();
}), "Columns not sorted.");
}
}
highestColumn = maxColumn;
lastColumn = columnsAndValues.empty() ? 0 : columnsAndValues.back().getColumn();
}
template<typename ValueType>
void SparseMatrixBuilder<ValueType>::addDiagonalEntry(index_type row, ValueType const& value) {
STORM_LOG_THROW(row >= lastRow, storm::exceptions::InvalidArgumentException, "Adding a diagonal element in row " << row << ", but an element in row " << lastRow << " has already been added.");
if (pendingDiagonalEntry) {
if (row == lastRow) {
// Add the two diagonal entries, nothing else to be done.
pendingDiagonalEntry.get() += value;
return;
} else {
// add the pending entry
index_type column = hasCustomRowGrouping ? currentRowGroupCount - 1 : lastRow;
ValueType diagValue = std::move(pendingDiagonalEntry.get());
pendingDiagonalEntry = boost::none; // clear now, so addNextValue works properly
addNextValue(lastRow, column, diagValue);
}
}
pendingDiagonalEntry = value;
if (lastRow != row) {
assert(rowIndications.size() == lastRow + 1);
rowIndications.resize(row + 1, currentEntryCount);
lastRow = row;
lastColumn = 0;
}
}
template<typename ValueType>
SparseMatrix<ValueType>::rows::rows(iterator begin, index_type entryCount) : beginIterator(begin), entryCount(entryCount) {
// Intentionally left empty.
}
template<typename ValueType>
typename SparseMatrix<ValueType>::iterator SparseMatrix<ValueType>::rows::begin() {
return beginIterator;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::iterator SparseMatrix<ValueType>::rows::end() {
return beginIterator + entryCount;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::rows::getNumberOfEntries() const {
return this->entryCount;
}
template<typename ValueType>
SparseMatrix<ValueType>::const_rows::const_rows(const_iterator begin, index_type entryCount) : beginIterator(begin), entryCount(entryCount) {
// Intentionally left empty.
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_iterator SparseMatrix<ValueType>::const_rows::begin() const {
return beginIterator;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_iterator SparseMatrix<ValueType>::const_rows::end() const {
return beginIterator + entryCount;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::const_rows::getNumberOfEntries() const {
return this->entryCount;
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix() : rowCount(0), columnCount(0), entryCount(0), nonzeroEntryCount(0), columnsAndValues(), rowIndications(), rowGroupIndices() {
// Intentionally left empty.
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix(SparseMatrix<ValueType> const& other) : rowCount(other.rowCount), columnCount(other.columnCount), entryCount(other.entryCount), nonzeroEntryCount(other.nonzeroEntryCount), columnsAndValues(other.columnsAndValues), rowIndications(other.rowIndications), trivialRowGrouping(other.trivialRowGrouping), rowGroupIndices(other.rowGroupIndices) {
// Intentionally left empty.
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix(SparseMatrix<value_type> const& other, bool insertDiagonalElements) {
storm::storage::BitVector rowConstraint(other.getRowCount(), true);
storm::storage::BitVector columnConstraint(other.getColumnCount(), true);
*this = other.getSubmatrix(false, rowConstraint, columnConstraint, insertDiagonalElements);
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix(SparseMatrix<ValueType>&& other) : rowCount(other.rowCount), columnCount(other.columnCount), entryCount(other.entryCount), nonzeroEntryCount(other.nonzeroEntryCount), columnsAndValues(std::move(other.columnsAndValues)), rowIndications(std::move(other.rowIndications)), trivialRowGrouping(other.trivialRowGrouping), rowGroupIndices(std::move(other.rowGroupIndices)) {
// Now update the source matrix
other.rowCount = 0;
other.columnCount = 0;
other.entryCount = 0;
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix(index_type columnCount, std::vector<index_type> const& rowIndications, std::vector<MatrixEntry<index_type, ValueType>> const& columnsAndValues, boost::optional<std::vector<index_type>> const& rowGroupIndices) : rowCount(rowIndications.size() - 1), columnCount(columnCount), entryCount(columnsAndValues.size()), nonzeroEntryCount(0), columnsAndValues(columnsAndValues), rowIndications(rowIndications), trivialRowGrouping(!rowGroupIndices), rowGroupIndices(rowGroupIndices) {
this->updateNonzeroEntryCount();
}
template<typename ValueType>
SparseMatrix<ValueType>::SparseMatrix(index_type columnCount, std::vector<index_type>&& rowIndications, std::vector<MatrixEntry<index_type, ValueType>>&& columnsAndValues, boost::optional<std::vector<index_type>>&& rowGroupIndices) : columnCount(columnCount), nonzeroEntryCount(0), columnsAndValues(std::move(columnsAndValues)), rowIndications(std::move(rowIndications)), rowGroupIndices(std::move(rowGroupIndices)) {
// Initialize some variables here which depend on other variables
// This way we are more robust against different initialization orders
this->rowCount = this->rowIndications.size() - 1;
this->entryCount = this->columnsAndValues.size();
this->trivialRowGrouping = !this->rowGroupIndices;
this->updateNonzeroEntryCount();
}
template<typename ValueType>
SparseMatrix<ValueType>& SparseMatrix<ValueType>::operator=(SparseMatrix<ValueType> const& other) {
// Only perform assignment if source and target are not the same.
if (this != &other) {
rowCount = other.rowCount;
columnCount = other.columnCount;
entryCount = other.entryCount;
nonzeroEntryCount = other.nonzeroEntryCount;
columnsAndValues = other.columnsAndValues;
rowIndications = other.rowIndications;
rowGroupIndices = other.rowGroupIndices;
trivialRowGrouping = other.trivialRowGrouping;
}
return *this;
}
template<typename ValueType>
SparseMatrix<ValueType>& SparseMatrix<ValueType>::operator=(SparseMatrix<ValueType>&& other) {
// Only perform assignment if source and target are not the same.
if (this != &other) {
rowCount = other.rowCount;
columnCount = other.columnCount;
entryCount = other.entryCount;
nonzeroEntryCount = other.nonzeroEntryCount;
columnsAndValues = std::move(other.columnsAndValues);
rowIndications = std::move(other.rowIndications);
rowGroupIndices = std::move(other.rowGroupIndices);
trivialRowGrouping = other.trivialRowGrouping;
}
return *this;
}
template<typename ValueType>
bool SparseMatrix<ValueType>::operator==(SparseMatrix<ValueType> const& other) const {
if (this == &other) {
return true;
}
bool equalityResult = true;
equalityResult &= this->getRowCount() == other.getRowCount();
if (!equalityResult) {
return false;
}
equalityResult &= this->getColumnCount() == other.getColumnCount();
if (!equalityResult) {
return false;
}
if (!this->hasTrivialRowGrouping() && !other.hasTrivialRowGrouping()) {
equalityResult &= this->getRowGroupIndices() == other.getRowGroupIndices();
} else {
equalityResult &= this->hasTrivialRowGrouping() && other.hasTrivialRowGrouping();
}
if (!equalityResult) {
return false;
}
// For the actual contents, we need to do a little bit more work, because we want to ignore elements that
// are set to zero, please they may be represented implicitly in the other matrix.
for (index_type row = 0; row < this->getRowCount(); ++row) {
for (const_iterator it1 = this->begin(row), ite1 = this->end(row), it2 = other.begin(row), ite2 = other.end(row); it1 != ite1 && it2 != ite2; ++it1, ++it2) {
// Skip over all zero entries in both matrices.
while (it1 != ite1 && storm::utility::isZero(it1->getValue())) {
++it1;
}
while (it2 != ite2 && storm::utility::isZero(it2->getValue())) {
++it2;
}
if ((it1 == ite1) || (it2 == ite2)) {
equalityResult = (it1 == ite1) ^ (it2 == ite2);
break;
} else {
if (it1->getColumn() != it2->getColumn() || it1->getValue() != it2->getValue()) {
equalityResult = false;
break;
}
}
}
if (!equalityResult) {
return false;
}
}
return equalityResult;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getRowCount() const {
return rowCount;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getColumnCount() const {
return columnCount;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getEntryCount() const {
return entryCount;
}
template<typename T>
uint_fast64_t SparseMatrix<T>::getRowGroupEntryCount(uint_fast64_t const group) const {
uint_fast64_t result = 0;
if (!this->hasTrivialRowGrouping()) {
for (uint_fast64_t row = this->getRowGroupIndices()[group]; row < this->getRowGroupIndices()[group + 1]; ++row) {
result += (this->rowIndications[row + 1] - this->rowIndications[row]);
}
} else {
result += (this->rowIndications[group + 1] - this->rowIndications[group]);
}
return result;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getNonzeroEntryCount() const {
return nonzeroEntryCount;
}
template<typename ValueType>
void SparseMatrix<ValueType>::updateNonzeroEntryCount() const {
this->nonzeroEntryCount = 0;
for (auto const& element : *this) {
if (element.getValue() != storm::utility::zero<ValueType>()) {
++this->nonzeroEntryCount;
}
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::updateNonzeroEntryCount(std::make_signed<index_type>::type difference) {
this->nonzeroEntryCount += difference;
}
template<typename ValueType>
void SparseMatrix<ValueType>::updateDimensions() const {
this->nonzeroEntryCount = 0;
this->columnCount = 0;
for (auto const& element : *this) {
if (element.getValue() != storm::utility::zero<ValueType>()) {
++this->nonzeroEntryCount;
this->columnCount = std::max(element.getColumn() + 1, this->columnCount);
}
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getRowGroupCount() const {
if (!this->hasTrivialRowGrouping()) {
return rowGroupIndices.get().size() - 1;
} else {
return rowCount;
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getRowGroupSize(index_type group) const {
return this->getRowGroupIndices()[group + 1] - this->getRowGroupIndices()[group];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getSizeOfLargestRowGroup() const {
if (this->hasTrivialRowGrouping()) {
return 1;
}
index_type res = 0;
index_type previousGroupStart = 0;
for (auto const& i : rowGroupIndices.get()) {
res = std::max(res, i - previousGroupStart);
previousGroupStart = i;
}
return res;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getNumRowsInRowGroups(storm::storage::BitVector const& groupConstraint) const {
if (this->hasTrivialRowGrouping()) {
return groupConstraint.getNumberOfSetBits();
}
index_type numRows = 0;
index_type rowGroupIndex = groupConstraint.getNextSetIndex(0);
while (rowGroupIndex < this->getRowGroupCount()) {
index_type start = this->getRowGroupIndices()[rowGroupIndex];
rowGroupIndex = groupConstraint.getNextUnsetIndex(rowGroupIndex + 1);
index_type end = this->getRowGroupIndices()[rowGroupIndex];
// All rows with index in [start,end) are selected.
numRows += end - start;
rowGroupIndex = groupConstraint.getNextSetIndex(rowGroupIndex + 1);
}
return numRows;
}
template<typename ValueType>
std::vector<typename SparseMatrix<ValueType>::index_type> const& SparseMatrix<ValueType>::getRowGroupIndices() const {
// If there is no current row grouping, we need to create it.
