From f591127e16b5c96b1ff27c43c2c5959f01a19c85 Mon Sep 17 00:00:00 2001 From: Christos Choutouridis Date: Mon, 18 Nov 2024 00:29:04 +0200 Subject: [PATCH] A V0 implementation tested against matlab Supports: - HDF5 file load and store - Precision timing of the process to stdout - logging (verbose mode) to stdout - Command line arguments and help --- .gitignore => homework_1/.gitignore | 4 +- homework_1/Libs/matrix/include/matrix.hpp | 750 ++++++++++++++++++++++ homework_1/Makefile | 18 +- homework_1/inc/config.h | 75 +++ homework_1/inc/utils.hpp | 224 +++++++ homework_1/inc/v0.hpp | 119 ++++ homework_1/inc/v1.hpp | 16 + homework_1/matlab/.gitignore | 2 + homework_1/matlab/dist2.m | 8 +- homework_1/matlab/knnsearch2.m | 12 +- homework_1/src/main.cpp | 242 ++++--- 11 files changed, 1364 insertions(+), 106 deletions(-) rename .gitignore => homework_1/.gitignore (87%) create mode 100644 homework_1/Libs/matrix/include/matrix.hpp create mode 100644 homework_1/inc/config.h create mode 100644 homework_1/inc/utils.hpp create mode 100644 homework_1/inc/v0.hpp create mode 100644 homework_1/inc/v1.hpp create mode 100644 homework_1/matlab/.gitignore diff --git a/.gitignore b/homework_1/.gitignore similarity index 87% rename from .gitignore rename to homework_1/.gitignore index 7b1c517..902ea27 100644 --- a/.gitignore +++ b/homework_1/.gitignore @@ -3,6 +3,7 @@ bin/ out/ mat/ mtx/ +.unused/ # hpc exclude @@ -13,6 +14,3 @@ hpc_auth_sync.sh .cproject .settings/ -# matlab -*.m~ - diff --git a/homework_1/Libs/matrix/include/matrix.hpp b/homework_1/Libs/matrix/include/matrix.hpp new file mode 100644 index 0000000..dc2fb2b --- /dev/null +++ b/homework_1/Libs/matrix/include/matrix.hpp @@ -0,0 +1,750 @@ +/** + * \file matrix.hpp + * \brief A matrix abstraction implementation + * + * \author + * Christos Choutouridis AEM:8997 + * + */ +#ifndef MATRIX_HPP_ +#define MATRIX_HPP_ + +#include +#include +#include +#include +#include + +namespace mtx { + +using std::size_t; + +/* + * Small helper to strip types + */ +template +struct remove_cvref { + typedef std::remove_cv_t> type; +}; +template +using remove_cvref_t = typename remove_cvref::type; + +/*! + * Enumerator to denote the storage type of the array to use. + */ +enum class MatrixType { + DENSE, /*!< Matrix is dense */ + SPARSE, /*!< Matrix is sparse */ +}; + +/*! + * Enumerator to denote the storage type of the array to use. + */ +enum class MatrixOrder { + COLMAJOR, /*!< Matrix is column major */ + ROWMAJOR, /*!< Matrix is row major */ +}; + +/* + * Forward type declarations + */ + +template struct MatCol; +template struct MatRow; +template struct MatVal; + + /*! + * A 2-D matrix functionality over a 1-D array + * + * This is a very thin abstraction layer over a native array. + * This is tested using compiler explorer and our template produce + * almost identical assembly. + * + * The penalty hit we have is due to the fact that we use a one dimension array + * and we have to calculate the actual position from an (i,j) pair. + * The use of 1D array was our intention from the beginning, so the penalty + * was pretty much unavoidable. + * + * \tparam DataType The underling data type of the array + * \tparam IndexType The underling type for the index variables and sizes + * \tparam Type The storage type of the array + * \arg FULL For full matrix + * \arg SYMMETRIC For symmetric matrix (we use only the lower part) + */ +template +struct Matrix { + + using dataType = DataType; //!< meta:export of underling data type + using indexType = IndexType; //!< meta:export of underling index type + static constexpr MatrixOrder matrixOrder = Order; //!< meta:export of array order + static constexpr MatrixType matrixType = Type; //!< meta:export of array type + static constexpr bool symmetric = Symmetric; //!< meta:export symmetric flag + /*! + * \name Obj lifetime + */ + //! @{ + + //! Construct an empty matrix with dimensions rows x columns + Matrix(IndexType rows = IndexType{}, IndexType columns = IndexType{}) noexcept : + m_(capacity(rows, columns)), rows_(rows), cols_(columns) { } + + //! Construct a matrix by copying existing data with dimensions rows x columns + Matrix(DataType* data, IndexType rows, IndexType columns) noexcept : + m_(data, data + capacity(rows, columns)), rows_(rows), cols_(columns) { } + + //! Construct a matrix using an initializer list + Matrix(IndexType rows, IndexType columns, std::initializer_list list) + : m_(list), rows_(rows), cols_(columns) { + if (list.size() != capacity(rows, columns)) { + throw std::invalid_argument("Matrix initializer list size does not match matrix dimensions."); + } + } + + //! move ctor + Matrix(Matrix&& m) noexcept { moves(std::move(m)); } + //! move + Matrix& operator=(Matrix&& m) noexcept { moves(std::move(m)); return *this; } + Matrix(const Matrix& m) = delete; //!< No copy ctor + Matrix& operator=(const Matrix& m) = delete; //!< No copy + //! @} + + //! \name Data exposure + //! @{ + + + //! Get/Set the size of each dimension + IndexType rows() const noexcept { return rows_; } + IndexType columns() const noexcept { return cols_; } + + //! Get the interface size of the Matrix (what appears to be the size) + IndexType size() const { + return rows_ * cols_; + } + //! Set the interface size of the Matrix (what appears to be the size) + IndexType resize(IndexType rows, IndexType columns) { + rows_ = rows; + cols_ = columns; + m_.reserve(capacity(rows_, cols_)); + return capacity(rows_, cols_); + } + + //! Actual memory capacity of the symmetric matrix + static constexpr IndexType capacity(IndexType M, IndexType N) { + if constexpr (Symmetric) + return (M+1)*N/2; + else + return M*N; + } + + /* + * virtual 2D accessors + */ + DataType get (IndexType i, IndexType j) { + if constexpr (Symmetric) { + auto T = [](size_t i)->size_t { return i*(i+1)/2; }; // Triangular number of i + if constexpr (Order == MatrixOrder::COLMAJOR) { + // In column major