792 lines
30 KiB
C++
792 lines
30 KiB
C++
#include <iostream>
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#include "Function.h"
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#include "Function2D.h"
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#include "Function1D.h"
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//必须在Eigen之前
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#include "AuroraDefs.h"
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#include <Eigen/Core>
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#include <Eigen/Eigen>
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#include <Eigen/Dense>
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using namespace Aurora;
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double Aurora::immse(const Aurora::Matrix &aImageA, const Aurora::Matrix &aImageB) {
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if (aImageA.getDims()!=2|| aImageB.getDims()!=2){
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std::cerr<<"Fail!immse args must all 2d matrix!";
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return 0.0;
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}
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if (!aImageB.compareShape(aImageA)){
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std::cerr<<"Fail!immse args must be same shape!";
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return 0.0;
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}
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if (aImageA.getValueType()!=Normal || aImageB.getValueType() != Normal) {
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std::cerr << "Fail!immse args must be normal value type!";
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return 0.0;
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}
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int size = aImageA.getDataSize();
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auto temp = malloc(size);
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vdSub(size, aImageA.getData(), aImageB.getData(), temp);
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vdSqr(size, temp, temp);
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double result = cblas_dasum(size, temp, 1) / (double) size;
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free(temp);
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return result;
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}
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Aurora::Matrix Aurora::inv(const Aurora::Matrix &aMatrix) {
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if (aMatrix.getDims() != 2) {
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std::cerr << "Fail!inv args must be 2d matrix!";
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return aMatrix;
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}
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if (aMatrix.getDimSize(0) != aMatrix.getDimSize(1)) {
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std::cerr << "Fail!inv args must be square matrix!";
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return aMatrix;
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}
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if (aMatrix.getValueType() != Normal) {
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std::cerr << "Fail!inv args must be normal value type!";
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return aMatrix;
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}
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int size = aMatrix.getDataSize();
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int *ipiv = new int[aMatrix.getDimSize(0)];
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auto result = malloc(size);
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cblas_dcopy(size,aMatrix.getData(), 1,result, 1);
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LAPACKE_dgetrf(LAPACK_ROW_MAJOR, aMatrix.getDimSize(0), aMatrix.getDimSize(0), result, aMatrix.getDimSize(0), ipiv);
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LAPACKE_dgetri(LAPACK_ROW_MAJOR, aMatrix.getDimSize(0), result, aMatrix.getDimSize(0), ipiv);
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delete[] ipiv;
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return Matrix::New(result,aMatrix);
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}
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Aurora::Matrix Aurora::inv(Aurora::Matrix&& aMatrix) {
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if (aMatrix.getDims() != 2) {
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std::cerr << "Fail!inv args must be 2d matrix!";
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return aMatrix;
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}
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if (aMatrix.getDimSize(0) != aMatrix.getDimSize(1)) {
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std::cerr << "Fail!inv args must be square matrix!";
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return aMatrix;
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}
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if (aMatrix.getValueType() != Normal) {
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std::cerr << "Fail!inv args must be normal value type!";
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return aMatrix;
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}
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int *ipiv = new int[aMatrix.getDimSize(0)];
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LAPACKE_dgetrf(LAPACK_ROW_MAJOR, aMatrix.getDimSize(0), aMatrix.getDimSize(0), aMatrix.getData(), aMatrix.getDimSize(0), ipiv);
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LAPACKE_dgetri(LAPACK_ROW_MAJOR, aMatrix.getDimSize(0), aMatrix.getData(), aMatrix.getDimSize(0), ipiv);
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delete[] ipiv;
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return aMatrix;
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}
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Matrix Aurora::interp2(const Matrix& aX, const Matrix& aY, const Matrix& aV, const Matrix& aX1, const Matrix& aY1, InterpnMethod aMethod)
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{
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if (aV.getDims() != 2)
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{
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return Matrix();
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}
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if (aX1.getDimSize(0) != aY1.getDimSize(0))
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{
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return Matrix();
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}
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int columnNum = aV.getDimSize(1);
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int rowNum = aV.getDimSize(0);
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if(aX.getDimSize(0) != columnNum || aY.getDimSize(0) != rowNum)
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{
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return Matrix();
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}
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int nx1 = aX1.getDimSize(0);
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std::shared_ptr<double> resultData = std::shared_ptr<double>(Aurora::malloc(nx1), Aurora::free);
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for (int i = 0; i < nx1; ++i)
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{