if (!this->rowGroupIndices) {
STORM_LOG_ASSERT(trivialRowGrouping, "Only trivial row-groupings can be constructed on-the-fly.");
this->rowGroupIndices = storm::utility::vector::buildVectorForRange(static_cast<index_type>(0), this->getRowGroupCount() + 1);
}
return rowGroupIndices.get();
}
template<typename ValueType>
std::vector<typename SparseMatrix<ValueType>::index_type> SparseMatrix<ValueType>::swapRowGroupIndices(std::vector<index_type>&& newRowGrouping) {
std::vector<index_type> result;
if (this->rowGroupIndices) {
result = std::move(rowGroupIndices.get());
rowGroupIndices = std::move(newRowGrouping);
}
return result;
}
template<typename ValueType>
void SparseMatrix<ValueType>::setRowGroupIndices(std::vector<index_type> const& newRowGroupIndices) {
trivialRowGrouping = false;
rowGroupIndices = newRowGroupIndices;
}
template<typename ValueType>
bool SparseMatrix<ValueType>::hasTrivialRowGrouping() const {
return trivialRowGrouping;
}
template<typename ValueType>
void SparseMatrix<ValueType>::makeRowGroupingTrivial() {
if (trivialRowGrouping) {
STORM_LOG_ASSERT(!rowGroupIndices || rowGroupIndices.get() == storm::utility::vector::buildVectorForRange(static_cast<index_type>(0), this->getRowGroupCount() + 1), "Row grouping is supposed to be trivial but actually it is not.");
} else {
trivialRowGrouping = true;
rowGroupIndices = boost::none;
}
}
template<typename ValueType>
storm::storage::BitVector SparseMatrix<ValueType>::getRowFilter(storm::storage::BitVector const& groupConstraint) const {
storm::storage::BitVector res(this->getRowCount(), false);
for(auto group : groupConstraint) {
uint_fast64_t const endOfGroup = this->getRowGroupIndices()[group + 1];
for(uint_fast64_t row = this->getRowGroupIndices()[group]; row < endOfGroup; ++row) {
res.set(row, true);
}
}
return res;
}
template<typename ValueType>
storm::storage::BitVector SparseMatrix<ValueType>::getRowFilter(storm::storage::BitVector const& groupConstraint, storm::storage::BitVector const& columnConstraint) const {
storm::storage::BitVector result(this->getRowCount(), false);
for (auto const& group : groupConstraint) {
uint_fast64_t const endOfGroup = this->getRowGroupIndices()[group + 1];
for (uint_fast64_t row = this->getRowGroupIndices()[group]; row < endOfGroup; ++row) {
bool choiceSatisfiesColumnConstraint = true;
for (auto const& entry : this->getRow(row)) {
if (!columnConstraint.get(entry.getColumn())) {
choiceSatisfiesColumnConstraint = false;
break;
}
}
if (choiceSatisfiesColumnConstraint) {
result.set(row, true);
}
}
}
return result;
}
template<typename ValueType>
storm::storage::BitVector SparseMatrix<ValueType>::getRowGroupFilter(storm::storage::BitVector const& rowConstraint, bool setIfForAllRowsInGroup) const {
STORM_LOG_ASSERT(!this->hasTrivialRowGrouping(), "Tried to get a row group filter but this matrix does not have row groups");
storm::storage::BitVector result(this->getRowGroupCount(), false);
auto const& groupIndices = this->getRowGroupIndices();
if (setIfForAllRowsInGroup) {
for (uint64_t group = 0; group < this->getRowGroupCount(); ++group) {
if (rowConstraint.getNextUnsetIndex(groupIndices[group]) >= groupIndices[group + 1]) {
// All rows within this group are set
result.set(group, true);
}
}
} else {
for (uint64_t group = 0; group < this->getRowGroupCount(); ++group) {
if (rowConstraint.getNextSetIndex(groupIndices[group]) < groupIndices[group + 1]) {
// Some row is set
result.set(group, true);
}
}
}
return result;
}
template<typename ValueType>
void SparseMatrix<ValueType>::makeRowsAbsorbing(storm::storage::BitVector const& rows) {
for (auto row : rows) {
makeRowDirac(row, row);
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::makeRowGroupsAbsorbing(storm::storage::BitVector const& rowGroupConstraint) {
if (!this->hasTrivialRowGrouping()) {
for (auto rowGroup : rowGroupConstraint) {
for (index_type row = this->getRowGroupIndices()[rowGroup]; row < this->getRowGroupIndices()[rowGroup + 1]; ++row) {
makeRowDirac(row, rowGroup);
}
}
} else {
for (auto rowGroup : rowGroupConstraint) {
makeRowDirac(rowGroup, rowGroup);
}
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::makeRowDirac(index_type row, index_type column) {
iterator columnValuePtr = this->begin(row);
iterator columnValuePtrEnd = this->end(row);
// If the row has no elements in it, we cannot make it absorbing, because we would need to move all elements
// in the vector of nonzeros otherwise.
if (columnValuePtr >= columnValuePtrEnd) {
throw storm::exceptions::InvalidStateException() << "Illegal call to SparseMatrix::makeRowDirac: cannot make row " << row << " absorbing, because there is no entry in this row.";
}
iterator lastColumnValuePtr = this->end(row) - 1;
// If there is at least one entry in this row, we can set it to one, modify its column value to the
// one given by the parameter and set all subsequent elements of this row to zero.
// However, we want to preserve that column indices within a row are ascending, so we pick an entry that is close to the desired column index
while (columnValuePtr->getColumn() < column && columnValuePtr != lastColumnValuePtr) {
if (!storm::utility::isZero(columnValuePtr->getValue())) {
--this->nonzeroEntryCount;
}
columnValuePtr->setValue(storm::utility::zero<ValueType>());
++columnValuePtr;
}
// At this point, we have found the first entry whose column is >= the desired column (or the last entry of the row, if no such column exist)
if (storm::utility::isZero(columnValuePtr->getValue())) {
++this->nonzeroEntryCount;
}
columnValuePtr->setValue(storm::utility::one<ValueType>());
columnValuePtr->setColumn(column);
for (++columnValuePtr; columnValuePtr != columnValuePtrEnd; ++columnValuePtr) {
if (!storm::utility::isZero(columnValuePtr->getValue())) {
--this->nonzeroEntryCount;
}
columnValuePtr->setValue(storm::utility::zero<ValueType>());
}
}
template<typename ValueType>
bool SparseMatrix<ValueType>::compareRows(index_type i1, index_type i2) const {
const_iterator end1 = this->end(i1);
const_iterator end2 = this->end(i2);
const_iterator it1 = this->begin(i1);
const_iterator it2 = this->begin(i2);
for(;it1 != end1 && it2 != end2; ++it1, ++it2 ) {
if(*it1 != *it2) {
return false;
}
}
if(it1 == end1 && it2 == end2) {
return true;
}
return false;
}
template<typename ValueType>
BitVector SparseMatrix<ValueType>::duplicateRowsInRowgroups() const {
BitVector bv(this->getRowCount());
for(size_t rowgroup = 0; rowgroup < this->getRowGroupCount(); ++rowgroup) {
for(size_t row1 = this->getRowGroupIndices().at(rowgroup); row1 < this->getRowGroupIndices().at(rowgroup+1); ++row1) {
for(size_t row2 = row1; row2 < this->getRowGroupIndices().at(rowgroup+1); ++row2) {
if(compareRows(row1, row2)) {
bv.set(row2);
}
}
}
}
return bv;
}
template<typename ValueType>
void SparseMatrix<ValueType>::swapRows(index_type const& row1, index_type const& row2) {
if (row1 == row2) {
return;
}
// Get the index of the row that has more / less entries than the other.
index_type largerRow = getRow(row1).getNumberOfEntries() > getRow(row2).getNumberOfEntries() ? row1 : row2;
index_type smallerRow = largerRow == row1 ? row2 : row1;
index_type rowSizeDifference = getRow(largerRow).getNumberOfEntries() - getRow(smallerRow).getNumberOfEntries();
// Save contents of larger row.
auto copyRow = getRow(largerRow);
std::vector<MatrixEntry<index_type, value_type>> largerRowContents(copyRow.begin(), copyRow.end());
if (largerRow < smallerRow) {
auto writeIt = getRows(largerRow, smallerRow + 1).begin();
// Write smaller row to its new position.
for (auto& smallerRowEntry : getRow(smallerRow)) {
*writeIt = std::move(smallerRowEntry);
++writeIt;
}
// Write the intermediate rows into their correct position.
if (!storm::utility::isZero(rowSizeDifference)) {
for (auto& intermediateRowEntry : getRows(largerRow + 1, smallerRow)) {
*writeIt = std::move(intermediateRowEntry);
++writeIt;
}
} else {
// skip the intermediate rows
writeIt = getRow(smallerRow).begin();
}
// Write the larger row to its new position.
for (auto& largerRowEntry : largerRowContents) {
*writeIt = std::move(largerRowEntry);
++writeIt;
}
STORM_LOG_ASSERT(writeIt == getRow(smallerRow).end(), "Unexpected position of write iterator.");
// Update the row indications to account for the shift of indices at where the rows now start.
if (!storm::utility::isZero(rowSizeDifference)) {
for (index_type row = largerRow + 1; row <= smallerRow; ++row) {
rowIndications[row] -= rowSizeDifference;
}
}
} else {
auto writeIt = getRows(smallerRow, largerRow + 1).end() - 1;
// Write smaller row to its new position
auto copyRow = getRow(smallerRow);
for (auto smallerRowEntryIt = copyRow.end() - 1; smallerRowEntryIt != copyRow.begin() - 1; --smallerRowEntryIt) {
*writeIt = std::move(*smallerRowEntryIt);
--writeIt;
}
// Write the intermediate rows into their correct position.
if (!storm::utility::isZero(rowSizeDifference)) {
for (auto intermediateRowEntryIt = getRows(smallerRow + 1, largerRow).end() - 1; intermediateRowEntryIt != getRows(smallerRow + 1, largerRow).begin() - 1; --intermediateRowEntryIt) {
*writeIt = std::move(*intermediateRowEntryIt);
--writeIt;
}
} else {
// skip the intermediate rows
writeIt = getRow(smallerRow).end() - 1;
}
// Write the larger row to its new position.
for (auto largerRowEntryIt = largerRowContents.rbegin(); largerRowEntryIt != largerRowContents.rend(); ++largerRowEntryIt) {
*writeIt = std::move(*largerRowEntryIt);
--writeIt;
}
STORM_LOG_ASSERT(writeIt == getRow(smallerRow).begin() - 1, "Unexpected position of write iterator.");
// Update row indications.
