#include "densematrix.h"
#include <random>
#include <stdexcept>
#include <thread>
#include <utility>
#include "utils.h"
#include "vector.h"
namespace fasttext {
DenseMatrix::DenseMatrix() : DenseMatrix(0, 0) {}
DenseMatrix::DenseMatrix(int64_t m, int64_t n) : Matrix(m, n), data_(m * n) {}
DenseMatrix::DenseMatrix(DenseMatrix&& other) noexcept
: Matrix(other.m_, other.n_), data_(std::move(other.data_)) {}
DenseMatrix::DenseMatrix(int64_t m, int64_t n, real* dataPtr)
: Matrix(m, n), data_(dataPtr, dataPtr + (m * n)) {}
void DenseMatrix::zero() {
std::fill(data_.begin(), data_.end(), 0.0);
}
void DenseMatrix::uniformThread(real a, int block, int32_t seed) {
std::minstd_rand rng(block + seed);
std::uniform_real_distribution<> uniform(-a, a);
int64_t blockSize = (m_ * n_) / 10;
for (int64_t i = blockSize * block;
i < (m_ * n_) && i < blockSize * (block + 1);
i++) {
data_[i] = uniform(rng);
}
}
void DenseMatrix::uniform(real a, unsigned int thread, int32_t seed) {
if (thread > 1) {
std::vector<std::thread> threads;
for (int i = 0; i < thread; i++) {
threads.push_back(std::thread([=]() { uniformThread(a, i, seed); }));
}
for (int32_t i = 0; i < threads.size(); i++) {
threads[i].join();
}
} else {
uniformThread(a, 0, seed);
}
}
void DenseMatrix::multiplyRow(const Vector& nums, int64_t ib, int64_t ie) {
if (ie == -1) {
ie = m_;
}
assert(ie <= nums.size());
for (auto i = ib; i < ie; i++) {
real n = nums[i - ib];
if (n != 0) {
for (auto j = 0; j < n_; j++) {
at(i, j) *= n;
}
}
}
}
void DenseMatrix::divideRow(const Vector& denoms, int64_t ib, int64_t ie) {
if (ie == -1) {
ie = m_;
}
assert(ie <= denoms.size());
for (auto i = ib; i < ie; i++) {
real n = denoms[i - ib];
if (n != 0) {
for (auto j = 0; j < n_; j++) {
at(i, j) /= n;
}
}
}
}
real DenseMatrix::l2NormRow(int64_t i) const {
auto norm = 0.0;
for (auto j = 0; j < n_; j++) {
norm += at(i, j) * at(i, j);
}
if (std::isnan(norm)) {
throw EncounteredNaNError();
}
return std::sqrt(norm);
}
void DenseMatrix::l2NormRow(Vector& norms) const {
assert(norms.size() == m_);
for (auto i = 0; i < m_; i++) {
norms[i] = l2NormRow(i);
}
}
real DenseMatrix::dotRow(const Vector& vec, int64_t i) const {
assert(i >= 0);
assert(i < m_);
assert(vec.size() == n_);
real d = 0.0;
for (int64_t j = 0; j < n_; j++) {
d += at(i, j) * vec[j];
}
if (std::isnan(d)) {
throw EncounteredNaNError();
}
return d;
}
void DenseMatrix::addVectorToRow(const Vector& vec, int64_t i, real a) {
assert(i >= 0);
assert(i < m_);
assert(vec.size() == n_);
for (int64_t j = 0; j < n_; j++) {
data_[i * n_ + j] += a * vec[j];
}
}
void DenseMatrix::addRowToVector(Vector& x, int32_t i) const {
assert(i >= 0);
assert(i < this->size(0));
assert(x.size() == this->size(1));
for (int64_t j = 0; j < n_; j++) {
x[j] += at(i, j);
}
}
void DenseMatrix::addRowToVector(Vector& x, int32_t i, real a) const {
assert(i >= 0);
assert(i < this->size(0));
assert(x.size() == this->size(1));
for (int64_t j = 0; j < n_; j++) {
x[j] += a * at(i, j);
}
}
void DenseMatrix::save(std::ostream& out) const {
out.write((char*)&m_, sizeof(int64_t));
out.write((char*)&n_, sizeof(int64_t));
out.write((char*)data_.data(), m_ * n_ * sizeof(real));
}
void DenseMatrix::load(std::istream& in) {
in.read((char*)&m_, sizeof(int64_t));
in.read((char*)&n_, sizeof(int64_t));
data_ = std::vector<real>(m_ * n_);
in.read((char*)data_.data(), m_ * n_ * sizeof(real));
}
void DenseMatrix::dump(std::ostream& out) const {
out << m_ << " " << n_ << std::endl;
for (int64_t i = 0; i < m_; i++) {
for (int64_t j = 0; j < n_; j++) {
if (j > 0) {
out << " ";
}
out << at(i, j);
}
out << std::endl;
}
};
}