we use the lower triangle of the matrix + if (i>=j) return m_[j*rows_ - T(j) + i]; // Lower, use our notation + else return m_[i*rows_ - T(i) + j]; // Upper, use opposite index + } + else { + // In row major we use the upper triangle of the matrix + if (i<=j) return m_[i*cols_ - T(i) + j]; // Upper, use our notation + else return m_[j*cols_ - T(j) + i]; // Lower, use opposite index + } + } + else { + if constexpr (Order == MatrixOrder::COLMAJOR) + return m_[i + j*rows_]; + else + return m_[i*cols_ + j]; + } + } + + DataType set (DataType v, IndexType i, IndexType j) { + if constexpr (Symmetric) { + auto T = [](size_t i)->size_t { return i*(i+1)/2; }; // Triangular number of i + if constexpr (Order == MatrixOrder::COLMAJOR) { + // In column major we use the lower triangle of the matrix + if (i>=j) return m_[j*rows_ - T(j) + i] = v; // Lower, use our notation + else return m_[i*rows_ - T(i) + j] = v; // Upper, use opposite index + } + else { + // In row major we use the upper triangle of the matrix + if (i<=j) return m_[i*cols_ - T(i) + j] = v; // Upper, use our notation + else return m_[j*cols_ - T(j) + i] = v; // Lower, use opposite index + } + } + else { + if constexpr (Order == MatrixOrder::COLMAJOR) + return m_[i + j*rows_] = v; + else + return m_[i*cols_ + j] = v; + } + } +// DataType operator()(IndexType i, IndexType j) { return get(i, j); } + /*! + * Return a proxy MatVal object with read and write capabilities. + * @param i The row number + * @param j The column number + * @return tHE MatVal object + */ + MatVal operator()(IndexType i, IndexType j) noexcept { + return MatVal(this, get(i, j), i, j); + } + + // a basic serial iterator support + DataType* data() noexcept { return m_.data(); } +// IndexType begin_idx() noexcept { return 0; } +// IndexType end_idx() noexcept { return capacity(rows_, cols_); } + + const DataType* data() const noexcept { return m_.data(); } + const IndexType begin_idx() const noexcept { return 0; } + const IndexType end_idx() const noexcept { return capacity(rows_, cols_); } + //! @} + + /*! + * \name Safe iteration API + * + * This api automates the iteration over the array based on + * MatrixType + */ + //! @{ + template + void for_each_in (IndexType begin, IndexType end, F&& lambda, Args&&... args) { + for (IndexType it=begin ; it(lambda)(std::forward(args)..., it); + } + } + //! @} + + // + void swap(Matrix& src) noexcept { + std::swap(m_, src.m_); + std::swap(rows_, src.rows_); + std::swap(cols_, src.cols_); + } +private: + //! move helper + void moves(Matrix&& src) noexcept { + m_ = std::move(src.m_); + rows_ = std::move(src.rows_); + cols_ = std::move(src.cols_); + } + + std::vector m_ {}; //!< Pointer to actual data. + IndexType rows_{}; //!< the virtual size of rows. + IndexType cols_{}; //!< the virtual size of columns. +}; + + +/** + * A simple sparse matrix specialization. + * + * We use CSC format and provide get/set functionalities for each (i,j) item + * on the matrix. We also provide a () overload using a proxy MatVal object. + * This way the user can: + * \code + * auto v = A(3,4); + * A(3, 4) = 7; + * \endcode + * + * We also provide getCol() and getRow() functions witch return a viewer/iterator to rows and + * columns of the matrix. In the case of a symmetric matrix instead of a row we return the + * equivalent column. This way we gain speed due to CSC format nature. + * + * @tparam DataType The type for values + * @tparam IndexType The type for indexes + * @tparam Type The Matrix type (FULL or SYMMETRIC) + */ +template +struct Matrix { + + using dataType = DataType; //!< meta:export of underling data type + using indexType = IndexType; //!< meta:export of underling index type + static constexpr MatrixOrder matrixOrder = Order; //!< meta:export of array order + static constexpr MatrixType matrixType = MatrixType::SPARSE; //!< meta:export of array type + static constexpr bool symmetric = Symmetric; //!< meta:export symmetric flag + + friend struct MatCol; + friend struct MatRow; + friend struct MatVal; + + /*! + * \name Obj lifetime + */ + //! @{ + + //! Default ctor with optional memory allocations + Matrix(IndexType n=IndexType{}) noexcept: + values{}, + rows{}, + col_ptr((n)? n+1:2, IndexType{}), + N(n), + NNZ(0) { } + + //! A ctor using csc array data + Matrix(IndexType n, IndexType nnz, const IndexType* row, const IndexType* col) noexcept: + values(nnz, 1), + rows(row, row+nnz), + col_ptr(col, col+n+1), + N(n), + NNZ(nnz) { } + + //! ctor using csc array data with value array + Matrix(IndexType n, IndexType nnz, const DataType* v, const IndexType* row, const IndexType* col) noexcept: + values(v, v+nnz), + rows(row, row+nnz), + col_ptr(col, col+n+1), + N(n), + NNZ(nnz) { } + + //! ctor vectors of row/col and default value for values array + Matrix(IndexType n, IndexType nnz, const DataType v, + const std::vector& row, const std::vector& col) noexcept: + values(nnz, v), + rows (row), + col_ptr(col), + N(n), + NNZ(nnz) { } + + //! move ctor + Matrix(Matrix&& m) noexcept { moves(std::move(m)); } + //! move + Matrix& operator=(Matrix&& m) noexcept { moves(std::move(m)); return *this; } + Matrix(const Matrix& m) = delete; //!< make sure there are no copies + Matrix& operator=(const Matrix& m) = delete; //!