// Update the row indications to account for the shift of indices at where the rows now start.
if (!storm::utility::isZero(rowSizeDifference)) {
for (index_type row = smallerRow + 1; row <= largerRow; ++row) {
rowIndications[row] += rowSizeDifference;
}
}
}
}
template<typename ValueType>
std::vector<ValueType> SparseMatrix<ValueType>::getRowSumVector() const {
std::vector<ValueType> result(this->getRowCount());
index_type row = 0;
for (auto resultIt = result.begin(), resultIte = result.end(); resultIt != resultIte; ++resultIt, ++row) {
*resultIt = getRowSum(row);
}
return result;
}
template<typename ValueType>
ValueType SparseMatrix<ValueType>::getConstrainedRowSum(index_type row, storm::storage::BitVector const& constraint) const {
ValueType result = storm::utility::zero<ValueType>();
for (const_iterator it = this->begin(row), ite = this->end(row); it != ite; ++it) {
if (constraint.get(it->getColumn())) {
result += it->getValue();
}
}
return result;
}
template<typename ValueType>
std::vector<ValueType> SparseMatrix<ValueType>::getConstrainedRowSumVector(storm::storage::BitVector const& rowConstraint, storm::storage::BitVector const& columnConstraint) const {
std::vector<ValueType> result(rowConstraint.getNumberOfSetBits());
index_type currentRowCount = 0;
for (auto row : rowConstraint) {
result[currentRowCount++] = getConstrainedRowSum(row, columnConstraint);
}
return result;
}
template<typename ValueType>
std::vector<ValueType> SparseMatrix<ValueType>::getConstrainedRowGroupSumVector(storm::storage::BitVector const& rowGroupConstraint, storm::storage::BitVector const& columnConstraint) const {
std::vector<ValueType> result;
result.reserve(this->getNumRowsInRowGroups(rowGroupConstraint));
if (!this->hasTrivialRowGrouping()) {
for (auto rowGroup : rowGroupConstraint) {
for (index_type row = this->getRowGroupIndices()[rowGroup]; row < this->getRowGroupIndices()[rowGroup + 1]; ++row) {
result.push_back(getConstrainedRowSum(row, columnConstraint));
}
}
} else {
for (auto rowGroup : rowGroupConstraint) {
result.push_back(getConstrainedRowSum(rowGroup, columnConstraint));
}
}
return result;
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::getSubmatrix(bool useGroups, storm::storage::BitVector const& rowConstraint, storm::storage::BitVector const& columnConstraint, bool insertDiagonalElements, storm::storage::BitVector const& makeZeroColumns) const {
if (useGroups) {
return getSubmatrix(rowConstraint, columnConstraint, this->getRowGroupIndices(), insertDiagonalElements, makeZeroColumns);
} else {
// Create a fake row grouping to reduce this to a call to a more general method.
std::vector<index_type> fakeRowGroupIndices(rowCount + 1);
index_type i = 0;
for (std::vector<index_type>::iterator it = fakeRowGroupIndices.begin(); it != fakeRowGroupIndices.end(); ++it, ++i) {
*it = i;
}
auto res = getSubmatrix(rowConstraint, columnConstraint, fakeRowGroupIndices, insertDiagonalElements, makeZeroColumns);
// Create a new row grouping that reflects the new sizes of the row groups if the current matrix has a
// non trivial row-grouping.
if (!this->hasTrivialRowGrouping()) {
std::vector<uint_fast64_t> newRowGroupIndices;
newRowGroupIndices.push_back(0);
auto selectedRowIt = rowConstraint.begin();
// For this, we need to count how many rows were preserved in every group.
for (uint_fast64_t group = 0; group < this->getRowGroupCount(); ++group) {
uint_fast64_t newRowCount = 0;
while (*selectedRowIt < this->getRowGroupIndices()[group + 1]) {
++selectedRowIt;
++newRowCount;
}
if (newRowCount > 0) {
newRowGroupIndices.push_back(newRowGroupIndices.back() + newRowCount);
}
}
res.trivialRowGrouping = false;
res.rowGroupIndices = newRowGroupIndices;
}
return res;
}
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::getSubmatrix(storm::storage::BitVector const& rowGroupConstraint, storm::storage::BitVector const& columnConstraint, std::vector<index_type> const& rowGroupIndices, bool insertDiagonalEntries, storm::storage::BitVector const& makeZeroColumns) const {
STORM_LOG_THROW(!rowGroupConstraint.empty() && !columnConstraint.empty(), storm::exceptions::InvalidArgumentException, "Cannot build empty submatrix.");
uint_fast64_t submatrixColumnCount = columnConstraint.getNumberOfSetBits();
// Start by creating a temporary vector that stores for each index whose bit is set to true the number of
// bits that were set before that particular index.
std::vector<index_type> columnBitsSetBeforeIndex = columnConstraint.getNumberOfSetBitsBeforeIndices();
std::unique_ptr<std::vector<index_type>> tmp;
if (rowGroupConstraint != columnConstraint) {
tmp = std::make_unique<std::vector<index_type>>(rowGroupConstraint.getNumberOfSetBitsBeforeIndices());
}
std::vector<index_type> const& rowBitsSetBeforeIndex = tmp ? *tmp : columnBitsSetBeforeIndex;
// Then, we need to determine the number of entries and the number of rows of the submatrix.
index_type subEntries = 0;
index_type subRows = 0;
index_type rowGroupCount = 0;
for (auto index : rowGroupConstraint) {
subRows += rowGroupIndices[index + 1] - rowGroupIndices[index];
for (index_type i = rowGroupIndices[index]; i < rowGroupIndices[index + 1]; ++i) {
bool foundDiagonalElement = false;
for (const_iterator it = this->begin(i), ite = this->end(i); it != ite; ++it) {
if (columnConstraint.get(it->getColumn()) && (makeZeroColumns.size() == 0 || !makeZeroColumns.get(it->getColumn())) ) {
++subEntries;
if (columnBitsSetBeforeIndex[it->getColumn()] == rowBitsSetBeforeIndex[index]) {
foundDiagonalElement = true;
}
}
}
// If requested, we need to reserve one entry more for inserting the diagonal zero entry.
if (insertDiagonalEntries && !foundDiagonalElement && rowGroupCount < submatrixColumnCount) {
++subEntries;
}
}
++rowGroupCount;
}
// Create and initialize resulting matrix.
SparseMatrixBuilder<ValueType> matrixBuilder(subRows, submatrixColumnCount, subEntries, true, !this->hasTrivialRowGrouping());
// Copy over selected entries.
rowGroupCount = 0;
index_type rowCount = 0;
subEntries = 0;
for (auto index : rowGroupConstraint) {
if (!this->hasTrivialRowGrouping()) {
matrixBuilder.newRowGroup(rowCount);
}
for (index_type i = rowGroupIndices[index]; i < rowGroupIndices[index + 1]; ++i) {
bool insertedDiagonalElement = false;
for (const_iterator it = this->begin(i), ite = this->end(i); it != ite; ++it) {
if (columnConstraint.get(it->getColumn()) && (makeZeroColumns.size() == 0 || !makeZeroColumns.get(it->getColumn()))) {
if (columnBitsSetBeforeIndex[it->getColumn()] == rowBitsSetBeforeIndex[index]) {
insertedDiagonalElement = true;
} else if (insertDiagonalEntries && !insertedDiagonalElement && columnBitsSetBeforeIndex[it->getColumn()] > rowBitsSetBeforeIndex[index]) {
matrixBuilder.addNextValue(rowCount, rowGroupCount, storm::utility::zero<ValueType>());
insertedDiagonalElement = true;
}
++subEntries;
matrixBuilder.addNextValue(rowCount, columnBitsSetBeforeIndex[it->getColumn()], it->getValue());
}
}
if (insertDiagonalEntries && !insertedDiagonalElement && rowGroupCount < submatrixColumnCount) {
matrixBuilder.addNextValue(rowCount, rowGroupCount, storm::utility::zero<ValueType>());
}
++rowCount;
}
++rowGroupCount;
}
return matrixBuilder.build();
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::restrictRows(storm::storage::BitVector const& rowsToKeep, bool allowEmptyRowGroups) const {
STORM_LOG_ASSERT(rowsToKeep.size() == this->getRowCount(), "Dimensions mismatch.");
// Count the number of entries of the resulting matrix
uint_fast64_t entryCount = 0;
for (auto const& row : rowsToKeep) {
entryCount += this->getRow(row).getNumberOfEntries();
}
// Get the smallest row group index such that all row groups with at least this index are empty.
uint_fast64_t firstTrailingEmptyRowGroup = this->getRowGroupCount();
for (auto groupIndexIt = this->getRowGroupIndices().rbegin() + 1; groupIndexIt != this->getRowGroupIndices().rend(); ++groupIndexIt) {
if (rowsToKeep.getNextSetIndex(*groupIndexIt) != rowsToKeep.size()) {
break;
}
--firstTrailingEmptyRowGroup;
}
STORM_LOG_THROW(allowEmptyRowGroups || firstTrailingEmptyRowGroup == this->getRowGroupCount(), storm::exceptions::InvalidArgumentException, "Empty rows are not allowed, but row group " << firstTrailingEmptyRowGroup << " is empty.");
// build the matrix. The row grouping will always be considered as nontrivial.
SparseMatrixBuilder<ValueType> builder(rowsToKeep.getNumberOfSetBits(), this->getColumnCount(), entryCount, true, true, this->getRowGroupCount());
uint_fast64_t newRow = 0;
for (uint_fast64_t rowGroup = 0; rowGroup < firstTrailingEmptyRowGroup; ++rowGroup) {
// Add a new row group
builder.newRowGroup(newRow);
bool rowGroupEmpty = true;
for (uint_fast64_t row = rowsToKeep.getNextSetIndex(this->getRowGroupIndices()[rowGroup]); row < this->getRowGroupIndices()[rowGroup + 1]; row = rowsToKeep.getNextSetIndex(row + 1)) {
rowGroupEmpty = false;
for (auto const& entry: this->getRow(row)) {
builder.addNextValue(newRow, entry.getColumn(), entry.getValue());
}
++newRow;
}
STORM_LOG_THROW(allowEmptyRowGroups || !rowGroupEmpty, storm::exceptions::InvalidArgumentException, "Empty rows are not allowed, but row group " << rowGroup << " is empty.");
}
// The all remaining row groups will be empty. Note that it is not allowed to call builder.addNewGroup(...) if there are no more rows afterwards.
SparseMatrix<ValueType> res = builder.build();
return res;
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::filterEntries(storm::storage::BitVector const& rowFilter) const {
// Count the number of entries in the resulting matrix.
index_type entryCount = 0;
for (auto const& row : rowFilter) {
entryCount += getRow(row).getNumberOfEntries();
}
// Build the resulting matrix.
