convert_to_fragmented_sparse.hpp

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#ifndef TATAMI_CONVERT_TO_FRAGMENTED_SPARSE_H
#define TATAMI_CONVERT_TO_FRAGMENTED_SPARSE_H
 
#include "FragmentedSparseMatrix.hpp"
#include "../utils/parallelize.hpp"
#include "../utils/consecutive_extractor.hpp"
 
#include <memory>
#include <vector>
 
namespace tatami {
 
template<typename Value_, typename Index_>
struct FragmentedSparseContents {
    FragmentedSparseContents(size_t n) : value(n), index(n) {}
    std::vector<std::vector<Value_> > value;
 
    std::vector<std::vector<Index_> > index;
};
 
struct RetrieveFragmentedSparseContentsOptions {
    int num_threads = 1;
};
 
template<typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(
    const Matrix<InputValue_, InputIndex_>& matrix,
    bool row,
    const RetrieveFragmentedSparseContentsOptions& options)
{
    InputIndex_ NR = matrix.nrow();
    InputIndex_ NC = matrix.ncol();
    InputIndex_ primary = (row ? NR : NC);
    InputIndex_ secondary = (row ? NC : NR);
 
    FragmentedSparseContents<StoredValue_, StoredIndex_> output(primary);
    auto& store_v = output.value;
    auto& store_i = output.index;
 
    if (row == matrix.prefer_rows()) {
        if (matrix.is_sparse()) {
            parallelize([&](int, InputIndex_ start, InputIndex_ length) -> void {
                std::vector<InputValue_> buffer_v(secondary);
                std::vector<InputIndex_> buffer_i(secondary);
                auto wrk = consecutive_extractor<true>(matrix, row, start, length);
 
                for (InputIndex_ p = start, pe = start + length; p < pe; ++p) {
                    auto range = wrk->fetch(buffer_v.data(), buffer_i.data());
                    auto& sv = store_v[p];
                    auto& si = store_i[p];
                    sv.reserve(range.number);
                    si.reserve(range.number);
 
                    for (InputIndex_ i = 0; i < range.number; ++i, ++range.value, ++range.index) {
                        if (*range.value) {
                            sv.push_back(*range.value);
                            si.push_back(*range.index);
                        }
                    }
                }
            }, primary, options.num_threads);
 
        } else {
            parallelize([&](int, InputIndex_ start, InputIndex_ length) -> void {
                std::vector<InputValue_> buffer_v(secondary);
                auto wrk = consecutive_extractor<false>(matrix, row, start, length);
 
                // Special conversion from dense to save ourselves from having to make
                // indices that we aren't really interested in.
                for (InputIndex_ p = start, pe = start + length; p < pe; ++p) {
                    auto ptr = wrk->fetch(buffer_v.data());
                    auto& sv = store_v[p];
                    auto& si = store_i[p];
 
                    for (InputIndex_ s = 0; s < secondary; ++s, ++ptr) {
                        if (*ptr) {
                            sv.push_back(*ptr);
                            si.push_back(s);
                        }
                    }
                }
            }, primary, options.num_threads);
        }
 
    } else {
        // We iterate on the matrix matrix's preferred dimension, under the
        // assumption that it may be arbitrarily costly to extract in the
        // non-preferred dim; it is thus cheaper to do cache-unfriendly inserts
        // into the output buffers. 
 
        if (matrix.is_sparse()) {
            parallelize([&](int, InputIndex_ start, InputIndex_ length) -> void {
                std::vector<InputValue_> buffer_v(primary);
                std::vector<InputIndex_> buffer_i(primary);
                auto wrk = consecutive_extractor<true>(matrix, !row, static_cast<InputIndex_>(0), secondary, start, length);
 
                for (InputIndex_ x = 0; x < secondary; ++x) {
                    auto range = wrk->fetch(buffer_v.data(), buffer_i.data());
                    for (InputIndex_ i = 0; i < range.number; ++i, ++range.value, ++range.index) {
                        if (*range.value) {
                            store_v[*range.index].push_back(*range.value);
                            store_i[*range.index].push_back(x);
                        }
                    }
                }
            }, primary, options.num_threads);
 