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std::shared_ptr<double> xResultData = std::shared_ptr<double>(Aurora::malloc(columnNum), Aurora::free);
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for(int j =0; j < columnNum; ++j)
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{
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xResultData.get()[j] = interp1(aY,aV($,j).toMatrix(),aY1(i).toMatrix(),aMethod).getData()[0];
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}
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Matrix xResult(xResultData,std::vector<int>{columnNum});
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resultData.get()[i] = interp1(aX,xResult,aX1(i).toMatrix(),aMethod).getData()[0];
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}
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return Matrix(resultData,std::vector<int>{nx1});
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}
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Matrix Aurora::interpn(const Matrix& aX, const Matrix& aY, const Matrix& aV, const Matrix& aX1, const Matrix& aY1, InterpnMethod aMethod)
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{
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return Aurora::interp2(aY,aX,aV,aY1,aX1,aMethod);
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}
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Matrix Aurora::std(const Matrix &aMatrix) {
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if (aMatrix.getDimSize(2) > 1 || aMatrix.isComplex()) {
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std::cerr
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<< (aMatrix.getDimSize(2) > 1 ? "std() not support 3D data!" : "std() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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Matrix src = aMatrix.isComplex() ? Aurora::abs(aMatrix) : aMatrix.deepCopy();
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int calc_size = src.getDimSize(0) == 1 ? src.getDimSize(1) : src.getDimSize(0);
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int col = src.getDimSize(0) == 1?1:src.getDimSize(1) ;
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auto std = Aurora::malloc(col);
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for (int i = 0; i < col; ++i) {
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double *p = src.getData() + i * calc_size;
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double mean = cblas_dasum(calc_size, p, 1) / calc_size;
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vdSubI(calc_size, p, 1, &mean, 0, p, 1);
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vdSqr(calc_size, p, p);
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std[i] = cblas_dasum(calc_size, p, 1) / (calc_size - 1);
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}
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vdSqrt(col, std, std);
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return Matrix::New(std, 1, col, aMatrix.getDimSize(2));
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}
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Matrix Aurora::min(const Matrix &aMatrix, FunctionDirection direction, long& rowIdx, long& colIdx) {
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if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
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std::cerr
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<< (aMatrix.getDimSize(2) > 1 ? "min() not support 3D data!" : "min() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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//针对向量行等于列
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if (direction == Column && aMatrix.getDimSize(0)==1){
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direction = All;
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}
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switch (direction)
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{
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case All: {
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Eigen::Map<Eigen::MatrixXd> retV(aMatrix.getData(), aMatrix.getDimSize(0), aMatrix.getDimSize(1));
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double *ret = malloc(1);
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ret[0] = retV.array().minCoeff(&rowIdx, &colIdx);
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return Matrix::New(ret, 1);
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}
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case Row:
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{
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Eigen::Map<Eigen::MatrixXd> srcMatrix(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(0));
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if (aMatrix.getDimSize(0) == 1){
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ret[0] = srcMatrix.topRows(0).minCoeff(&rowIdx, &colIdx);
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}
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else{
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Eigen::Map<Eigen::MatrixXd> retMatrix(ret,aMatrix.getDimSize(0),1);
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retMatrix = srcMatrix.rowwise().minCoeff();
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}
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return Matrix::New(ret,aMatrix.getDimSize(0),1);
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}
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case Column:
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default:
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{
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Eigen::Map<Eigen::MatrixXd> srcMatrix(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(1));
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if (aMatrix.getDimSize(1) == 1){
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ret[0] = srcMatrix.col(0).minCoeff(&rowIdx, &colIdx);
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}
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else {
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Eigen::Map<Eigen::MatrixXd> retMatrix(ret,1,aMatrix.getDimSize(1));
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retMatrix = srcMatrix.colwise().minCoeff();
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}
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return Matrix::New(ret,1,aMatrix.getDimSize(1));
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}
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}
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}
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Matrix Aurora::min(const Matrix &aMatrix, FunctionDirection direction) {
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long a,b;
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return min(aMatrix,direction,a,b);
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}
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Matrix Aurora::min(const Matrix &aMatrix, const Matrix &aOther) {
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if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
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std::cerr
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<< (aMatrix.getDimSize(2) > 1 ? "min() not support 3D data!" : "min() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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if (aOther.getDimSize(2)>1 || aOther.isComplex()) {
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std::cerr