SparseMatrixBuilder<ValueType> builder(getRowCount(), getColumnCount(), entryCount);
for (auto const& row : rowFilter) {
for (auto const& entry : getRow(row)) {
builder.addNextValue(row, entry.getColumn(), entry.getValue());
}
}
SparseMatrix<ValueType> result = builder.build();
// Add a row grouping if necessary.
if (!hasTrivialRowGrouping()) {
result.setRowGroupIndices(getRowGroupIndices());
}
return result;
}
template<typename ValueType>
void SparseMatrix<ValueType>::dropZeroEntries() {
updateNonzeroEntryCount();
if (getNonzeroEntryCount() != getEntryCount()) {
SparseMatrixBuilder<ValueType> builder(getRowCount(), getColumnCount(), getNonzeroEntryCount(), true);
for (index_type row = 0; row < getRowCount(); ++row) {
for (auto const& entry : getRow(row)) {
if (!storm::utility::isZero(entry.getValue())) {
builder.addNextValue(row, entry.getColumn(), entry.getValue());
}
}
}
SparseMatrix<ValueType> result = builder.build();
// Add a row grouping if necessary.
if (!hasTrivialRowGrouping()) {
result.setRowGroupIndices(getRowGroupIndices());
}
*this = std::move(result);
}
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::selectRowsFromRowGroups(std::vector<index_type> const& rowGroupToRowIndexMapping, bool insertDiagonalEntries) const {
// First, we need to count how many non-zero entries the resulting matrix will have and reserve space for
// diagonal entries if requested.
index_type subEntries = 0;
for (index_type rowGroupIndex = 0, rowGroupIndexEnd = rowGroupToRowIndexMapping.size(); rowGroupIndex < rowGroupIndexEnd; ++rowGroupIndex) {
// Determine which row we need to select from the current row group.
index_type rowToCopy = this->getRowGroupIndices()[rowGroupIndex] + rowGroupToRowIndexMapping[rowGroupIndex];
// Iterate through that row and count the number of slots we have to reserve for copying.
bool foundDiagonalElement = false;
for (const_iterator it = this->begin(rowToCopy), ite = this->end(rowToCopy); it != ite; ++it) {
if (it->getColumn() == rowGroupIndex) {
foundDiagonalElement = true;
}
++subEntries;
}
if (insertDiagonalEntries && !foundDiagonalElement) {
++subEntries;
}
}
// Now create the matrix to be returned with the appropriate size.
SparseMatrixBuilder<ValueType> matrixBuilder(rowGroupIndices.get().size() - 1, columnCount, subEntries);
// Copy over the selected lines from the source matrix.
for (index_type rowGroupIndex = 0, rowGroupIndexEnd = rowGroupToRowIndexMapping.size(); rowGroupIndex < rowGroupIndexEnd; ++rowGroupIndex) {
// Determine which row we need to select from the current row group.
index_type rowToCopy = this->getRowGroupIndices()[rowGroupIndex] + rowGroupToRowIndexMapping[rowGroupIndex];
// Iterate through that row and copy the entries. This also inserts a zero element on the diagonal if
// there is no entry yet.
bool insertedDiagonalElement = false;
for (const_iterator it = this->begin(rowToCopy), ite = this->end(rowToCopy); it != ite; ++it) {
if (it->getColumn() == rowGroupIndex) {
insertedDiagonalElement = true;
} else if (insertDiagonalEntries && !insertedDiagonalElement && it->getColumn() > rowGroupIndex) {
matrixBuilder.addNextValue(rowGroupIndex, rowGroupIndex, storm::utility::zero<ValueType>());
insertedDiagonalElement = true;
}
matrixBuilder.addNextValue(rowGroupIndex, it->getColumn(), it->getValue());
}
if (insertDiagonalEntries && !insertedDiagonalElement) {
matrixBuilder.addNextValue(rowGroupIndex, rowGroupIndex, storm::utility::zero<ValueType>());
}
}
// Finalize created matrix and return result.
return matrixBuilder.build();
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::selectRowsFromRowIndexSequence(std::vector<index_type> const& rowIndexSequence, bool insertDiagonalEntries) const{
// First, we need to count how many non-zero entries the resulting matrix will have and reserve space for
// diagonal entries if requested.
index_type newEntries = 0;
for(index_type row = 0, rowEnd = rowIndexSequence.size(); row < rowEnd; ++row) {
bool foundDiagonalElement = false;
for (const_iterator it = this->begin(rowIndexSequence[row]), ite = this->end(rowIndexSequence[row]); it != ite; ++it) {
if (it->getColumn() == row) {
foundDiagonalElement = true;
}
++newEntries;
}
if (insertDiagonalEntries && !foundDiagonalElement) {
++newEntries;
}
}
// Now create the matrix to be returned with the appropriate size.
SparseMatrixBuilder<ValueType> matrixBuilder(rowIndexSequence.size(), columnCount, newEntries);
// Copy over the selected rows from the source matrix.
for(index_type row = 0, rowEnd = rowIndexSequence.size(); row < rowEnd; ++row) {
bool insertedDiagonalElement = false;
for (const_iterator it = this->begin(rowIndexSequence[row]), ite = this->end(rowIndexSequence[row]); it != ite; ++it) {
if (it->getColumn() == row) {
insertedDiagonalElement = true;
} else if (insertDiagonalEntries && !insertedDiagonalElement && it->getColumn() > row) {
matrixBuilder.addNextValue(row, row, storm::utility::zero<ValueType>());
insertedDiagonalElement = true;
}
matrixBuilder.addNextValue(row, it->getColumn(), it->getValue());
}
if (insertDiagonalEntries && !insertedDiagonalElement) {
matrixBuilder.addNextValue(row, row, storm::utility::zero<ValueType>());
}
}
// Finally create matrix and return result.
return matrixBuilder.build();
}
template<typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::permuteRows(std::vector<index_type> const& inversePermutation) const {
// Now create the matrix to be returned with the appropriate size.
// The entry size is only adequate if this is indeed a permutation.
SparseMatrixBuilder<ValueType> matrixBuilder(inversePermutation.size(), columnCount, entryCount);
// Copy over the selected rows from the source matrix.
for (index_type writeTo = 0; writeTo < inversePermutation.size(); ++writeTo) {
index_type const &readFrom = inversePermutation[writeTo];
auto row = this->getRow(readFrom);
for (auto const& entry : row) {
matrixBuilder.addNextValue(writeTo, entry.getColumn(), entry.getValue());
}
}
// Finally create matrix and return result.
auto result = matrixBuilder.build();
if (this->rowGroupIndices) {
result.setRowGroupIndices(this->rowGroupIndices.get());
}
return result;
}
template <typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::transpose(bool joinGroups, bool keepZeros) const {
index_type rowCount = this->getColumnCount();
index_type columnCount = joinGroups ? this->getRowGroupCount() : this->getRowCount();
index_type entryCount;
if (keepZeros) {
entryCount = this->getEntryCount();
} else {
this->updateNonzeroEntryCount();
entryCount = this->getNonzeroEntryCount();
}
std::vector<index_type> rowIndications(rowCount + 1);
std::vector<MatrixEntry<index_type, ValueType>> columnsAndValues(entryCount);
// First, we need to count how many entries each column has.
for (index_type group = 0; group < columnCount; ++group) {
for (auto const& transition : joinGroups ? this->getRowGroup(group) : this->getRow(group)) {
if (transition.getValue() != storm::utility::zero<ValueType>() || keepZeros) {
++rowIndications[transition.getColumn() + 1];
}
}
}
// Now compute the accumulated offsets.
for (index_type i = 1; i < rowCount + 1; ++i) {
rowIndications[i] = rowIndications[i - 1] + rowIndications[i];
}
// Create an array that stores the index for the next value to be added for
// each row in the transposed matrix. Initially this corresponds to the previously
// computed accumulated offsets.
std::vector<index_type> nextIndices = rowIndications;
// Now we are ready to actually fill in the values of the transposed matrix.
for (index_type group = 0; group < columnCount; ++group) {
for (auto const& transition : joinGroups ? this->getRowGroup(group) : this->getRow(group)) {
if (transition.getValue() != storm::utility::zero<ValueType>() || keepZeros) {
columnsAndValues[nextIndices[transition.getColumn()]] = std::make_pair(group, transition.getValue());
nextIndices[transition.getColumn()]++;
}
}
}
storm::storage::SparseMatrix<ValueType> transposedMatrix(columnCount, std::move(rowIndications), std::move(columnsAndValues), boost::none);
return transposedMatrix;
}
template <typename ValueType>
SparseMatrix<ValueType> SparseMatrix<ValueType>::transposeSelectedRowsFromRowGroups(std::vector<uint_fast64_t> const& rowGroupChoices, bool keepZeros) const {
index_type rowCount = this->getColumnCount();
index_type columnCount = this->getRowGroupCount();
// Get the overall entry count as well as the number of entries of each column
index_type entryCount = 0;
std::vector<index_type> rowIndications(columnCount + 1);
auto rowGroupChoiceIt = rowGroupChoices.begin();
for (index_type rowGroup = 0; rowGroup < columnCount; ++rowGroup, ++rowGroupChoiceIt) {
for(auto const& entry : this->getRow(rowGroup, *rowGroupChoiceIt)) {
if(keepZeros || !storm::utility::isZero(entry.getValue())) {
++entryCount;
++rowIndications[entry.getColumn() + 1];
}
}
}
// Now compute the accumulated offsets.
for (index_type i = 1; i < rowCount + 1; ++i) {
rowIndications[i] = rowIndications[i - 1] + rowIndications[i];
}
std::vector<MatrixEntry<index_type, ValueType>> columnsAndValues(entryCount);
// Create an array that stores the index for the next value to be added for
// each row in the transposed matrix. Initially this corresponds to the previously
// computed accumulated offsets.
std::vector<index_type> nextIndices = rowIndications;
// Now we are ready to actually fill in the values of the transposed matrix.
rowGroupChoiceIt = rowGroupChoices.begin();
for (index_type rowGroup = 0; rowGroup < columnCount; ++rowGroup, ++rowGroupChoiceIt) {
for(auto const& entry : this->getRow(rowGroup, *rowGroupChoiceIt)) {
if(keepZeros || !storm::utility::isZero(entry.getValue())) {
columnsAndValues[nextIndices[entry.getColumn()]] = std::make_pair(rowGroup, entry.getValue());
++nextIndices[entry.getColumn()];
}
}
}
return storm::storage::SparseMatrix<ValueType>(std::move(columnCount), std::move(rowIndications), std::move(columnsAndValues), boost::none);
}
template<typename ValueType>
void SparseMatrix<ValueType>::convertToEquationSystem() {
invertDiagonal();
negateAllNonDiagonalEntries();
}
template<typename ValueType>
void SparseMatrix<ValueType>::invertDiagonal() {
// Now iterate over all row groups and set the diagonal elements to the inverted value.
// If there is a row without the diagonal element, an exception is thrown.
