Bausager
cb65174cf4
fixed rowwise/colswise mean/sum and implemented binomial_corssentrhopy. Next up is regression.
64 lines
1.4 KiB
C++
64 lines
1.4 KiB
C++
#pragma once
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#include <cstdint> //uint64_t
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//#include <stdexcept> // std::runtime_error
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#include "utils/vector.h"
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#include "utils/matrix.h"
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namespace numerics::detail{
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// ---------------- Matrix -> Scalar ----------------
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template <typename T>
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T sum_serial(const utils::Matrix<T>& A) {
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const uint64_t rows = A.rows();
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const uint64_t cols = A.cols();
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T sum = T{0};
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for (uint64_t i = 0; i < rows; ++i){
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for (uint64_t j = 0; j < cols; ++j){
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sum += A(i,j);
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}
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}
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return sum;
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}
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// ---------------- Vector -> Scalar ----------------
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template <typename T>
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T sum_serial(const utils::Vector<T>& v) {
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const uint64_t N = v.size();
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T sum = T{0};
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for (uint64_t i = 0; i < N; ++i){
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sum += v[i];
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}
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return sum;
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}
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// ---------------- Matrix -> Vector ----------------
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template <typename T>
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utils::Vector<T> sum_rowwise_serial(const utils::Matrix<T>& A) {
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const uint64_t rows = A.rows();
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const uint64_t cols = A.cols();
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utils::Vector<T> sum(rows, T{0});
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for (uint64_t i = 0; i < rows; ++i){
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for (uint64_t j = 0; j < cols; ++j){
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sum[i] += A(i,j);
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}
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}
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return sum;
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}
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template <typename T>
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utils::Vector<T> sum_colwise_serial(const utils::Matrix<T>& A) {
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const uint64_t rows = A.rows();
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const uint64_t cols = A.cols();
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utils::Vector<T> sum(cols, T{0});
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for (uint64_t i = 0; i < rows; ++i){
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for (uint64_t j = 0; j < cols; ++j){
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sum[j] += A(i,j);
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}
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}
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return sum;
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}
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} // namespace numerics
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