        } else {
            parallelize([&](int, InputIndex_ start, InputIndex_ length) -> void {
                auto wrk = consecutive_extractor<false>(matrix, !row, static_cast<InputIndex_>(0), secondary, start, length);
                std::vector<InputValue_> buffer_v(length);
 
                for (InputIndex_ x = 0; x < secondary; ++x) {
                    auto ptr = wrk->fetch(buffer_v.data());
                    for (InputIndex_ p = start, pe = start + length; p < pe; ++p, ++ptr) {
                        if (*ptr) {
                            store_v[p].push_back(*ptr);
                            store_i[p].push_back(x);
                        }
                    }
                }
            }, primary, options.num_threads);
        }
    }
 
    return output;
}
 
struct ConvertToFragmentedSparseOptions {
    int num_threads = 1;
};
 
template<
    typename Value_,
    typename Index_,
    typename StoredValue_ = Value_,
    typename StoredIndex_ = Index_,
    typename InputValue_,
    typename InputIndex_
>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(
    const Matrix<InputValue_, InputIndex_>& matrix,
    bool row,
    const ConvertToFragmentedSparseOptions& options)
{
    auto frag = retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(
        matrix,
        row,
        [&]{
            RetrieveFragmentedSparseContentsOptions ropt;
            ropt.num_threads = options.num_threads;
            return ropt;
        }()
    );
    return std::shared_ptr<Matrix<Value_, Index_> >(
        new FragmentedSparseMatrix<
            Value_, 
            Index_,
            std::vector<std::vector<StoredValue_> >,
            std::vector<std::vector<StoredIndex_> >
        >(
            matrix.nrow(), 
            matrix.ncol(), 
            std::move(frag.value), 
            std::move(frag.index),
            row, 
            []{
                FragmentedSparseMatrixOptions fopt;
                fopt.check = false; // no need for checks, as we guarantee correctness.
                return fopt;
            }()
        )
    );
}
 
// Backwards compatbility.
template<typename Value_, typename Index_, typename StoredValue_ = Value_, typename StoredIndex_ = Index_, typename InputValue_, typename InputIndex_>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(const Matrix<InputValue_, InputIndex_>* matrix, bool row, int threads = 1) {
    return convert_to_fragmented_sparse<Value_, Index_, StoredValue_, StoredIndex_>(
        *matrix,
        row,
        [&]{
            ConvertToFragmentedSparseOptions opt;
            opt.num_threads = threads;
            return opt;
        }()
    );
}
 
template<typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(const Matrix<InputValue_, InputIndex_>* matrix, bool row, int threads = 1) {
    return retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(
        *matrix,
        row,
        [&]{
            RetrieveFragmentedSparseContentsOptions opt;
            opt.num_threads = threads;
            return opt;
        }()
    );
}
 
template <bool row_, typename StoredValue_, typename StoredIndex_, typename InputValue_, typename InputIndex_>
FragmentedSparseContents<StoredValue_, StoredIndex_> retrieve_fragmented_sparse_contents(const Matrix<InputValue_, InputIndex_>* matrix, int threads = 1) {
    return retrieve_fragmented_sparse_contents<StoredValue_, StoredIndex_>(matrix, row_, threads);
}
 
template <bool row_, typename Value_, typename Index_, typename StoredValue_ = Value_, typename StoredIndex_ = Index_, typename InputValue_, typename InputIndex_>
std::shared_ptr<Matrix<Value_, Index_> > convert_to_fragmented_sparse(const Matrix<InputValue_, InputIndex_>* matrix, int threads = 1) {
    return convert_to_fragmented_sparse<Value_, Index_, StoredValue_, StoredIndex_>(matrix, row_, threads);
}
}
 
#endif