FESADev/src/fesa/math/matrix.cpp

#include "fesa/math/matrix.hpp"

#include <mkl.h>

#include <limits>
#include <stdexcept>
#include <utility>

namespace fesa {
namespace {

std::size_t checkedStorageSize(const std::size_t rows, const std::size_t columns) {
    // Reject shape multiplication overflow before logical dimensions and storage diverge.
    if (columns != 0 &&
        rows > (std::numeric_limits<std::size_t>::max)() / columns) {
        throw std::length_error{"Dense matrix dimensions exceed the storage size range."};
    }
    return rows * columns;
}

MKL_INT toMklSize(const std::size_t size) {
    if (size > static_cast<std::size_t>((std::numeric_limits<MKL_INT>::max)())) {
        throw std::length_error{"Dense matrix dimension exceeds the MKL integer range."};
    }
    return static_cast<MKL_INT>(size);
}

void copyValues(const std::vector<double>& source, std::vector<double>& destination) {
    if (source.empty()) {
        return;
    }

    cblas_dcopy(toMklSize(source.size()), source.data(), 1, destination.data(), 1);
}

} // namespace

Matrix::Matrix(
    const std::size_t rows,
    const std::size_t columns,
    const double value)
    : rows_(rows), columns_(columns), values_(checkedStorageSize(rows, columns), value) {}

Matrix::Matrix(const Matrix& other)
    : rows_(other.rows_), columns_(other.columns_), values_(other.values_.size()) {
    copyValues(other.values_, values_);
}

Matrix::Matrix(Matrix&& other) noexcept
    : rows_(other.rows_),
      columns_(other.columns_),
      values_(std::move(other.values_)) {
    other.rows_ = 0;
    other.columns_ = 0;
    other.values_.clear();
}

Matrix& Matrix::operator=(const Matrix& other) {
    if (this != &other) {
        std::vector<double> copied(other.values_.size());
        copyValues(other.values_, copied);
        rows_ = other.rows_;
        columns_ = other.columns_;
        values_.swap(copied);
    }
    return *this;
}

Matrix& Matrix::operator=(Matrix&& other) noexcept {
    if (this != &other) {
        rows_ = other.rows_;
        columns_ = other.columns_;
        values_ = std::move(other.values_);
        other.rows_ = 0;
        other.columns_ = 0;
        other.values_.clear();
    }
    return *this;
}

std::size_t Matrix::rows() const noexcept {
    return rows_;
}

std::size_t Matrix::columns() const noexcept {
    return columns_;
}

double& Matrix::operator()(const std::size_t row, const std::size_t column) {
    if (row >= rows_ || column >= columns_) {
        throw std::out_of_range{"Matrix index is outside its dimensions."};
    }
    return values_[row * columns_ + column];
}

const double& Matrix::operator()(const std::size_t row, const std::size_t column) const {
    if (row >= rows_ || column >= columns_) {
        throw std::out_of_range{"Matrix index is outside its dimensions."};
    }
    return values_[row * columns_ + column];
}

Vector Matrix::multiply(const Vector& rhs) const {
    if (columns_ != rhs.size()) {
        throw std::invalid_argument{"Matrix-vector multiplication has incompatible dimensions."};
    }

    Vector result{rows_};
    if (rows_ == 0 || columns_ == 0) {
        return result;
    }

    // The owned layout is row-major, so the leading dimension is the column
    // count for the adapter call and remains invisible to public consumers.
    cblas_dgemv(
        CblasRowMajor,
        CblasNoTrans,
        toMklSize(rows_),
        toMklSize(columns_),
        1.0,
        values_.data(),
        toMklSize(columns_),
        rhs.data(),
        1,
        0.0,
        result.data(),
        1);
    return result;
}

Matrix Matrix::multiply(const Matrix& rhs) const {
    if (columns_ != rhs.rows_) {
        throw std::invalid_argument{"Matrix multiplication has incompatible dimensions."};
    }

    Matrix result{rows_, rhs.columns_};
    if (rows_ == 0 || columns_ == 0 || rhs.columns_ == 0) {
        return result;
    }

    cblas_dgemm(
        CblasRowMajor,
        CblasNoTrans,
        CblasNoTrans,
        toMklSize(rows_),
        toMklSize(rhs.columns_),
        toMklSize(columns_),
        1.0,
        values_.data(),
        toMklSize(columns_),
        rhs.values_.data(),
        toMklSize(rhs.columns_),
        0.0,
        result.values_.data(),
        toMklSize(rhs.columns_));
    return result;
}

} // namespace fesa