< make sure there are no copies + //! @} + + //! \name Data exposure + //! @{ + + //! \return the dimension of the matrix + IndexType size() noexcept { return N; } + //! After construction size configuration tool + IndexType resize(IndexType n) { + col_ptr.resize(n+1); + return N = n; + } + //! \return the NNZ of the matrix + IndexType capacity() noexcept { return NNZ; } + //! After construction NNZ size configuration tool + IndexType capacity(IndexType nnz) noexcept { + values.reserve(nnz); + rows.reserve(nnz); + return NNZ; + } + // getters for row arrays of the struct (unused) + std::vector& getValues() noexcept { return values; } + std::vector& getRows() noexcept { return rows; } + std::vector& getCols() noexcept { return col_ptr; } + + /*! + * Return a proxy MatVal object with read and write capabilities. + * @param i The row number + * @param j The column number + * @return tHE MatVal object + */ + MatVal operator()(IndexType i, IndexType j) noexcept { + return MatVal(this, get(i, j), i, j); + } + + /*! + * A read item functionality using binary search to find the correct row + * + * @param i The row number + * @param j The column number + * @return The value of the item or DataType{} if is not present. + */ + DataType get(IndexType i, IndexType j) noexcept { + IndexType idx; bool found; + std::tie(idx, found) =find_idx(rows, col_ptr[j], col_ptr[j+1], i); + return (found) ? values[idx] : 0; + } + + /*! + * A write item functionality. + * + * First we search if the matrix has already a value in (i, j) position. + * If so we just change it to a new value. If not we add the item on the matrix. + * + * @note + * When change a value, we don't increase the NNZ value of the struct. We expect the user has already + * change the NNZ value to the right one using @see capacity() function. When adding a value we + * increase the NNZ. + * + * @param i The row number + * @param j The column number + * @return The new value of the item . + */ + DataType set(DataType v, IndexType i, IndexType j) { + IndexType idx; bool found; + std::tie(idx, found) = find_idx(rows, col_ptr[j], col_ptr[j+1], i); + if (found) + return values[idx] = v; // we don't change NNZ even if we write "0" + else { + values.insert(values.begin()+idx, v); + rows.insert(rows.begin()+idx, i); + std::transform(col_ptr.begin()+j+1, col_ptr.end(), col_ptr.begin()+j+1, [](IndexType it) { + return ++it; + }); + ++NNZ; // we increase the NNZ even if we write "0" + return v; + } + } + + /*! + * Get a view of a CSC column + * @param j The column to get + * @return The MatCol object @see MatCol + */ + MatCol getCol(IndexType j) noexcept { + return MatCol(this, col_ptr[j], col_ptr[j+1]); + } + + /*! + * Get a view of a CSC row + * + * In case of a SYMMETRIC matrix we can return a column instead. + * + * @param j The row to get + * @return On symmetric matrix MatCol otherwise a MatRow + */ + + MatCol getRow(IndexType i) noexcept { + if constexpr (Symmetric) + return getCol(i); + else + return MatRow(this, i); + } + + // values only iterator support + DataType* begin() noexcept { return values.begin(); } + DataType* end() noexcept { return values.end(); } + //! @} + + //! A small iteration helper + template + void for_each_in (IndexType begin, IndexType end, F&& lambda, Args&&... args) { + for (IndexType it=begin ; it(lambda)(std::forward(args)..., it); + } + } + +private: + /*! + * A small binary search implementation using index for begin-end instead of iterators. + * + * \param v Reference to vector to search + * \param begin The vector's index to begin + * \param end The vector's index to end + * \param match What to search + * \return An pair. + * index is the index of the item or end if not found + * status is true if found, false otherwise + */ + std::pair find_idx(const std::vector& v, IndexType begin, IndexType end, IndexType match) { + if (v.capacity() != 0 && begin < end) { + IndexType b = begin, e = end-1; + while (b <= e) { + IndexType m = (b+e)/2; + if (v[m] == match) return std::make_pair(m, true); + else if (b >= e) return std::make_pair(end, false); + else { + if (v[m] < match) b = m +1; + else e = m -1; + } + } + } + return std::make_pair(end, false); + } + + // move helper + void moves(Matrix&& src) noexcept { + values = std::move(src.values); + rows = std::move(src.rows); + col_ptr = std::move(src.col_ptr); + N = std::move(src.N); // redundant for primitives + NNZ = std::move(src.NNZ); // + } + //! \name Data + //! @{ + std::vector values {}; //!< vector to store the values of the matrix + std::vector rows{}; //!< vector to store the row information + std::vector col_ptr{1,0}; //!< vector to store the column pointers + IndexType N{0}; //!< The dimension of the matrix (square) + IndexType NNZ{0}; //!< The NNZ (capacity of the matrix) + //! @} +}; + + +/*! + * A view/iterator hybrid object for Matrix columns. + * + * This object provides access to a column of a Matrix. The public functionalities + * allow data access using indexes instead of iterators. We prefer indexes over iterators + * because we can apply the same index to different inner vector of Matrix without conversion. + * + * @tparam DataType + * @tparam IndexType + */ +template +struct MatCol { + using owner_t = MatrixType; + + using DataType = typename MatrixType::dataType; + using IndexType = typename MatrixType::indexType; + + /*! + * ctor using column pointers for begin-end. own is pointer to Matrix. + */ + MatCol(owner_t* own, const IndexType begin, const IndexType end) noexcept : + owner_(own), index_(begin), begin_(begin), end_(end) { + vindex_ = vIndexCalc(index_); + } + MatCol() = default; + MatCol(const MatCol&) = delete; //!< make sure there are no copies + MatCol& operator=(const MatCol&)= delete; //!