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<< (aOther.getDimSize(2) > 1 ? "min() not support 3D data!" : "min() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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//same shape
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if (aMatrix.compareShape(aOther)){
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double* output = malloc(aMatrix.getDataSize());
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vdFminI(aMatrix.getDataSize(),aMatrix.getData(),1,aOther.getData(),1,output,1);
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return Matrix::New(output,aMatrix);
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}
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// one is scalar
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else if (aMatrix.getDataSize() == 1 || aOther.getDataSize() == 1){
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double scalar = (aMatrix.getDataSize() == 1)?aMatrix.getData()[0]:aOther.getData()[0];
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auto matrix = (aMatrix.getDataSize() == 1)?aOther:aMatrix;
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double* output = malloc(matrix.getDataSize());
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vdFminI(matrix.getDataSize(),matrix.getData(),1,&scalar,0,output,1);
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return Matrix::New(output,matrix);
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}
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else if (aMatrix.getDimSize(1) == 1 || aOther.getDimSize(0) == 1) {
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if (aMatrix.getDimSize(1) == 1){
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double* output = malloc(aOther.getDataSize());
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for (int i = 0; i < aOther.getDimSize(1); ++i) {
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vdFminI(aMatrix.getDataSize(), aMatrix.getData(), 1, aOther.getData() + aOther.getDimSize(0) * i, 1,
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output + aOther.getDimSize(0) * i, 1);
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}
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return Matrix::New(output,aOther);
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}
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else{
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double* output = malloc(aMatrix.getDataSize());
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for (int i = 0; i < aMatrix.getDimSize(0); ++i) {
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vdFminI(aOther.getDataSize(), aOther.getData(), 1, aMatrix.getData() + i, aMatrix.getDimSize(0),
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output + i, aOther.getDimSize(0));
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}
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return Matrix::New(output,aMatrix);
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}
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}
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else{
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std::cerr
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<< "min(A,B) with matrix must be like A[MxN] - B[1xN] or A[Mx1] - B[MxN]"
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<< std::endl;
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return Matrix();
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}
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}
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Matrix Aurora::max(const Matrix &aMatrix, FunctionDirection direction) {
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long a,b;
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return max(aMatrix,direction,a,b);
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}
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Matrix Aurora::max(const Matrix &aMatrix, FunctionDirection direction, long& rowIdx, long& colIdx) {
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if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
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std::cerr
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<< (aMatrix.getDimSize(2) > 1 ? "max() not support 3D data!" : "max() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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//针对向量行等于列
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if (direction == Column && aMatrix.getDimSize(0)==1){
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direction = All;
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}
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switch (direction)
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{
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case All:
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{
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Eigen::Map<Eigen::MatrixXd> retV(aMatrix.getData(), aMatrix.getDimSize(0), aMatrix.getDimSize(1));
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double *ret = malloc(1);
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ret[0] = retV.array().maxCoeff(&rowIdx, &colIdx);
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return Matrix::New(ret,1);
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}
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case Row:
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{
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Eigen::Map<Eigen::MatrixXd> srcMatrix(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(0));
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if (aMatrix.getDimSize(0) == 1){
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ret[0] = srcMatrix.topRows(0).maxCoeff(&rowIdx, &colIdx);
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}
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else{
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Eigen::Map<Eigen::MatrixXd> retMatrix(ret,aMatrix.getDimSize(0),1);
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retMatrix = srcMatrix.rowwise().maxCoeff();
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}
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return Matrix::New(ret,aMatrix.getDimSize(0),1);
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}
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case Column:
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default:
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{
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Eigen::Map<Eigen::MatrixXd> srcMatrix(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(0));
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if (aMatrix.getDimSize(1) == 1){
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ret[0] = srcMatrix.col(0).maxCoeff(&rowIdx, &colIdx);
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}
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else {
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Eigen::Map<Eigen::MatrixXd> retMatrix(ret,1,aMatrix.getDimSize(1));
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retMatrix = srcMatrix.colwise().maxCoeff();
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}
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return Matrix::New(ret,1,aMatrix.getDimSize(1));
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}
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}
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}
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Matrix Aurora::sum(const Matrix &aMatrix, FunctionDirection direction) {
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if (aMatrix.getDimSize(2)>1 ) {
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std::cerr<< "sum() not support 3D data!"