ValueType one = storm::utility::one<ValueType>();
ValueType zero = storm::utility::zero<ValueType>();
bool foundDiagonalElement = false;
for (index_type group = 0; group < this->getRowGroupCount(); ++group) {
for (auto& entry : this->getRowGroup(group)) {
if (entry.getColumn() == group) {
if (entry.getValue() == one) {
--this->nonzeroEntryCount;
entry.setValue(zero);
} else if (entry.getValue() == zero) {
++this->nonzeroEntryCount;
entry.setValue(one);
} else {
entry.setValue(one - entry.getValue());
}
foundDiagonalElement = true;
}
}
// Throw an exception if a row did not have an element on the diagonal.
if (!foundDiagonalElement) {
throw storm::exceptions::InvalidArgumentException() << "Illegal call to SparseMatrix::invertDiagonal: matrix is missing diagonal entries.";
}
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::negateAllNonDiagonalEntries() {
// Iterate over all row groups and negate all the elements that are not on the diagonal.
for (index_type group = 0; group < this->getRowGroupCount(); ++group) {
for (auto& entry : this->getRowGroup(group)) {
if (entry.getColumn() != group) {
entry.setValue(-entry.getValue());
}
}
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::deleteDiagonalEntries() {
// Iterate over all rows and negate all the elements that are not on the diagonal.
for (index_type group = 0; group < this->getRowGroupCount(); ++group) {
for (auto& entry : this->getRowGroup(group)) {
if (entry.getColumn() == group) {
--this->nonzeroEntryCount;
entry.setValue(storm::utility::zero<ValueType>());
}
}
}
}
template<typename ValueType>
typename std::pair<storm::storage::SparseMatrix<ValueType>, std::vector<ValueType>> SparseMatrix<ValueType>::getJacobiDecomposition() const {
STORM_LOG_THROW(this->getRowCount() == this->getColumnCount(), storm::exceptions::InvalidArgumentException, "Canno compute Jacobi decomposition of non-square matrix.");
// Prepare the resulting data structures.
SparseMatrixBuilder<ValueType> luBuilder(this->getRowCount(), this->getColumnCount());
std::vector<ValueType> invertedDiagonal(rowCount);
// Copy entries to the appropriate matrices.
for (index_type rowNumber = 0; rowNumber < rowCount; ++rowNumber) {
for (const_iterator it = this->begin(rowNumber), ite = this->end(rowNumber); it != ite; ++it) {
if (it->getColumn() == rowNumber) {
invertedDiagonal[rowNumber] = storm::utility::one<ValueType>() / it->getValue();
} else {
luBuilder.addNextValue(rowNumber, it->getColumn(), it->getValue());
}
}
}
return std::make_pair(luBuilder.build(), std::move(invertedDiagonal));
}
#ifdef STORM_HAVE_CARL
template<>
typename std::pair<storm::storage::SparseMatrix<Interval>, std::vector<Interval>> SparseMatrix<Interval>::getJacobiDecomposition() const {
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "This operation is not supported.");
}
template<>
typename std::pair<storm::storage::SparseMatrix<RationalFunction>, std::vector<RationalFunction>> SparseMatrix<RationalFunction>::getJacobiDecomposition() const {
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "This operation is not supported.");
}
#endif
template<typename ValueType>
template<typename OtherValueType, typename ResultValueType>
ResultValueType SparseMatrix<ValueType>::getPointwiseProductRowSum(storm::storage::SparseMatrix<OtherValueType> const& otherMatrix, index_type const& row) const {
typename storm::storage::SparseMatrix<ValueType>::const_iterator it1 = this->begin(row);
typename storm::storage::SparseMatrix<ValueType>::const_iterator ite1 = this->end(row);
typename storm::storage::SparseMatrix<OtherValueType>::const_iterator it2 = otherMatrix.begin(row);
typename storm::storage::SparseMatrix<OtherValueType>::const_iterator ite2 = otherMatrix.end(row);
ResultValueType result = storm::utility::zero<ResultValueType>();
for (;it1 != ite1 && it2 != ite2; ++it1) {
if (it1->getColumn() < it2->getColumn()) {
continue;
} else {
// If the precondition of this method (i.e. that the given matrix is a submatrix
// of the current one) was fulfilled, we know now that the two elements are in
// the same column, so we can multiply and add them to the row sum vector.
STORM_LOG_ASSERT(it1->getColumn() == it2->getColumn(), "The given matrix is not a submatrix of this one.");
result += it2->getValue() * OtherValueType(it1->getValue());
++it2;
}
}
return result;
}
template<typename ValueType>
template<typename OtherValueType, typename ResultValueType>
std::vector<ResultValueType> SparseMatrix<ValueType>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<OtherValueType> const& otherMatrix) const {
std::vector<ResultValueType> result;
result.reserve(rowCount);
for (index_type row = 0; row < rowCount && row < otherMatrix.getRowCount(); ++row) {
result.push_back(getPointwiseProductRowSum<OtherValueType, ResultValueType>(otherMatrix, row));
}
return result;
}
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyWithVector(std::vector<ValueType> const& vector, std::vector<ValueType>& result, std::vector<value_type> const* summand) const {
// If the vector and the result are aliases and this is not set to be allowed, we need and temporary vector.
std::vector<ValueType>* target;
std::vector<ValueType> temporary;
if (&vector == &result) {
STORM_LOG_WARN("Vectors are aliased. Using temporary, which is potentially slow.");
temporary = std::vector<ValueType>(vector.size());
target = &temporary;
} else {
target = &result;
}
this->multiplyWithVectorForward(vector, *target, summand);
if (target == &temporary) {
std::swap(result, *target);
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyWithVectorForward(std::vector<ValueType> const& vector, std::vector<ValueType>& result, std::vector<value_type> const* summand) const {
const_iterator it = this->begin();
const_iterator ite;
std::vector<index_type>::const_iterator rowIterator = rowIndications.begin();
typename std::vector<ValueType>::iterator resultIterator = result.begin();
typename std::vector<ValueType>::iterator resultIteratorEnd = result.end();
typename std::vector<ValueType>::const_iterator summandIterator;
if (summand) {
summandIterator = summand->begin();
}
for (; resultIterator != resultIteratorEnd; ++rowIterator, ++resultIterator, ++summandIterator) {
ValueType newValue;
if (summand) {
newValue = *summandIterator;
} else {
newValue = storm::utility::zero<ValueType>();
}
for (ite = this->begin() + *(rowIterator + 1); it != ite; ++it) {
newValue += it->getValue() * vector[it->getColumn()];
}
*resultIterator = newValue;
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyWithVectorBackward(std::vector<ValueType> const& vector, std::vector<ValueType>& result, std::vector<value_type> const* summand) const {
const_iterator it = this->end() - 1;
const_iterator ite;
std::vector<index_type>::const_iterator rowIterator = rowIndications.end() - 2;
typename std::vector<ValueType>::iterator resultIterator = result.end() - 1;
typename std::vector<ValueType>::iterator resultIteratorEnd = result.begin() - 1;
typename std::vector<ValueType>::const_iterator summandIterator;
if (summand) {
summandIterator = summand->end() - 1;
}
for (; resultIterator != resultIteratorEnd; --rowIterator, --resultIterator, --summandIterator) {
ValueType newValue;
if (summand) {
newValue = *summandIterator;
} else {
newValue = storm::utility::zero<ValueType>();
}
for (ite = this->begin() + *rowIterator - 1; it != ite; --it) {
newValue += (it->getValue() * vector[it->getColumn()]);
}
*resultIterator = newValue;
}
}
#ifdef STORM_HAVE_INTELTBB
template <typename ValueType>
class TbbMultAddFunctor {
public:
typedef typename storm::storage::SparseMatrix<ValueType>::index_type index_type;
typedef typename storm::storage::SparseMatrix<ValueType>::value_type value_type;
typedef typename storm::storage::SparseMatrix<ValueType>::const_iterator const_iterator;
TbbMultAddFunctor(std::vector<MatrixEntry<index_type, value_type>> const& columnsAndEntries, std::vector<uint64_t> const& rowIndications, std::vector<ValueType> const& x, std::vector<ValueType>& result, std::vector<value_type> const* summand) : columnsAndEntries(columnsAndEntries), rowIndications(rowIndications), x(x), result(result), summand(summand) {
// Intentionally left empty.
}
void operator()(tbb::blocked_range<index_type> const& range) const {
index_type startRow = range.begin();
index_type endRow = range.end();
typename std::vector<index_type>::const_iterator rowIterator = rowIndications.begin() + startRow;
const_iterator it = columnsAndEntries.begin() + *rowIterator;
const_iterator ite;
typename std::vector<ValueType>::iterator resultIterator = result.begin() + startRow;
typename std::vector<ValueType>::iterator resultIteratorEnd = result.begin() + endRow;
typename std::vector<ValueType>::const_iterator summandIterator;
if (summand) {
summandIterator = summand->begin() + startRow;
}
for (; resultIterator != resultIteratorEnd; ++rowIterator, ++resultIterator, ++summandIterator) {
ValueType newValue = summand ? *summandIterator : storm::utility::zero<ValueType>();
for (ite = columnsAndEntries.begin() + *(rowIterator + 1); it != ite; ++it) {
newValue += it->getValue() * x[it->getColumn()];
}
*resultIterator = newValue;
}
}
private:
std::vector<MatrixEntry<index_type, value_type>> const& columnsAndEntries;
std::vector<uint64_t> const& rowIndications;
std::vector<ValueType> const& x;
std::vector<ValueType>& result;
std::vector<value_type> const* summand;
};
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyWithVectorParallel(std::vector<ValueType> const& vector, std::vector<ValueType>& result, std::vector<value_type> const* summand) const {
if (&vector == &result) {
STORM_LOG_WARN("Matrix-vector-multiplication invoked but the target vector uses the same memory as the input vector. This requires to allocate auxiliary memory.");
std::vector<ValueType> tmpVector(this->getRowCount());
multiplyWithVectorParallel(vector, tmpVector);
result = std::move(tmpVector);
} else {
tbb::parallel_for(tbb::blocked_range<index_type>(0, result.size(), 100), TbbMultAddFunctor<ValueType>(columnsAndValues, rowIndications, vector, result, summand));
}
}
#endif
template<typename ValueType>
ValueType SparseMatrix<ValueType>::multiplyRowWithVector(index_type row, std::vector<ValueType> const& vector) const {
ValueType result = storm::utility::zero<ValueType>();
for (auto const& entry : this->getRow(row)){
result += entry.getValue() * vector[entry.getColumn()];
}
return result;
}
template<typename ValueType>
void SparseMatrix<ValueType>::performSuccessiveOverRelaxationStep(ValueType omega, std::vector<ValueType>& x, std::vector<ValueType> const& b) const {
const_iterator it = this->end() - 1;
const_iterator ite;
std::vector<index_type>::const_iterator rowIterator = rowIndications.end() - 2;
typename std::vector<ValueType>::const_iterator bIt = b.end() - 1;
typename std::vector<ValueType>::iterator resultIterator = x.end() - 1;
typename std::vector<ValueType>::iterator resultIteratorEnd = x.begin() - 1;
index_type currentRow = getRowCount();
for (; resultIterator != resultIteratorEnd; --rowIterator, --resultIterator, --bIt) {
--currentRow;
ValueType tmpValue = storm::utility::zero<ValueType>();
ValueType diagonalElement = storm::utility::zero<ValueType>();
for (ite = this->begin() + *rowIterator - 1; it != ite; --it) {
if (it->getColumn() != currentRow) {
tmpValue += it->getValue() * x[it->getColumn()];
} else {
diagonalElement += it->getValue();
}
}
assert(!storm::utility::isZero(diagonalElement));
*resultIterator = ((storm::utility::one<ValueType>() - omega) * *resultIterator) + (omega / diagonalElement) * (*bIt - tmpValue);
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<Interval>::performSuccessiveOverRelaxationStep(Interval, std::vector<Interval>&, std::vector<Interval> const&) const {
STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "This operation is not supported.");
}
#endif
template<typename ValueType>
void SparseMatrix<ValueType>::performWalkerChaeStep(std::vector<ValueType> const& x, std::vector<ValueType> const& columnSums, std::vector<ValueType> const& b, std::vector<ValueType> const& ax, std::vector<ValueType>& result) const {
const_iterator it = this->begin();
const_iterator ite;
std::vector<index_type>::const_iterator rowIterator = rowIndications.begin();
// Clear all previous entries.