< make sure there are no copies + MatCol(MatCol&&) = default; + MatCol& operator=(MatCol&&) = default; + + //! a simple dereference operator, like an iterator + DataType operator* () { + return get(); + } + //! Increment operator acts on index(), like an iterator + MatCol& operator++ () { advance(); return *this; } + MatCol& operator++ (int) { MatCol& p = *this; advance(); return p; } + + //! () operator acts as member access (like a view) + DataType operator()(IndexType x) { + return (x == index())? get() : DataType{}; + } + //! = operator acts as member assignment (like a view) + DataType operator= (DataType v) { return owner_->values[index_] = v; } + // iterator like handlers + // these return a virtual index value based on the items position on the full matrix + // but the move of the index is just a ++ away. + IndexType index() noexcept { return vindex_; } + const IndexType index() const noexcept { return vindex_; } + IndexType begin() noexcept { return vIndexCalc(begin_); } + const IndexType begin() const noexcept { return vIndexCalc(begin_); } + IndexType end() noexcept { return owner_->N; } + const IndexType end() const noexcept { return owner_->N; } + + /*! + * Multiplication operator + * + * We follow only the non-zero values and multiply only the common indexes. + * + * @tparam C Universal reference for the type right half site column + * + * @param c The right hand site matrix + * @return The value of the inner product of two vectors + * @note The time complexity is \$ O(nnz1+nnz2) \$. + * Where the nnz is the max NNZ elements of the column of the matrix + */ + template + DataType operator* (C&& c) { + static_assert(std::is_same, MatCol>(), ""); + DataType v{}; + while (index() != end() && c.index() != c.end()) { + if (index() < c.index()) advance(); // advance me + else if (index() > c.index()) ++c; // advance other + else { //index() == c.index() + v += get() * *c; // multiply and advance both + ++c; + advance(); + } + } + return v; + } + +private: + //! small tool to increase the index pointers to Matrix + void advance() noexcept { + ++index_; + vindex_ = vIndexCalc(index_); + } + //! tool to translate between col_ptr indexes and Matrix "virtual" full matrix indexes + IndexType vIndexCalc(IndexType idx) { + return (idx < end_) ? owner_->rows[idx] : end(); + } + //! small get tool + DataType get() { return owner_->values[index_]; } + + owner_t* owner_ {nullptr}; //!< Pointer to owner Matrix. MatCol is just a view + IndexType vindex_ {IndexType{}}; //!< Virtual index of full matrix + IndexType index_ {IndexType{}}; //!< index to Matrix::rows + IndexType begin_ {IndexType{}}; //!< beginning index of the column in Matrix::rows + IndexType end_ {IndexType{}}; //!< ending index of the column in Matrix::rows +}; + +/*! + * A view/iterator hybrid object for Matrix rows. + * + * This object provides access to a column of a Matrix. The public functionalities + * allow data access using indexes instead of iterators. We prefer indexes over iterators + * because we can apply the same index to different inner vector of Matrix without conversion. + * + * @tparam DataType + * @tparam IndexType + */ +template +struct MatRow { + using owner_t = MatrixType; + + using DataType = typename MatrixType::dataType; + using IndexType = typename MatrixType::indexType; + + /*! + * ctor using virtual full matrix row index. own is pointer to Matrix. + */ + MatRow(owner_t* own, const IndexType row) noexcept : + owner_(own), vindex_(IndexType{}), row_(row), index_(IndexType{}), + begin_(IndexType{}), end_(owner_->NNZ) { + // place begin + while(begin_ != end_ && owner_->rows[begin_] != row_) + ++begin_; + // place index_ and vindex_ + if (owner_->rows[index_] != row_) + advance(); + } + MatRow() = default; + MatRow(const MatRow&) = delete; //!< make sure there are no copies + MatRow& operator=(const MatRow&)= delete; //!< make sure there are no copies + MatRow(MatRow&&) = default; + MatRow& operator=(MatRow&&) = default; + + //! a simple dereference operator, like an iterator + DataType operator* () { + return get(); + } + //! Increment operator acts on index(), like an iterator + //! here the increment is a O(N) process. + MatRow& operator++ () { advance(); return *this; } + MatRow& operator++ (int) { MatRow& p = *this; advance(); return p; } + + //! () operator acts as member access (like a view) + DataType operator()(IndexType x) { + return (x == index())? get() : DataType{}; + } + //! = operator acts as member assignment (like a view) + DataType operator= (DataType v) { return owner_->values[index_] = v; } + // iterator like handlers + // these return a virtual index value based on the items position on the full matrix + // but the move of the index is just a ++ away. + IndexType index() noexcept { return vindex_; } + const IndexType index() const noexcept { return vindex_; } + IndexType begin() noexcept { return vIndexCalc(begin_); } + const IndexType begin() const noexcept { return vIndexCalc(begin_); } + IndexType end() noexcept { return owner_->N; } + const IndexType end() const noexcept { return owner_->N; } + + /*! + * Multiplication operator + * + * We follow only the non-zero values and multiply only the common indexes. + * + * @tparam C Universal reference for the type right half site column + * + * @param c The right hand site matrix + * @return The value of the inner product of two vectors + * @note The time complexity is \$ O(N+nnz2) \$ and way heavier the ColxCol multiplication. + * Where the nnz is the max NNZ elements of the column of the matrix + */ + template + DataType operator* (C&& c) { + static_assert(std::is_same, MatCol>(), ""); + DataType v{}; + while (index() != end() && c.index() != c.end()) { + if (index() < c.index()) advance(); // advance me + else if (index() > c.index()) ++c; // advance other + else { //index() == c.index() + v += get() * *c; // multiply and advance both + ++c; + advance(); + } + } + return v; + } +private: + //! small tool to increase the index pointers to Matrix matrix + //! We have to search the entire rows vector in Matrix to find the next + //! virtual row position. + //! time complexity O(N) + void advance() noexcept { + do + ++index_; + while(index_ != end_ && owner_->rows[index_] != row_); + vindex_ = vIndexCalc(index_); + } + //! tool to translate between col_ptr indexes and Matrix "virtual" full matrix indexes + IndexType vIndexCalc(IndexType idx) { + for(IndexType i =0 ; i<(owner_->N+1) ; ++i) + if (idx < owner_->col_ptr[i]) + return i-1; + return end(); + } + //! small get tool + DataType get() { return owner_->values[index_]; } + + owner_t* owner_ {nullptr}; //!< Pointer to owner Matrix. MatCol is just a view + IndexType vindex_ {IndexType{}}; //!< Virtual index of full matrix + IndexType row_ {IndexType{}}; //!