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<< std::endl;
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return Matrix();
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}
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//针对向量行等于列
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if (direction == Column && aMatrix.getDimSize(0)==1){
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direction = Row;
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}
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if (aMatrix.isComplex()){
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switch (direction)
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{
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case All:
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{
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Eigen::Map<Eigen::VectorXcd> srcV((std::complex<double>*)aMatrix.getData(),aMatrix.getDataSize());
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std::complex<double>* ret = (std::complex<double>*)malloc(1,true);
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ret[0] = srcV.array().sum();
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return Matrix::New((double*)ret,1,1,1,Complex);
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}
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case Row:
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{
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Eigen::Map<Eigen::MatrixXcd> srcM((std::complex<double>*)aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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std::complex<double> * ret = (std::complex<double>*)malloc(aMatrix.getDimSize(0),true);
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Eigen::Map<Eigen::VectorXcd> retV(ret,aMatrix.getDimSize(0));
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retV = srcM.rowwise().sum();
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return Matrix::New((double*)ret,aMatrix.getDimSize(0),1,1,Complex);
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}
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case Column:
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default:
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{
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Eigen::Map<Eigen::MatrixXcd> srcM((std::complex<double>*)aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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std::complex<double>* ret = (std::complex<double>*)malloc(aMatrix.getDimSize(1),true);
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Eigen::Map<Eigen::VectorXcd> retV(ret,aMatrix.getDimSize(1));
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retV = srcM.colwise().sum();
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return Matrix::New((double*)ret,1,aMatrix.getDimSize(1),1,Complex);
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}
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}
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}
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else{
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switch (direction)
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{
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case All:
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{
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Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
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double * ret = malloc(1);
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ret[0] = srcV.array().sum();
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return Matrix::New(ret,1);
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}
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case Row:
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{
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Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(0));
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Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
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retV = srcM.rowwise().sum();
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return Matrix::New(ret,aMatrix.getDimSize(0),1);
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}
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case Column:
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default:
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{
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Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(1));
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Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(1));
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retV = srcM.colwise().sum();
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return Matrix::New(ret,1,aMatrix.getDimSize(1));
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}
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}
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}
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}
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Matrix Aurora::mean(const Matrix &aMatrix, FunctionDirection direction, bool aIncludeNan) {
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if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
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std::cerr
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<< (aMatrix.getDimSize(2) > 1 ? "sum() not support 3D data!" : "sum() not support complex value type!")
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<< std::endl;
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return Matrix();
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}
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//针对向量行等于列
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if (direction == Column && aMatrix.getDimSize(0)==1){
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direction = All;
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}
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if (aIncludeNan){
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switch (direction)
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{
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case All:
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{
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Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
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double * ret = malloc(64);