ValueType zero = storm::utility::zero<ValueType>();
for (auto& entry : result) {
entry = zero;
}
for (index_type row = 0; row < rowCount; ++row, ++rowIterator) {
for (ite = this->begin() + *(rowIterator + 1); it != ite; ++it) {
result[it->getColumn()] += it->getValue() * (b[row] / ax[row]);
}
}
auto xIterator = x.begin();
auto sumsIterator = columnSums.begin();
for (auto& entry : result) {
entry *= *xIterator / *sumsIterator;
++xIterator;
++sumsIterator;
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<Interval>::performWalkerChaeStep(std::vector<Interval> const& x, std::vector<Interval> const& rowSums, std::vector<Interval> const& b, std::vector<Interval> const& ax, std::vector<Interval>& result) const {
STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "This operation is not supported.");
}
#endif
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyAndReduceForward(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
if (dir == OptimizationDirection::Minimize) {
multiplyAndReduceForward<storm::utility::ElementLess<ValueType>>(rowGroupIndices, vector, summand, result, choices);
} else {
multiplyAndReduceForward<storm::utility::ElementGreater<ValueType>>(rowGroupIndices, vector, summand, result, choices);
}
}
template<typename ValueType>
template<typename Compare>
void SparseMatrix<ValueType>::multiplyAndReduceForward(std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
Compare compare;
auto elementIt = this->begin();
auto rowGroupIt = rowGroupIndices.begin();
auto rowIt = rowIndications.begin();
typename std::vector<ValueType>::const_iterator summandIt;
if (summand) {
summandIt = summand->begin();
}
typename std::vector<uint_fast64_t>::iterator choiceIt;
if (choices) {
choiceIt = choices->begin();
}
// Variables for correctly tracking choices (only update if new choice is strictly better).
ValueType oldSelectedChoiceValue;
uint64_t selectedChoice;
uint64_t currentRow = 0;
for (auto resultIt = result.begin(), resultIte = result.end(); resultIt != resultIte; ++resultIt, ++choiceIt, ++rowGroupIt) {
ValueType currentValue = storm::utility::zero<ValueType>();
// Only multiply and reduce if there is at least one row in the group.
if (*rowGroupIt < *(rowGroupIt + 1)) {
if (summand) {
currentValue = *summandIt;
++summandIt;
}
for (auto elementIte = this->begin() + *(rowIt + 1); elementIt != elementIte; ++elementIt) {
currentValue += elementIt->getValue() * vector[elementIt->getColumn()];
}
if (choices) {
selectedChoice = 0;
if (*choiceIt == 0) {
oldSelectedChoiceValue = currentValue;
}
}
++rowIt;
++currentRow;
for (; currentRow < *(rowGroupIt + 1); ++rowIt, ++currentRow) {
ValueType newValue = summand ? *summandIt : storm::utility::zero<ValueType>();
for (auto elementIte = this->begin() + *(rowIt + 1); elementIt != elementIte; ++elementIt) {
newValue += elementIt->getValue() * vector[elementIt->getColumn()];
}
if (choices && currentRow == *choiceIt + *rowGroupIt) {
oldSelectedChoiceValue = newValue;
}
if (compare(newValue, currentValue)) {
currentValue = newValue;
if (choices) {
selectedChoice = currentRow - *rowGroupIt;
}
}
if (summand) {
++summandIt;
}
}
// Finally write value to target vector.
*resultIt = currentValue;
if (choices && compare(currentValue, oldSelectedChoiceValue)) {
*choiceIt = selectedChoice;
}
}
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<storm::RationalFunction>::multiplyAndReduceForward(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<storm::RationalFunction> const& vector, std::vector<storm::RationalFunction> const* b, std::vector<storm::RationalFunction>& result, std::vector<uint_fast64_t>* choices) const {
STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "This operation is not supported.");
}
#endif
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyAndReduceBackward(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
if (dir == storm::OptimizationDirection::Minimize) {
multiplyAndReduceBackward<storm::utility::ElementLess<ValueType>>(rowGroupIndices, vector, summand, result, choices);
} else {
multiplyAndReduceBackward<storm::utility::ElementGreater<ValueType>>(rowGroupIndices, vector, summand, result, choices);
}
}
template<typename ValueType>
template<typename Compare>
void SparseMatrix<ValueType>::multiplyAndReduceBackward(std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
Compare compare;
auto elementIt = this->end() - 1;
auto rowGroupIt = rowGroupIndices.end() - 2;
auto rowIt = rowIndications.end() - 2;
typename std::vector<ValueType>::const_iterator summandIt;
if (summand) {
summandIt = summand->end() - 1;
}
typename std::vector<uint_fast64_t>::iterator choiceIt;
if (choices) {
choiceIt = choices->end() - 1;
}
// Variables for correctly tracking choices (only update if new choice is strictly better).
ValueType oldSelectedChoiceValue;
uint64_t selectedChoice;
uint64_t currentRow = this->getRowCount() - 1;
for (auto resultIt = result.end() - 1, resultIte = result.begin() - 1; resultIt != resultIte; --resultIt, --choiceIt, --rowGroupIt) {
ValueType currentValue = storm::utility::zero<ValueType>();
// Only multiply and reduce if there is at least one row in the group.
if (*rowGroupIt < *(rowGroupIt + 1)) {
if (summand) {
currentValue = *summandIt;
--summandIt;
}
for (auto elementIte = this->begin() + *rowIt - 1; elementIt != elementIte; --elementIt) {
currentValue += elementIt->getValue() * vector[elementIt->getColumn()];
}
if (choices) {
selectedChoice = currentRow - *rowGroupIt;
if (*choiceIt == selectedChoice) {
oldSelectedChoiceValue = currentValue;
}
}
--rowIt;
--currentRow;
for (uint64_t i = *rowGroupIt + 1, end = *(rowGroupIt + 1); i < end; --rowIt, --currentRow, ++i, --summandIt) {
ValueType newValue = summand ? *summandIt : storm::utility::zero<ValueType>();
for (auto elementIte = this->begin() + *rowIt - 1; elementIt != elementIte; --elementIt) {
newValue += elementIt->getValue() * vector[elementIt->getColumn()];
}
if (choices && currentRow == *choiceIt + *rowGroupIt) {
oldSelectedChoiceValue = newValue;
}
if (compare(newValue, currentValue)) {
currentValue = newValue;
if (choices) {
selectedChoice = currentRow - *rowGroupIt;
}
}
}
// Finally write value to target vector.
*resultIt = currentValue;
if (choices && compare(currentValue, oldSelectedChoiceValue)) {
*choiceIt = selectedChoice;
}
}
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<storm::RationalFunction>::multiplyAndReduceBackward(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<storm::RationalFunction> const& vector, std::vector<storm::RationalFunction> const* b, std::vector<storm::RationalFunction>& result, std::vector<uint_fast64_t>* choices) const {
STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "This operation is not supported.");
}
#endif
#ifdef STORM_HAVE_INTELTBB
template <typename ValueType, typename Compare>
class TbbMultAddReduceFunctor {
public:
typedef typename storm::storage::SparseMatrix<ValueType>::index_type index_type;
typedef typename storm::storage::SparseMatrix<ValueType>::value_type value_type;
typedef typename storm::storage::SparseMatrix<ValueType>::const_iterator const_iterator;
TbbMultAddReduceFunctor(std::vector<uint64_t> const& rowGroupIndices, std::vector<MatrixEntry<index_type, value_type>> const& columnsAndEntries, std::vector<uint64_t> const& rowIndications, std::vector<ValueType> const& x, std::vector<ValueType>& result, std::vector<value_type> const* summand, std::vector<uint_fast64_t>* choices) : rowGroupIndices(rowGroupIndices), columnsAndEntries(columnsAndEntries), rowIndications(rowIndications), x(x), result(result), summand(summand), choices(choices) {
// Intentionally left empty.
}
void operator()(tbb::blocked_range<index_type> const& range) const {
auto groupIt = rowGroupIndices.begin() + range.begin();
auto groupIte = rowGroupIndices.begin() + range.end();
auto rowIt = rowIndications.begin() + *groupIt;
auto elementIt = columnsAndEntries.begin() + *rowIt;
typename std::vector<ValueType>::const_iterator summandIt;
if (summand) {
summandIt = summand->begin() + *groupIt;
}
typename std::vector<uint_fast64_t>::iterator choiceIt;
if (choices) {
choiceIt = choices->begin() + range.begin();
}
auto resultIt = result.begin() + range.begin();
// Variables for correctly tracking choices (only update if new choice is strictly better).
ValueType oldSelectedChoiceValue;
uint64_t selectedChoice;
uint64_t currentRow = *groupIt;
for (; groupIt != groupIte; ++groupIt, ++resultIt, ++choiceIt) {
ValueType currentValue = storm::utility::zero<ValueType>();
// Only multiply and reduce if there is at least one row in the group.
if (*groupIt < *(groupIt + 1)) {
if (summand) {
currentValue = *summandIt;
++summandIt;
}
for (auto elementIte = columnsAndEntries.begin() + *(rowIt + 1); elementIt != elementIte; ++elementIt) {
currentValue += elementIt->getValue() * x[elementIt->getColumn()];
}
if (choices) {
selectedChoice = 0;
if (*choiceIt == 0) {
oldSelectedChoiceValue = currentValue;
}
}
++rowIt;
++currentRow;
for (; currentRow < *(groupIt + 1); ++rowIt, ++currentRow, ++summandIt) {
ValueType newValue = summand ? *summandIt : storm::utility::zero<ValueType>();
for (auto elementIte = columnsAndEntries.begin() + *(rowIt + 1); elementIt != elementIte; ++elementIt) {
newValue += elementIt->getValue() * x[elementIt->getColumn()];
}
if (choices && currentRow == *choiceIt + *groupIt) {
oldSelectedChoiceValue = newValue;
}
if (compare(newValue, currentValue)) {
currentValue = newValue;
if (choices) {
selectedChoice = currentRow - *groupIt;
}
}
}
// Finally write value to target vector.