< The virtual full matrix row of the object + IndexType index_ {IndexType{}}; //!< index to Matrix::rows + IndexType begin_ {IndexType{}}; //!< beginning index of the column in Matrix::rows + IndexType end_ {IndexType{}}; //!< ending index of the column in Matrix::rows +}; + +/*! + * A proxy Matrix value object/view. + * + * This object acts as proxy to provide read/write access to an Matrix item. + * + * @tparam DataType The type of the values of the Matrix matrix + * @tparam IndexType The type of the indexes of the Matrix matrix + */ +template +struct MatVal { + using owner_t = MatrixType; + + using DataType = typename MatrixType::dataType; + using IndexType = typename MatrixType::indexType; + + //!< ctor using all value-row-column data, plus a pointer to owner Matrix object + MatVal(owner_t* own, DataType v, IndexType i, IndexType j) : + owner_(own), v_(v), i_(i), j_(j) { } + MatVal() = default; + MatVal(const MatVal&) = delete; //!< make sure there are no copies + MatVal& operator=(const MatVal&) = delete; //!< make sure there are no copies + MatVal(MatVal&&) = default; + MatVal& operator=(MatVal&&) = default; + + //! Operator to return the DataType value implicitly + operator DataType() { return v_; } + //! Operator to write back to owner the assigned value + //! for ex: A(2,3) = 5; + MatVal& operator=(DataType v) { + v_ = v; + owner_->set(v_, i_, j_); + return *this; + } +private: + owner_t* owner_{nullptr}; //!< Pointer to owner Matrix. MatVal is just a view. + DataType v_{DataType{}}; //!< The value of the row-column pair (for speed) + IndexType i_{IndexType{}}; //!< The row + IndexType j_{IndexType{}}; //!< the column +}; + + +} // namespace mtx + + +#endif /* MATRIX_HPP_ */ diff --git a/homework_1/Makefile b/homework_1/Makefile index 3c5593b..4da2071 100644 --- a/homework_1/Makefile +++ b/homework_1/Makefile @@ -26,7 +26,11 @@ TARGET := knnsearch SRC_DIR_LIST := src # Include directories list(space seperated). Makefile-relative path. INC_DIR_LIST := inc \ - src + src \ + Libs/matrix/include/ \ + /usr/include/hdf5/serial/ +# Libs/MATLAB/R2019b/include/ \ + # Exclude files list(space seperated). Filenames only. # EXC_FILE_LIST := bad.cpp old.cpp @@ -49,7 +53,11 @@ PRE_DEFS := # ============== Linker settings ============== # Linker flags (example: -pthread -lm) -LDFLAGS := -pthread -lopenblas +LDFLAGS := -pthread -lopenblas \ + -L/usr/lib/x86_64-linux-gnu/hdf5/serial -lhdf5 +# -LLibs/MATLAB/R2019b/bin/ -lmat -lmx -Wl,-rpath,Libs/MATLAB/R2019b/bin/ \ +# -Wl,-rpath,Libs/unwind/bin/ + # Map output file MAP_FILE := output.map MAP_FLAG := -Xlinker -Map=$(BUILD_DIR)/$(MAP_FILE) @@ -184,6 +192,12 @@ local_v0: TARGET := local_v0 local_v0: $(BUILD_DIR)/$(TARGET) cp $(BUILD_DIR)/$(TARGET) out/$(TARGET) +local_v0_opt: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=0 +local_v0_opt: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=0 +local_v0_opt: TARGET := local_v0_opt +local_v0_opt: $(BUILD_DIR)/$(TARGET) + cp $(BUILD_DIR)/$(TARGET) out/$(TARGET) + local_v1: CFLAGS := $(DEB_CFLAGS) -DCODE_VERSION=4 local_v1: CXXFLAGS := $(DEB_CXXFLAGS) -DCODE_VERSION=4 local_v1: TARGET := local_v1 diff --git a/homework_1/inc/config.h b/homework_1/inc/config.h new file mode 100644 index 0000000..007e11d --- /dev/null +++ b/homework_1/inc/config.h @@ -0,0 +1,75 @@ +/*! + * \file config,h + * \brief Build configuration file. + * + * \author + * Christos Choutouridis AEM:8997 + * + */ + +#ifndef CONFIG_H_ +#define CONFIG_H_ + +#include +#include + +#include + +// HDF5 supported types +enum class HDF5_type { + SCHAR, CHAR, SHORT, USHORT, INT, UINT, LONG, ULONG, LLONG, ULLONG, FLOAT, DOUBLE +}; + +/* + * Defines for different version of the exercise + */ +#define V0 0 +#define V1 1 + + +// Fail-safe version selection +#if !defined CODE_VERSION + #define CODE_VERSION V1 +#endif + +// matrix alias template dispatcher based on pre-define flag from compiler (see Makefile) +#if CODE_VERSION == V0 + #define NAMESPACE_VERSION using namespace v0 + using MatrixDst = mtx::Matrix; + using MatrixIdx = mtx::Matrix; + static constexpr HDF5_type DstHDF5Type = HDF5_type::DOUBLE; + static constexpr HDF5_type IdxHDF5Type = HDF5_type::INT; +#elif CODE_VERSION == V1 + #define NAMESPACE_VERSION using namespace v1 + using MatrixDst = mtx::Matrix; + using MatrixIdx = mtx::Matrix; + static constexpr HDF5_type DstHDF5Type = HDF5_type::DOUBLE; + static constexpr HDF5_type IdxHDF5Type = HDF5_type::INT; +#endif + + + +//! enumerator for output handling +enum class StdOutputMode{ STD, FILE }; + +/*! + * Session option for each invocation of the executable + */ +struct session_t { + std::string corpusMtxFile {}; //!< corpus matrix file name in HDF5 format + std::string corpusDataSet {}; //!< corpus dataset name in HDF5 matrix file + std::string queryMtxFile {}; //!< optional query matrix file name in HDF5 format + std::string queryDataSet {}; //!< optional query dataset name in HDF5 matrix file + bool queryMtx {false}; //!< Flag to indicate that there is a separate query matrix + size_t k {1}; //!< The number of nearest neighbors to find + std::string outMtxFile {"out.hdf5"}; //!< output matrix file name in HDF5 format + std::string outMtxIdxDataSet {"/Idx"}; //!< Index output dataset name in HDF5 matrix file + std::string outMtxDstDataSet {"/Dst"}; //!< Distance output dataset name in HDF5 matrix file + std::size_t max_threads {}; //!< Maximum threads to use + bool timing {false}; //!< Enable timing prints of the program + bool verbose {false}; //!< Flag to enable verbose output to stdout +}; + +extern session_t session; + +#endif /* CONFIG_H_ */ diff --git a/homework_1/inc/utils.hpp b/homework_1/inc/utils.hpp new file mode 100644 index 0000000..0a9322d --- /dev/null +++ b/homework_1/inc/utils.hpp @@ -0,0 +1,224 @@ +/** + * \file utils.hpp + * \brief Utilities header + * + * \author + * Christos Choutouridis AEM:8997 + * + */ +#ifndef UTILS_HPP_ +#define UTILS_HPP_ + +#include +#include +#include +#include + +#include +#include + +/*! + * A Logger for entire program. + */ +struct Log { + struct Endl {} endl; //!