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ret[0] = srcV.array().mean();
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return Matrix::New(ret,1);
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}
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case Row:
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{
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Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(0));
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Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
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retV = srcM.rowwise().mean();
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return Matrix::New(ret,aMatrix.getDimSize(0),1);
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}
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case Column:
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default:
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{
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Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
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double * ret = malloc(aMatrix.getDimSize(1));
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Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(1));
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retV = srcM.colwise().mean();
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return Matrix::New(ret,1,aMatrix.getDimSize(1));
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}
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}
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}
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else{
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switch (direction)
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{
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case All:
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{
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Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
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double * retVd = malloc(aMatrix.getDataSize());
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Eigen::Map<Eigen::VectorXd> retV(retVd,aMatrix.getDataSize());
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Eigen::VectorXd ones = Eigen::VectorXd(aMatrix.getDataSize());
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ones.setConstant(1.0);
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retV = srcV.array().isNaN().select(0.0,ones);
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int count = retV.sum();
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if (count == 0){
|
|
free(retVd);
|
|
double *ret = malloc(1);
|
|
ret[0]=0;
|
|
return Matrix::New(ret,1);
|
|
}
|
|
else {
|
|
double *ret = malloc(1);
|
|
retV = srcV.array().isNaN().select(0.0,srcV);
|
|
ret[0] = retV.sum() / count;
|
|
free(retVd);
|
|
return Matrix::New(ret, 1);
|
|
}
|
|
}
|
|
case Row:
|
|
{
|
|
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
|
|
double * retMd = malloc(aMatrix.getDataSize());
|
|
Eigen::Map<Eigen::MatrixXd> retM(retMd,aMatrix.getDimSize(0),aMatrix.getDimSize(1));
|
|
Eigen::MatrixXd zeros = Eigen::MatrixXd(aMatrix.getDimSize(0), aMatrix.getDimSize(1));
|
|
zeros.setConstant(0.0);
|
|
retM = srcM.array().isNaN().select(zeros,1.0);
|
|
Eigen::VectorXd countM = retM.rowwise().sum();
|
|
countM = (countM.array()==0.0).select(1.0,countM);
|
|
retM = srcM.array().isNaN().select(0.0,srcM);
|
|
double * ret = malloc(aMatrix.getDimSize(0));
|
|
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
|
|
retV = retM.rowwise().sum();
|
|
retV =retV.array()/countM.array();
|
|
free(retMd);
|
|
return Matrix::New(ret,aMatrix.getDimSize(0),1);
|
|
}
|
|
case Column:
|
|
default:
|
|
{
|
|
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
|
|
double * retMd = malloc(aMatrix.getDataSize());
|
|
Eigen::Map<Eigen::MatrixXd> retM(retMd,aMatrix.getDimSize(0),aMatrix.getDimSize(1));
|
|
Eigen::MatrixXd zeros = Eigen::MatrixXd(aMatrix.getDimSize(0), aMatrix.getDimSize(1));
|
|
zeros.setConstant(0.0);
|
|
retM = srcM.array().isNaN().select(zeros,1.0);
|
|
Eigen::VectorXd countM = retM.colwise().sum();
|
|
countM = (countM.array()==0).select(1.0,countM);
|
|
retM = srcM.array().isNaN().select(0.0,srcM);
|
|
double * ret = malloc(aMatrix.getDimSize(1));
|
|
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(1));
|
|
retV = retM.colwise().sum();
|
|
retV = retV.array()/countM.array();
|
|
free(retMd);
|
|
return Matrix::New(ret,1,aMatrix.getDimSize(1));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
Matrix Aurora::sort(const Matrix &aMatrix) {
|
|
if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
|
|
std::cerr
|
|
<< (aMatrix.getDimSize(2) > 1 ? "sort() not support 3D data!" : "sort() not support complex value type!")
|
|
<< std::endl;
|
|
return Matrix();
|
|
}
|
|
return sort(std::forward<Matrix &&>(aMatrix.deepCopy()));
|
|
}
|
|
|
|
Matrix Aurora::sort(Matrix &&aMatrix) {
|
|
if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
|
|
std::cerr
|
|
<< (aMatrix.getDimSize(2) > 1 ? "sort() not support 3D data!" : "sort() not support complex value type!")
|
|
<< std::endl;
|
|
return Matrix();
|
|
}
|
|
//针对向量行等于列
|
|
if (aMatrix.getDimSize(0)==1){
|
|
return sortrows(aMatrix);
|
|
}
|
|
if (aMatrix.getDimSize(0)>=100000){
|
|
#pragma omp parallel for
|
|
for (int i = 0; i < aMatrix.getDimSize(1); ++i) {
|
|
Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData()+i*aMatrix.getDimSize(0),aMatrix.getDimSize(0));
|
|
std::sort(srcV.array().begin(),srcV.array().end());
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (int i = 0; i < aMatrix.getDimSize(1); ++i) {
|
|
Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData()+i*aMatrix.getDimSize(0),aMatrix.getDimSize(0));
|
|
std::sort(srcV.array().begin(),srcV.array().end());
|
|
}
|
|
}
|
|
|
|
return aMatrix;
|
|
}
|
|
|
|
Matrix Aurora::sortrows(const Matrix &aMatrix) {
|
|
if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
|
|
std::cerr
|
|
<< (aMatrix.getDimSize(2) > 1 ? "sortrows() not support 3D data!" : "sortrows() not support complex value type!")
|
|
<< std::endl;
|
|
return Matrix();
|
|
}
|
|
return sortrows(std::forward<Matrix &&>(aMatrix.deepCopy()));
|
|
}
|
|
|
|
Matrix Aurora::sortrows(Matrix &&aMatrix) {
|
|
if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
|
|
std::cerr
|
|
<< (aMatrix.getDimSize(2) > 1 ? "sortrows() not support 3D data!" : "sortrows() not support complex value type!")