*resultIt = currentValue;
if (choices && compare(currentValue, oldSelectedChoiceValue)) {
*choiceIt = selectedChoice;
}
}
}
}
private:
Compare compare;
std::vector<uint64_t> const& rowGroupIndices;
std::vector<MatrixEntry<index_type, value_type>> const& columnsAndEntries;
std::vector<uint64_t> const& rowIndications;
std::vector<ValueType> const& x;
std::vector<ValueType>& result;
std::vector<value_type> const* summand;
std::vector<uint_fast64_t>* choices;
};
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyAndReduceParallel(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
if (dir == storm::OptimizationDirection::Minimize) {
tbb::parallel_for(tbb::blocked_range<index_type>(0, rowGroupIndices.size() - 1, 100), TbbMultAddReduceFunctor<ValueType, storm::utility::ElementLess<ValueType>>(rowGroupIndices, columnsAndValues, rowIndications, vector, result, summand, choices));
} else {
tbb::parallel_for(tbb::blocked_range<index_type>(0, rowGroupIndices.size() - 1, 100), TbbMultAddReduceFunctor<ValueType, storm::utility::ElementGreater<ValueType>>(rowGroupIndices, columnsAndValues, rowIndications, vector, result, summand, choices));
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<storm::RationalFunction>::multiplyAndReduceParallel(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<storm::RationalFunction> const& vector, std::vector<storm::RationalFunction> const* summand, std::vector<storm::RationalFunction>& result, std::vector<uint_fast64_t>* choices) const {
STORM_LOG_THROW(false, storm::exceptions::NotSupportedException, "This operation is not supported.");
}
#endif
#endif
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyAndReduce(OptimizationDirection const& dir, std::vector<uint64_t> const& rowGroupIndices, std::vector<ValueType> const& vector, std::vector<ValueType> const* summand, std::vector<ValueType>& result, std::vector<uint_fast64_t>* choices) const {
// If the vector and the result are aliases, we need and temporary vector.
std::vector<ValueType>* target;
std::vector<ValueType> temporary;
if (&vector == &result) {
STORM_LOG_WARN("Vectors are aliased but are not allowed to be. Using temporary, which is potentially slow.");
temporary = std::vector<ValueType>(vector.size());
target = &temporary;
} else {
target = &result;
}
this->multiplyAndReduceForward(dir, rowGroupIndices, vector, summand, *target, choices);
if (target == &temporary) {
std::swap(temporary, result);
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::multiplyVectorWithMatrix(std::vector<value_type> const& vector, std::vector<value_type>& result) const {
const_iterator it = this->begin();
const_iterator ite;
std::vector<index_type>::const_iterator rowIterator = rowIndications.begin();
std::vector<index_type>::const_iterator rowIteratorEnd = rowIndications.end();
uint_fast64_t currentRow = 0;
for (; rowIterator != rowIteratorEnd - 1; ++rowIterator) {
for (ite = this->begin() + *(rowIterator + 1); it != ite; ++it) {
result[it->getColumn()] += it->getValue() * vector[currentRow];
}
++currentRow;
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::scaleRowsInPlace(std::vector<ValueType> const& factors) {
STORM_LOG_ASSERT(factors.size() == this->getRowCount(), "Can not scale rows: Number of rows and number of scaling factors do not match.");
uint_fast64_t row = 0;
for (auto const& factor : factors) {
for (auto& entry : getRow(row)) {
entry.setValue(entry.getValue() * factor);
}
++row;
}
}
template<typename ValueType>
void SparseMatrix<ValueType>::divideRowsInPlace(std::vector<ValueType> const& divisors) {
STORM_LOG_ASSERT(divisors.size() == this->getRowCount(), "Can not divide rows: Number of rows and number of divisors do not match.");
uint_fast64_t row = 0;
for (auto const& divisor : divisors) {
STORM_LOG_ASSERT(!storm::utility::isZero(divisor), "Can not divide row " << row << " by 0.");
for (auto& entry : getRow(row)) {
entry.setValue(entry.getValue() / divisor);
}
++row;
}
}
#ifdef STORM_HAVE_CARL
template<>
void SparseMatrix<Interval>::divideRowsInPlace(std::vector<Interval> const&) {
STORM_LOG_THROW(false, storm::exceptions::NotImplementedException, "This operation is not supported.");
}
#endif
template<typename ValueType>
typename SparseMatrix<ValueType>::const_rows SparseMatrix<ValueType>::getRows(index_type startRow, index_type endRow) const {
return const_rows(this->columnsAndValues.begin() + this->rowIndications[startRow], this->rowIndications[endRow] - this->rowIndications[startRow]);
}
template<typename ValueType>
typename SparseMatrix<ValueType>::rows SparseMatrix<ValueType>::getRows(index_type startRow, index_type endRow) {
return rows(this->columnsAndValues.begin() + this->rowIndications[startRow], this->rowIndications[endRow] - this->rowIndications[startRow]);
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_rows SparseMatrix<ValueType>::getRow(index_type row) const {
return getRows(row, row + 1);
}
template<typename ValueType>
typename SparseMatrix<ValueType>::rows SparseMatrix<ValueType>::getRow(index_type row) {
return getRows(row, row + 1);
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_rows SparseMatrix<ValueType>::getRow(index_type rowGroup, index_type offset) const {
STORM_LOG_ASSERT(rowGroup < this->getRowGroupCount(), "Row group is out-of-bounds.");
STORM_LOG_ASSERT(offset < this->getRowGroupSize(rowGroup), "Row offset in row-group is out-of-bounds.");
if (!this->hasTrivialRowGrouping()) {
return getRow(this->getRowGroupIndices()[rowGroup] + offset);
} else {
return getRow(this->getRowGroupIndices()[rowGroup] + offset);
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::rows SparseMatrix<ValueType>::getRow(index_type rowGroup, index_type offset) {
STORM_LOG_ASSERT(rowGroup < this->getRowGroupCount(), "Row group is out-of-bounds.");
STORM_LOG_ASSERT(offset < this->getRowGroupSize(rowGroup), "Row offset in row-group is out-of-bounds.");
if (!this->hasTrivialRowGrouping()) {
return getRow(this->getRowGroupIndices()[rowGroup] + offset);
} else {
STORM_LOG_ASSERT(offset == 0, "Invalid offset.");
return getRow(rowGroup + offset);
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_rows SparseMatrix<ValueType>::getRowGroup(index_type rowGroup) const {
STORM_LOG_ASSERT(rowGroup < this->getRowGroupCount(), "Row group is out-of-bounds.");
if (!this->hasTrivialRowGrouping()) {
return getRows(this->getRowGroupIndices()[rowGroup], this->getRowGroupIndices()[rowGroup + 1]);
} else {
return getRows(rowGroup, rowGroup + 1);
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::rows SparseMatrix<ValueType>::getRowGroup(index_type rowGroup) {
STORM_LOG_ASSERT(rowGroup < this->getRowGroupCount(), "Row group is out-of-bounds.");
if (!this->hasTrivialRowGrouping()) {
return getRows(this->getRowGroupIndices()[rowGroup], this->getRowGroupIndices()[rowGroup + 1]);
} else {
return getRows(rowGroup, rowGroup + 1);
}
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_iterator SparseMatrix<ValueType>::begin(index_type row) const {
return this->columnsAndValues.begin() + this->rowIndications[row];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::iterator SparseMatrix<ValueType>::begin(index_type row) {
return this->columnsAndValues.begin() + this->rowIndications[row];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_iterator SparseMatrix<ValueType>::end(index_type row) const {
return this->columnsAndValues.begin() + this->rowIndications[row + 1];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::iterator SparseMatrix<ValueType>::end(index_type row) {
return this->columnsAndValues.begin() + this->rowIndications[row + 1];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::const_iterator SparseMatrix<ValueType>::end() const {
return this->columnsAndValues.begin() + this->rowIndications[rowCount];
}
template<typename ValueType>
typename SparseMatrix<ValueType>::iterator SparseMatrix<ValueType>::end() {
return this->columnsAndValues.begin() + this->rowIndications[rowCount];
}
template<typename ValueType>
ValueType SparseMatrix<ValueType>::getRowSum(index_type row) const {
ValueType sum = storm::utility::zero<ValueType>();
for (const_iterator it = this->begin(row), ite = this->end(row); it != ite; ++it) {
sum += it->getValue();
}
return sum;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getNonconstantEntryCount() const {
index_type nonConstEntries = 0;
for( auto const& entry : *this){
if(!storm::utility::isConstant(entry.getValue())){
++nonConstEntries;
}
}
return nonConstEntries;
}
template<typename ValueType>
typename SparseMatrix<ValueType>::index_type SparseMatrix<ValueType>::getNonconstantRowGroupCount() const {
index_type nonConstRowGroups = 0;
for (index_type rowGroup=0; rowGroup < this->getRowGroupCount(); ++rowGroup) {
for (auto const& entry : this->getRowGroup(rowGroup)){
if(!storm::utility::isConstant(entry.getValue())){
++nonConstRowGroups;
break;
}
}
}
return nonConstRowGroups;
}
template<typename ValueType>
bool SparseMatrix<ValueType>::isProbabilistic() const {
storm::utility::ConstantsComparator<ValueType> comparator;
for (index_type row = 0; row < this->rowCount; ++row) {
auto rowSum = getRowSum(row);
if (!comparator.isOne(rowSum)) {
return false;
}
}
for (auto const& entry : *this) {
if (comparator.isConstant(entry.getValue())) {
if (comparator.isLess(entry.getValue(), storm::utility::zero<ValueType>())) {
return false;
}
}
}
return true;
}
template<typename ValueType>
template<typename OtherValueType>
bool SparseMatrix<ValueType>::isSubmatrixOf(SparseMatrix<OtherValueType> const& matrix) const {
// Check for matching sizes.
if (this->getRowCount() != matrix.getRowCount()) return false;
if (this->getColumnCount() != matrix.getColumnCount()) return false;
if (this->hasTrivialRowGrouping() && !matrix.hasTrivialRowGrouping()) return false;
if (!this->hasTrivialRowGrouping() && matrix.hasTrivialRowGrouping()) return false;
if (!this->hasTrivialRowGrouping() && !matrix.hasTrivialRowGrouping() && this->getRowGroupIndices() != matrix.getRowGroupIndices()) return false;
if (this->getRowGroupIndices() != matrix.getRowGroupIndices()) return false;
// Check the subset property for all rows individually.
for (index_type row = 0; row < this->getRowCount(); ++row) {
auto it2 = matrix.begin(row);
auto ite2 = matrix.end(row);
for (const_iterator it1 = this->begin(row), ite1 = this->end(row); it1 != ite1; ++it1) {
// Skip over all entries of the other matrix that are before the current entry in the current matrix.
while (it2 != ite2 && it2->getColumn() < it1->getColumn()) {
++it2;
}
if (it2 == ite2 || it1->getColumn() != it2->getColumn()) {
return false;
}
}
}
return true;
}
template<typename ValueType>
bool SparseMatrix<ValueType>::isIdentityMatrix() const {
if (this->getRowCount() != this->getColumnCount()) {
return false;
}
if (this->getNonzeroEntryCount() != this->getRowCount()) {
return false;
}
for (uint64_t row = 0; row < this->getRowCount(); ++row) {
bool rowHasEntry = false;
for (auto const& entry : this->getRow(row)) {
if (entry.getColumn() == row) {
if (!storm::utility::isOne(entry.getValue())) {
return false;
}
rowHasEntry = true;
} else {
if (!storm::utility::isZero(entry.getValue())) {
return false;
}
}
}
if (!rowHasEntry) {
return false;
}
}
return true;
}
template<typename ValueType>
std::string SparseMatrix<ValueType>::getDimensionsAsString() const {
std::string result = std::to_string(getRowCount()) + "x" + std::to_string(getColumnCount()) + " matrix (" + std::to_string(getNonzeroEntryCount()) + " non-zeroes";
if (!hasTrivialRowGrouping()) {
result += ", " + std::to_string(getRowGroupCount()) + " groups";
}
result += ")";
return result;
}
template<typename ValueType>
std::ostream& operator<<(std::ostream& out, SparseMatrix<ValueType> const& matrix) {
// Print column numbers in header.