< a tag object to to use it as a new line request. + + //! We provide logging via << operator + template + Log& operator<< (T&& t) { + if (session.verbose) { + if (line_) { + std::cout << "[Log]: " << t; + line_ = false; + } + else + std::cout << t; + } + return *this; + } + // overload for special end line handling + Log& operator<< (Endl e) { (void)e; + if (session.verbose) { + std::cout << '\n'; + line_ = true; + } + return *this; + } +private: + bool line_ {true}; +}; + +extern Log logger; + +/*! + * A small timing utility based on chrono. + */ +struct Timing{ + using Tpoint = std::chrono::steady_clock::time_point; + using microseconds = std::chrono::microseconds; + using milliseconds = std::chrono::milliseconds; + using seconds = std::chrono::seconds; + + //! tool to mark the starting point + Tpoint start () noexcept { return start_ = std::chrono::steady_clock::now(); } + //! tool to mark the ending point + Tpoint stop () noexcept { return stop_ = std::chrono::steady_clock::now(); } + + auto dt () noexcept { + return std::chrono::duration_cast(stop_ - start_).count(); + } + //! tool to print the time interval + void print_dt (const char* what) noexcept { + if (session.timing) { + auto t = stop_ - start_; + if (std::chrono::duration_cast(t).count() < 10000) + std::cout << "[Timing]: " << what << ": " << std::to_string(std::chrono::duration_cast(t).count()) << " [usec]\n"; + else if (std::chrono::duration_cast(t).count() < 10000) + std::cout << "[Timing]: " << what << ": " << std::to_string(std::chrono::duration_cast(t).count()) << " [msec]\n"; + else + std::cout << "[Timing]: " << what << ": " << std::to_string(std::chrono::duration_cast(t).count()) << " [sec]\n"; + } + } +private: + Tpoint start_; + Tpoint stop_; +}; + + + +struct Mtx { + + template + static void load(const std::string& filename, const std::string& dataset, MatrixType& matrix) { + + hid_t file_id{}, dataset_id{}, dataspace_id{}; + herr_t read_st; + do { + // Open file + logger << "Load HDF5 file: " << filename << " Dataset: " << dataset << "..."; + if ((file_id = H5Fopen(filename.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT)) < 0) + break; + + // Open dataset + if ((dataset_id = H5Dopen2(file_id, dataset.c_str(), H5P_DEFAULT)) < 0) + break; + + // Get dataspace and allocate memory for read buffer + if ((dataspace_id = H5Dget_space(dataset_id)) < 0) + break; + hsize_t dims[2]; + H5Sget_simple_extent_dims(dataspace_id, dims, NULL); + matrix.resize(dims[0], dims[1]); + + // Read the dataset + // ToDo: Come up with a better way to do this + if constexpr (Type == HDF5_type::DOUBLE) { + if ((read_st = H5Dread(dataset_id, H5T_NATIVE_DOUBLE, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) < 0) + break; + } + else if (Type == HDF5_type::FLOAT) { + if ((read_st = H5Dread(dataset_id, H5T_NATIVE_FLOAT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) < 0) + break; + } + else if (Type == HDF5_type::UINT) { + if ((read_st = H5Dread(dataset_id, H5T_NATIVE_UINT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) < 0) + break; + } + else if (Type == HDF5_type::INT) { + if ((read_st = H5Dread(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) < 0) + break; + } + // Done + H5Dclose(dataset_id); + H5Sclose(dataspace_id); + H5Fclose(file_id); + logger << " Done" << logger.endl; + return; + } while (0); + + // Error: close everything (if possible) and return false + H5Dclose(dataset_id); + H5Sclose(dataspace_id); + H5Fclose(file_id); + throw std::runtime_error("Cannot store to " + filename + " dataset:" + dataset + '\n'); + } + + + template + static void store(const std::string& filename, const std::string& dataset, MatrixType& matrix) { + + hid_t file_id{}, dataset_id{}, dataspace_id{}; + herr_t write_st; + do { + // Try to open the file in read-write mode + logger << "Store HDF5 file: " << filename << " Dataset: " << dataset << "..."; + if (access(session.outMtxFile.c_str(), F_OK) == 0){ + if ((file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT)) < 0) + break; + } + else { + if ((file_id = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT, H5P_DEFAULT)) < 0) + break; + } + + // Create the dataspace for the dataset + hsize_t dims[] = { matrix.rows(), matrix.columns() }; + if ((dataspace_id = H5Screate_simple(2, dims, NULL)) < 0) + break; + + // ToDo: Come up with a better way to do this + if constexpr (Type == HDF5_type::DOUBLE) { + // Create the dataset with default properties + if ((dataset_id = H5Dcreate2( + file_id, dataset.c_str(), H5T_NATIVE_DOUBLE, dataspace_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT)) < 0) + break; + // Write the data to the dataset + if ((write_st = H5Dwrite(dataset_id, H5T_NATIVE_DOUBLE, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) <0 ) + break; + } + else if (Type == HDF5_type::FLOAT) { + // Create the dataset with default properties + if ((dataset_id = H5Dcreate2( + file_id, dataset.c_str(), H5T_NATIVE_FLOAT, dataspace_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT)) < 0) + break; + // Write the data to the dataset + if ((write_st = H5Dwrite(dataset_id, H5T_NATIVE_FLOAT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) <0 ) + break; + } + else if (Type == HDF5_type::UINT) { + // Create the dataset with default properties + if ((dataset_id = H5Dcreate2( + file_id, dataset.c_str(), H5T_NATIVE_UINT, dataspace_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT)) < 0) + break; + // Write the data to the dataset + if ((write_st = H5Dwrite(dataset_id, H5T_NATIVE_UINT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) <0 ) + break; + } + else if (Type == HDF5_type::INT) { + // Create the dataset with default properties + if ((dataset_id = H5Dcreate2( + file_id, dataset.c_str(), H5T_NATIVE_INT, dataspace_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT)) < 0) + break; + // Write the data to the dataset + if ((write_st = H5Dwrite(dataset_id, H5T_NATIVE_INT, H5S_ALL, H5S_ALL, H5P_DEFAULT, matrix.data())) <0 ) + break; + } + // Close the dataset, dataspace, and file + H5Dclose(dataset_id); + H5Sclose(dataspace_id); + H5Fclose(file_id); + logger << " Done" << logger.endl; + return; + } while (0); + + // Error: close everything (if possible) and return false + H5Dclose(dataset_id); + H5Sclose(dataspace_id); + H5Fclose(file_id); + throw std::runtime_error("Cannot store " + filename + " with dataset:" + dataset +'\n'); + } +}; + + + +#endif /* UTILS_HPP_ */ diff --git a/homework_1/inc/v0.hpp b/homework_1/inc/v0.hpp new file mode 100644 index 0000000..7414ee0 --- /dev/null +++ b/homework_1/inc/v0.hpp @@ -0,0 +1,119 @@ +/** + * \file v0.hpp + * \brief + * + * \author + * Christos Choutouridis AEM:8997 + * + */ +#ifndef V0_HPP_ +#define V0_HPP_ + +#include +#include +#include +#include + +#include +#include + +namespace v0 { + +/*! + * Function to compute squared Euclidean distances + * + * \fn void pdist2(const double*, const double*, double*, int, int, int) + * \param X m x d matrix (Column major) + * \param Y n x d matrix (Column major) + * \param D2 m x n matrix to store distances (Column major) + * \param m number of rows in X + * \param n number of rows in Y + * \param d number of columns in both X and Y + */ +template +void pdist2(const mtx::Matrix& X, const mtx::Matrix& Y, mtx::Matrix& D2) { + int M = X.rows(); + int N = Y.rows(); + int d = X.columns(); + + // Compute the squared norms of each row in X and Y + std::vector X_norms(M), Y_norms(N); + for (int i = 0; i < M ; ++i) { + X_norms[i] = cblas_ddot(d, X.data() + i * d, 1, X.data() + i * d, 1); + } + for (int j = 0; j < N ; ++j) { + Y_norms[j] = cblas_ddot(d, Y.data() + j * d, 1, Y.data() + j * d, 1); + } + + // Compute -2 * X * Y' + cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasTrans, M, N, d, -2.0, X.data(), d, Y.data(), d, 0.0, D2.data(), N); + + // Step 3: Add the squared norms to each entry in D2 + for (int i = 0; i < M ; ++i) { + for (int j = 0; j < N; ++j) { + D2.set(D2.get(i, j) + X_norms[i] + Y_norms[j], i, j); + //D2.set(std::max(D2.get(i, j), 0.0), i, j); // Ensure non-negative + D2.set(std::sqrt(D2.get(i, j)), i, j); // Take the square root of each + } + } +} + +template +void quickselect(std::vector>& vec, int k) { + std::nth_element( + vec.begin(), + vec.begin() + k, + vec.end(), + [](const std::pair& a, const std::pair& b) { + return a.first < b.first; + }); + vec.resize(k); // Keep only the k smallest elements +} + +/*! + * \param C Is a MxD matrix (Corpus) + * \param Q Is a NxD matrix (Query) + * \param k The number of nearest neighbors needed + * \param idx Is the Nxk matrix with the k indexes of the C points, that are + * neighbors of the nth point of Q + * \param dst Is the Nxk matrix with the k distances to the C points of the nth + * point of Q + */ +template +void knnsearch(const mtx::Matrix& C, const mtx::Matrix& Q, int k, + mtx::Matrix& idx, + mtx::Matrix& dst) { + + int M = C.rows(); + int N = Q.rows(); + + mtx::Matrix D(M, N); + + pdist2(C, Q, D); + + idx.resize(N, k); + dst.resize(N, k); + + for (int j = 0; j < N; ++j) { + // Create a vector of pairs (distance, index) for the j-th query + std::vector> dst_idx(M); + for (int i = 0; i < M; ++i) { + dst_idx[i] = {D.data()[i * N + j], i}; + } + // Find the k smallest distances using quickSelectKSmallest + quickselect(dst_idx, k); + + // Sort the k smallest results by distance for consistency + std::sort(dst_idx.begin(), dst_idx.end()); + + // Store the indices and distances + for (int i = 0; i < k; ++i) { + idx(j, i) = dst_idx[i].second; + dst(j, i) = dst_idx[i].first; + } + } +} + +} + +#endif /* V0_HPP_ */ diff --git a/homework_1/inc/v1.hpp b/homework_1/inc/v1.hpp new file mode 100644 index 0000000..a50ff68 --- /dev/null +++ b/homework_1/inc/v1.hpp @@ -0,0 +1,16 @@ +/** + * \file v0.hpp + * \brief + * + * \author + * Christos Choutouridis AEM:8997 + * + */ +#ifndef V1_HPP_ +#define V1_HPP_ + + + + + +#endif /* V1_HPP_ */ diff --git a/homework_1/matlab/.gitignore b/homework_1/matlab/.gitignore new file mode 100644 index 0000000..6a1298a --- /dev/null +++ b/homework_1/matlab/.gitignore @@ -0,0 +1,2 @@ +# matlab +*.m~ diff --git a/homework_1/matlab/dist2.m b/homework_1/matlab/dist2.m index a6451ce..75ab988 100644 --- a/homework_1/matlab/dist2.m +++ b/homework_1/matlab/dist2.m @@ -1,10 +1,12 @@ function [D2] = dist2(X, Y) % Calculates the squares of the distances of X and Y % -% X: A mxd array with m d-dimentional points -% Y: A nxd array with n d-dimentional points +% X: A Mxd array with m d-dimentional points +% Y: A Nxd array with n d-dimentional points % d: Must be the same - +% +% D2: The MxN matrix with the distances +% [~, d1] = size(X); [~, d2] = size(Y); if d1 ~= d2 diff --git a/homework_1/matlab/knnsearch2.m b/homework_1/matlab/knnsearch2.m index 560694d..5d53c66 100644 --- a/homework_1/matlab/knnsearch2.m +++ b/homework_1/matlab/knnsearch2.m @@ -1,8 +1,12 @@ function [idx, dst] = knnsearch2(C, Q, k) -% C: Is a mxd matrix (Corpus) -% Q: Is a nxd matrix (Query) -% k: The number of nearest neighbors needded - +% C: Is a MxD matrix (Corpus) +% Q: Is a NxD matrix (Query) +% k: The number of nearest neighbors needded +% idx: Is the Nxk matrix with the k indexes of the C points, that are +% neighbors of the nth point of Q +% dst: Is the Nxk matrix with the k distances to the C points of the nth +% point of Q +% % Calculate the distance matrix between C and Q % D is an m x n matrix where each element D(i, j) is the distance % between the i-th point in C and the j-th point in Q. diff --git a/homework_1/src/main.cpp b/homework_1/src/main.cpp index 425332d..ca93770 100644 --- a/homework_1/src/main.cpp +++ b/homework_1/src/main.cpp @@ -1,107 +1,161 @@ -#include -#include -#include -#include -#include -#include - /*! - * Function to compute squared Euclidean distances + * \file main.cpp + * \brief Main application file * - * \fn void pdist2(const double*, const double*, double*, int, int, int) - * \param X m x d matrix - * \param Y n x d matrix - * \param D2 m x n matrix to store distances - * \param m number of rows in X - * \param n number of rows in Y - * \param d number of columns in both X and Y + * \author + * Christos Choutouridis AEM:8997 + * */ -void pdist2(const double* X, const double* Y, double* D2, int m, int n, int d){ - // Compute the squared norms of each row in X and Y - std::vector X_norms(m), Y_norms(n); - for (int i = 0; i < m; ++i) { - X_norms[i] = cblas_ddot(d, X + i * d, 1, X + i * d, 1); - } - for (int j = 0; j < n; ++j) { - Y_norms[j] = cblas_ddot(d, Y + j * d, 1, Y + j * d, 1); - } - // Compute -2 * X * Y' - cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasTrans, m, n, d, -2.0, X, d, Y, d, 0.0, D2, n); +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +// Global session data +session_t session; +Log logger; +Timing timer; - // Step 3: Add the squared norms to each entry in D2 - for (int i = 0; i < m; ++i) { - for (int j = 0; j < n; ++j) { - D2[i * n + j] += X_norms[i] + Y_norms[j]; - D2[i * n + j] = std::max(D2[i * n + j], 0.0); // Ensure non-negative - D2[i * n + j] = std::sqrt(D2[i * n + j]); // Take the square root of each +/*! + * A small command line argument parser + * \return The status of the operation + */ +bool get_options(int argc, char* argv[]){ + bool status =true; + + // iterate over the passed arguments + for (int i=1 ; i [-k ] [-o ] [-q ] [-n ] [-t] [-v]\n"; + std::cout << '\n'; + std::cout << "Options:\n\n"; + std::cout << " -c | --corpus \n"; + std::cout << " Path to hdf5 file to open and name of the dataset to load\n\n"; + std::cout << " -o | --output \n"; + std::cout << " Path to to store the data and the names of the datasets.\n\n"; + std::cout << " -q | --query \n"; + std::cout << " Path to hdf5 file to open and name of the dataset to load\n"; + std::cout << " If not defined, the corpus is used\n\n"; + std::cout << " -k \n"; + std::cout << " Set the number of closest neighbors to find. \n\n"; + std::cout << " -n | --max_trheads \n"; + std::cout << " Reduce the thread number for the execution to . must be less or equal to available CPUs.\n\n"; + std::cout << " -t | --timing\n"; + std::cout << " Request timing measurements output to stdout.\n\n"; + std::cout << " -v | --verbose\n"; + std::cout << " Request a more verbose output to stdout.\n\n"; + std::cout << " -h | --help \n"; + std::cout << " Prints this and exit.\n\n"; + std::cout << "Examples:\n\n"; + std::cout << " ...Example case...:\n"; + std::cout << " > ./annsearch -i ... \n\n"; + + exit(0); + } + else { // parse error + std::cout << "Invocation error. Try -h for details.\n"; + status = false; } } -} - -void quickselect(std::vector>& vec, int k) { - std::nth_element( - vec.begin(), - vec.begin() + k, - vec.end(), - [](const std::pair& a, const std::pair& b) { - return a.first < b.first; - }); - vec.resize(k); // Keep only the k smallest elements -} -// K-nearest neighbor search function -void knnsearch(const double* C, const double* Q, int m, int n, int d, int k, - std::vector>& idx, std::vector>& dst) { - std::vector D(m * n); - pdist2(C, Q, D.data(), m, n, d); - - idx.resize(n, std::vector(k)); - dst.resize(n, std::vector(k)); - - for (int j = 0; j < n; ++j) { - // Create a vector of pairs (distance, index) for the j-th query - std::vector> dst_idx(m); - for (int i = 0; i < m; ++i) { - dst_idx[i] = {D[i * n + j], i}; - } - - // Find the k smallest distances using quickSelectKSmallest - quickselect(dst_idx, k); - - // Sort the k smallest results by distance for consistency - std::sort(dst_idx.begin(), dst_idx.end()); - - // Store the indices and distances - for (int i = 0; i < k; ++i) { - idx[j][i] = dst_idx[i].second; - dst[j][i] = dst_idx[i].first; - } - } + return status; } -int main(){ - - int m = 5; // Number of points in C (corpus) - int n = 3; // Number of points in Q (query) - int d = 2; // Dimensions - int k = 2; // Number of nearest neighbors to find - - double C[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}; // m x d matrix - double Q[] = {1.5, 2.5, 3.5, 4.5, 5.5, 6.5}; // n x d matrix - - std::vector> idx; - std::vector> dst; - - knnsearch(C, Q, m, n, d, k, idx, dst); - - // Print results - for (int i = 0; i < n; ++i) { - std::cout << "Query point " << i << ":\n"; - for (int j = 0; j < k; ++j) { - std::cout << " Neighbor " << j <<": Index = " << idx[i][j] <<", Distance = " << dst[i][j] << '\n'; - } - } +NAMESPACE_VERSION; + +int main(int argc, char* argv[]) try { + // Instantiate matrixes + MatrixDst Corpus; + MatrixDst Query; + MatrixIdx Idx; + MatrixDst Dst; + + // try to read command line + if (!get_options(argc, argv)) + exit(1); + + if (access(session.outMtxFile.c_str(), F_OK) == 0) + std::remove(session.outMtxFile.c_str()); + + // Load data + timer.start(); + Mtx::load(session.corpusMtxFile, session.corpusDataSet, Corpus); + if (session.queryMtx) + Mtx::load(session.corpusMtxFile, session.corpusDataSet, Query); + timer.stop(); + timer.print_dt("Load hdf5 files"); + + logger << "Start knnsearch ..."; + timer.start(); + if (session.queryMtx) + knnsearch(Corpus, Query, session.k, Idx, Dst); + else + knnsearch(Corpus, Corpus, session.k, Idx, Dst); + timer.stop(); + logger << " Done" << logger.endl; + timer.print_dt("knnsearch"); + + + // Store data + timer.start(); + Mtx::store(session.outMtxFile, session.outMtxIdxDataSet, Idx); + Mtx::store(session.outMtxFile, session.outMtxDstDataSet, Dst); + timer.stop(); + timer.print_dt("Store hdf5 files"); return 0; } +catch (std::exception& e) { + //we probably pollute the user's screen. Comment `cerr << ...` if you don't like it. + std::cerr << "Error: " << e.what() << '\n'; + exit(1); +} + +