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型定義などを追加、修正 #30

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claude-a merged 8 commits into from
Feb 9, 2025
Merged

型定義などを追加、修正 #30

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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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claude-a Feb 9, 2025
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8 changes: 8 additions & 0 deletions python_numpy/python_numpy_base.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -779,6 +779,14 @@ inline auto operator*(const Matrix<DefSparse, T, M, N, SparseAvailable_A> &A,
std::move(A.matrix * B.matrix));
}

/* Type */
template <typename A_Type, typename B_Type>
using A_Multiply_B_Type =
decltype(std::declval<A_Type>() * std::declval<B_Type>());

template <typename A_Type>
using Transpose_Type = decltype(std::declval<A_Type>().transpose());

/* Matrix Type Checker */
template <typename MatrixInput>
using Is_Dense_Matrix =
Expand Down
4 changes: 2 additions & 2 deletions python_numpy/python_numpy_linalg_eig.hpp
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Expand Up @@ -585,12 +585,12 @@ class LinalgSolverEigSparse {
}

/* Set */
inline void set_iteration_max(std::size_t iteration_max) {
inline void set_iteration_max(const std::size_t &iteration_max) {
this->_Eigen_solver.iteration_max = iteration_max;
}

inline void set_iteration_max_for_eigen_vector(
std::size_t iteration_max_for_eigen_vector) {
const std::size_t &iteration_max_for_eigen_vector) {
this->_Eigen_solver.iteration_max_for_eigen_vector =
iteration_max_for_eigen_vector;
}
Expand Down
15 changes: 8 additions & 7 deletions python_numpy/python_numpy_linalg_solver.hpp
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Expand Up @@ -304,11 +304,11 @@ class LinalgSolver {
return Matrix<DefDense, T, M, K>(this->X_1);
}

inline void set_decay_rate(const T &decay_rate_in) {
inline void set_decay_rate(const Value_Type &decay_rate_in) {
this->decay_rate = decay_rate_in;
}

inline void set_division_min(const T &division_min_in) {
inline void set_division_min(const Value_Type &division_min_in) {
this->division_min = division_min_in;
}

Expand Down Expand Up @@ -751,11 +751,11 @@ class LinalgLstsqSolver {
return Matrix<DefDense, T, N, K>(this->X_1);
}

inline void set_decay_rate(const T &decay_rate_in) {
inline void set_decay_rate(const Value_Type &decay_rate_in) {
this->decay_rate = decay_rate_in;
}

inline void set_division_min(const T &division_min_in) {
inline void set_division_min(const Value_Type &division_min_in) {
this->division_min = division_min_in;
}

Expand All @@ -771,9 +771,10 @@ class LinalgLstsqSolver {
/* Properties */
Base::Matrix::Matrix<T, N, K> X_1;

T decay_rate = static_cast<T>(0);
T division_min = static_cast<T>(DEFAULT_DIVISION_MIN_LINALG_SOLVER);
std::array<T, K> rho;
Value_Type decay_rate = static_cast<Value_Type>(0);
Value_Type division_min =
static_cast<Value_Type>(DEFAULT_DIVISION_MIN_LINALG_SOLVER);
std::array<Value_Type, K> rho;
std::array<std::size_t, K> rep_num;
};

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11 changes: 11 additions & 0 deletions python_numpy/python_numpy_transpose_operation.hpp
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Expand Up @@ -6,6 +6,7 @@
#include "python_numpy_templates.hpp"

#include <cstddef>
#include <utility>

namespace PythonNumpy {

Expand Down Expand Up @@ -103,6 +104,11 @@ A_mul_BTranspose(const Matrix<DefSparse, T, M, K, SparseAvailable_A> &A,
matrix_multiply_SparseA_mul_SparseBTranspose(A.matrix, B.matrix));
}

/* A_mul_BTranspose Type */
template <typename A_Type, typename B_Type>
using A_mul_BTranspose_Type =
decltype(A_mul_BTranspose(std::declval<A_Type>(), std::declval<B_Type>()));

/* (transpose matrix) * (matrix) */
template <typename T, std::size_t M, std::size_t K, std::size_t N>
inline auto ATranspose_mul_B(const Matrix<DefDense, T, N, M> &A,
Expand Down Expand Up @@ -199,6 +205,11 @@ ATranspose_mul_B(const Matrix<DefSparse, T, N, M, SparseAvailable_A> &A,
B.matrix));
}