out << "\t\t";
for (typename SparseMatrix<ValueType>::index_type i = 0; i < matrix.getColumnCount(); ++i) {
out << i << "\t";
}
out << std::endl;
// Iterate over all row groups.
for (typename SparseMatrix<ValueType>::index_type group = 0; group < matrix.getRowGroupCount(); ++group) {
out << "\t---- group " << group << "/" << (matrix.getRowGroupCount() - 1) << " ---- " << std::endl;
typename SparseMatrix<ValueType>::index_type start = matrix.hasTrivialRowGrouping() ? group : matrix.getRowGroupIndices()[group];
typename SparseMatrix<ValueType>::index_type end = matrix.hasTrivialRowGrouping() ? group + 1 : matrix.getRowGroupIndices()[group + 1];
for (typename SparseMatrix<ValueType>::index_type i = start; i < end; ++i) {
typename SparseMatrix<ValueType>::index_type nextIndex = matrix.rowIndications[i];
// Print the actual row.
out << i << "\t(\t";
typename SparseMatrix<ValueType>::index_type currentRealIndex = 0;
while (currentRealIndex < matrix.columnCount) {
if (nextIndex < matrix.rowIndications[i + 1] && currentRealIndex == matrix.columnsAndValues[nextIndex].getColumn()) {
out << std::setprecision(3) << matrix.columnsAndValues[nextIndex].getValue() << "\t";
++nextIndex;
} else {
out << "0\t";
}
++currentRealIndex;
}
out << "\t)\t" << i << std::endl;
}
}
// Print column numbers in footer.
out << "\t\t";
for (typename SparseMatrix<ValueType>::index_type i = 0; i < matrix.getColumnCount(); ++i) {
out << i << "\t";
}
out << std::endl;
return out;
}
template<typename ValueType>
void SparseMatrix<ValueType>::printAsMatlabMatrix(std::ostream& out) const {
// Iterate over all row groups.
for (typename SparseMatrix<ValueType>::index_type group = 0; group < this->getRowGroupCount(); ++group) {
STORM_LOG_ASSERT(this->getRowGroupSize(group) == 1, "Incorrect row group size.");
for (typename SparseMatrix<ValueType>::index_type i = this->getRowGroupIndices()[group]; i < this->getRowGroupIndices()[group + 1]; ++i) {
typename SparseMatrix<ValueType>::index_type nextIndex = this->rowIndications[i];
// Print the actual row.
out << i << "\t(";
typename SparseMatrix<ValueType>::index_type currentRealIndex = 0;
while (currentRealIndex < this->columnCount) {
if (nextIndex < this->rowIndications[i + 1] && currentRealIndex == this->columnsAndValues[nextIndex].getColumn()) {
out << this->columnsAndValues[nextIndex].getValue() << " ";
++nextIndex;
} else {
out << "0 ";
}
++currentRealIndex;
}
out << ";" << std::endl;
}
}
}
template<typename ValueType>
std::size_t SparseMatrix<ValueType>::hash() const {
std::size_t result = 0;
boost::hash_combine(result, this->getRowCount());
boost::hash_combine(result, this->getColumnCount());
boost::hash_combine(result, this->getEntryCount());
boost::hash_combine(result, boost::hash_range(columnsAndValues.begin(), columnsAndValues.end()));
boost::hash_combine(result, boost::hash_range(rowIndications.begin(), rowIndications.end()));
if (!this->hasTrivialRowGrouping()) {
boost::hash_combine(result, boost::hash_range(rowGroupIndices.get().begin(), rowGroupIndices.get().end()));
}
return result;
}
#ifdef STORM_HAVE_CARL
std::set<storm::RationalFunctionVariable> getVariables(SparseMatrix<storm::RationalFunction> const& matrix)
{
std::set<storm::RationalFunctionVariable> result;
for(auto const& entry : matrix) {
entry.getValue().gatherVariables(result);
}
return result;
}
#endif
// Explicitly instantiate the entry, builder and the matrix.
// double
template class MatrixEntry<typename SparseMatrix<double>::index_type, double>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<typename SparseMatrix<double>::index_type, double> const& entry);
template class SparseMatrixBuilder<double>;
template class SparseMatrix<double>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<double> const& matrix);
template double SparseMatrix<double>::getPointwiseProductRowSum(storm::storage::SparseMatrix<double> const& otherMatrix, typename SparseMatrix<double>::index_type const& row) const;
template std::vector<double> SparseMatrix<double>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<double> const& otherMatrix) const;
template bool SparseMatrix<double>::isSubmatrixOf(SparseMatrix<double> const& matrix) const;
template class MatrixEntry<uint32_t, double>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<uint32_t, double> const& entry);
// float
template class MatrixEntry<typename SparseMatrix<float>::index_type, float>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<typename SparseMatrix<float>::index_type, float> const& entry);
template class SparseMatrixBuilder<float>;
template class SparseMatrix<float>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<float> const& matrix);
template float SparseMatrix<float>::getPointwiseProductRowSum(storm::storage::SparseMatrix<float> const& otherMatrix, typename SparseMatrix<float>::index_type const& row) const;
template std::vector<float> SparseMatrix<float>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<float> const& otherMatrix) const;
template bool SparseMatrix<float>::isSubmatrixOf(SparseMatrix<float> const& matrix) const;
// int
template class MatrixEntry<typename SparseMatrix<int>::index_type, int>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<typename SparseMatrix<int>::index_type, int> const& entry);
template class SparseMatrixBuilder<int>;
template class SparseMatrix<int>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<int> const& matrix);
template bool SparseMatrix<int>::isSubmatrixOf(SparseMatrix<int> const& matrix) const;
// state_type
template class MatrixEntry<typename SparseMatrix<storm::storage::sparse::state_type>::index_type, storm::storage::sparse::state_type>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<typename SparseMatrix<storm::storage::sparse::state_type>::index_type, storm::storage::sparse::state_type> const& entry);
template class SparseMatrixBuilder<storm::storage::sparse::state_type>;
template class SparseMatrix<storm::storage::sparse::state_type>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<storm::storage::sparse::state_type> const& matrix);
template bool SparseMatrix<int>::isSubmatrixOf(SparseMatrix<storm::storage::sparse::state_type> const& matrix) const;
#ifdef STORM_HAVE_CARL
// Rational Numbers
#if defined(STORM_HAVE_CLN)
template class MatrixEntry<typename SparseMatrix<ClnRationalNumber>::index_type, ClnRationalNumber>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<uint_fast64_t, ClnRationalNumber> const& entry);
template class SparseMatrixBuilder<ClnRationalNumber>;
template class SparseMatrix<ClnRationalNumber>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<ClnRationalNumber> const& matrix);
template storm::ClnRationalNumber SparseMatrix<storm::ClnRationalNumber>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::ClnRationalNumber> const& otherMatrix, typename SparseMatrix<storm::ClnRationalNumber>::index_type const& row) const;
template std::vector<storm::ClnRationalNumber> SparseMatrix<ClnRationalNumber>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::ClnRationalNumber> const& otherMatrix) const;
template bool SparseMatrix<storm::ClnRationalNumber>::isSubmatrixOf(SparseMatrix<storm::ClnRationalNumber> const& matrix) const;
#endif
#if defined(STORM_HAVE_GMP)
template class MatrixEntry<typename SparseMatrix<GmpRationalNumber>::index_type, GmpRationalNumber>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<uint_fast64_t, GmpRationalNumber> const& entry);
template class SparseMatrixBuilder<GmpRationalNumber>;
template class SparseMatrix<GmpRationalNumber>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<GmpRationalNumber> const& matrix);
template storm::GmpRationalNumber SparseMatrix<storm::GmpRationalNumber>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::GmpRationalNumber> const& otherMatrix, typename SparseMatrix<storm::GmpRationalNumber>::index_type const& row) const;
template std::vector<storm::GmpRationalNumber> SparseMatrix<GmpRationalNumber>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::GmpRationalNumber> const& otherMatrix) const;
template bool SparseMatrix<storm::GmpRationalNumber>::isSubmatrixOf(SparseMatrix<storm::GmpRationalNumber> const& matrix) const;
#endif
// Rational Function
template class MatrixEntry<typename SparseMatrix<RationalFunction>::index_type, RationalFunction>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<uint_fast64_t, RationalFunction> const& entry);
template class SparseMatrixBuilder<RationalFunction>;
template class SparseMatrix<RationalFunction>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<RationalFunction> const& matrix);
template storm::RationalFunction SparseMatrix<storm::RationalFunction>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix, typename SparseMatrix<storm::RationalFunction>::index_type const& row) const;
template storm::RationalFunction SparseMatrix<double>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix, typename SparseMatrix<storm::RationalFunction>::index_type const& row) const;
template storm::RationalFunction SparseMatrix<float>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix, typename SparseMatrix<storm::RationalFunction>::index_type const& row) const;
template storm::RationalFunction SparseMatrix<int>::getPointwiseProductRowSum(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix, typename SparseMatrix<storm::RationalFunction>::index_type const& row) const;
template std::vector<storm::RationalFunction> SparseMatrix<RationalFunction>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix) const;
template std::vector<storm::RationalFunction> SparseMatrix<double>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix) const;
template std::vector<storm::RationalFunction> SparseMatrix<float>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix) const;
template std::vector<storm::RationalFunction> SparseMatrix<int>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::RationalFunction> const& otherMatrix) const;
template bool SparseMatrix<storm::RationalFunction>::isSubmatrixOf(SparseMatrix<storm::RationalFunction> const& matrix) const;
// Intervals
template std::vector<storm::Interval> SparseMatrix<double>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::Interval> const& otherMatrix) const;
template class MatrixEntry<typename SparseMatrix<Interval>::index_type, Interval>;
template std::ostream& operator<<(std::ostream& out, MatrixEntry<uint_fast64_t, Interval> const& entry);
template class SparseMatrixBuilder<Interval>;
template class SparseMatrix<Interval>;
template std::ostream& operator<<(std::ostream& out, SparseMatrix<Interval> const& matrix);
template std::vector<storm::Interval> SparseMatrix<Interval>::getPointwiseProductRowSumVector(storm::storage::SparseMatrix<storm::Interval> const& otherMatrix) const;
template bool SparseMatrix<storm::Interval>::isSubmatrixOf(SparseMatrix<storm::Interval> const& matrix) const;
template bool SparseMatrix<storm::Interval>::isSubmatrixOf(SparseMatrix<double> const& matrix) const;
#endif
} // namespace storage
} // namespace storm