/* ATranspose_mul_B Type */
template <typename A_Type, typename B_Type>
using ATranspose_mul_B_Type =
decltype(ATranspose_mul_B(std::declval<A_Type>(), std::declval<B_Type>()));

} // namespace PythonNumpy

#endif // __PYTHON_NUMPY_TRANSPOSE_OPERATION_HPP__
19 changes: 12 additions & 7 deletions test_vs/check_python_numpy.hpp
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Expand Up @@ -353,7 +353,8 @@ void CheckPythonNumpy<T>::check_python_numpy_base(void) {
tester.expect_near(Dense_mul_Dense.matrix.data, Dense_mul_Dense_answer.matrix.data, NEAR_LIMIT_STRICT,
"check DenseMatrix multiply DenseMatrix.");

auto Dense_mul_Diag = AA * B;
A_Multiply_B_Type<decltype(AA), decltype(B)>
Dense_mul_Diag = AA * B;

Matrix<DefDense, T, 4, 3> Dense_mul_Diag_answer({
{ 1, 6, 0 },
Expand Down Expand Up @@ -1846,7 +1847,7 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose(void) {
CL({ 1, 3, 2, 8, 4, 1 });

/* 転置 */
auto AT = A.transpose();
Transpose_Type<decltype(A)> AT = A.transpose();

Matrix<DefDense, T, 3, 3> AT_answer({
{1, 5, 9},
Expand All @@ -1857,7 +1858,7 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose(void) {
tester.expect_near(AT.matrix.data, AT_answer.matrix.data, NEAR_LIMIT_STRICT,
"check transpose Dense.");

auto CL_T = CL.transpose();
Transpose_Type<decltype(CL)> CL_T = CL.transpose();
auto CL_T_dense = CL_T.create_dense();

Matrix<DefDense, T, 3, 4> CL_T_answer({
Expand Down Expand Up @@ -2061,7 +2062,8 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose_operation(void) {


/* 転置積 */
auto A_At = A_mul_BTranspose(A, A);
A_mul_BTranspose_Type<decltype(A), decltype(A)> A_At
= A_mul_BTranspose(A, A);

Matrix<DefDense, T, 3, 3> A_At_answer({
{14, 31, 46},
Expand Down Expand Up @@ -2152,7 +2154,8 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose_operation(void) {
tester.expect_near(C_Bt_dense.matrix.data, C_Bt_answer.matrix.data, NEAR_LIMIT_STRICT,
"check A_mul_BTranspose Sparse and Diag.");

auto C_Ct = A_mul_BTranspose(C, C);
A_mul_BTranspose_Type<decltype(C), decltype(C)> C_Ct
= A_mul_BTranspose(C, C);
auto C_Ct_dense = C_Ct.create_dense();

Matrix<DefDense, T, 3, 3> C_Ct_answer({
Expand All @@ -2164,7 +2167,8 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose_operation(void) {
tester.expect_near(C_Ct_dense.matrix.data, C_Ct_answer.matrix.data, NEAR_LIMIT_STRICT,
"check A_mul_BTranspose Sparse and Sparse.");

auto At_A = ATranspose_mul_B(A, A);
ATranspose_mul_B_Type<decltype(A), decltype(A)> At_A
= ATranspose_mul_B(A, A);

Matrix<DefDense, T, 3, 3> At_A_answer({
{107, 94, 96},
Expand Down Expand Up @@ -2255,7 +2259,8 @@ void CheckPythonNumpy<T>::check_python_numpy_transpose_operation(void) {
tester.expect_near(Ct_B_dense.matrix.data, Ct_B_answer.matrix.data, NEAR_LIMIT_STRICT,
"check ATranspose_mul_B Sparse and Diag.");

auto Ct_C = ATranspose_mul_B(C, C);
ATranspose_mul_B_Type<decltype(C), decltype(C)> Ct_C
= ATranspose_mul_B(C, C);
auto Ct_C_dense = Ct_C.create_dense();

Matrix<DefDense, T, 3, 3> Ct_C_answer({
Expand Down