|
|
<< std::endl;
|
|
return Matrix();
|
|
}
|
|
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
|
|
if (aMatrix.getDimSize(1)>=100000){
|
|
#pragma omp parallel for
|
|
for (int i = 0; i < aMatrix.getDimSize(0); ++i) {
|
|
std::sort(srcM.row(i).array().begin(),srcM.row(i).array().end());
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (int i = 0; i < aMatrix.getDimSize(0); ++i) {
|
|
std::sort(srcM.row(i).array().begin(),srcM.row(i).array().end());
|
|
}
|
|
}
|
|
|
|
return aMatrix;
|
|
}
|
|
|
|
Matrix Aurora::median(const Matrix &aMatrix) {
|
|
if (aMatrix.getDimSize(2) > 1 || aMatrix.isComplex()) {
|
|
std::cerr
|
|
<< (aMatrix.getDimSize(2) > 1 ? "median() not support 3D data!"
|
|
: "median() not support complex value type!")
|
|
<< std::endl;
|
|
return Matrix();
|
|
}
|
|
bool horVector = aMatrix.getDimSize(0)==1;
|
|
Matrix sorted = horVector?sortrows(aMatrix):sort(aMatrix);
|
|
int rows = horVector?sorted.getDimSize(1):sorted.getDimSize(0);
|
|
int cols = horVector?sorted.getDimSize(0):sorted.getDimSize(1);
|
|
Eigen::Map<Eigen::MatrixXd> srcM(sorted.getData(),rows,cols);
|
|
bool flag = rows % 2 == 1;
|
|
double* ret = malloc(cols);
|
|
Eigen::Map<Eigen::VectorXd> retV(ret,cols);
|
|
if (flag) {
|
|
retV = srcM.row(rows/2);
|
|
return Matrix::New(ret,1,cols);
|
|
} else {
|
|
retV = (srcM.row(rows/2-1).array()+srcM.row(rows/2).array())/2;
|
|
return Matrix::New(ret,1,cols);
|
|
}
|
|
}
|
|
|
|
Matrix Aurora::fft(const Matrix &aMatrix) {
|
|
double *output = nullptr;
|
|
mkl_free_buffers();
|
|
output = malloc(aMatrix.getDataSize(), true);
|
|
if (!aMatrix.isComplex()) {
|
|
cblas_dcopy(aMatrix.getDataSize(), aMatrix.getData(), 1, output, 2);
|
|
double zero = 0.0;
|
|
cblas_dcopy(aMatrix.getDataSize(), &zero, 0, output+1, 2);
|
|
} else {
|
|
cblas_zcopy(aMatrix.getDataSize(), aMatrix.getData(), 1, output, 1);
|
|
}
|
|
|
|
DFTI_DESCRIPTOR_HANDLE my_desc_handle = NULL;
|
|
MKL_LONG status;
|
|
|
|
//创建 Descriptor, 精度 double , 输入类型实数, 维度1
|
|
status = DftiCreateDescriptor(&my_desc_handle, DFTI_DOUBLE, DFTI_COMPLEX, 1, aMatrix.getDimSize(0));
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//通过 setValue 配置Descriptor
|
|
//使用单独的输出数据缓存
|
|
status = DftiSetValue(my_desc_handle, DFTI_PLACEMENT, DFTI_INPLACE);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//每个傅里叶变换的输入距离
|
|
status = DftiSetValue(my_desc_handle,DFTI_INPUT_DISTANCE,aMatrix.getDimSize(0));
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//每个傅里叶变换的输出距离
|
|
status = DftiSetValue(my_desc_handle,DFTI_OUTPUT_DISTANCE,aMatrix.getDimSize(0));
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//傅里叶变换的数量
|
|
status = DftiSetValue(my_desc_handle,DFTI_NUMBER_OF_TRANSFORMS,aMatrix.getDimSize(1));
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//提交 修改配置后的Descriptor(实际上会进行FFT的计算初始化)
|
|
status = DftiCommitDescriptor(my_desc_handle);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//执行计算
|
|
status = DftiComputeForward(my_desc_handle, output, output);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//释放资源
|
|
status = DftiFreeDescriptor(&my_desc_handle);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
mkl_free_buffers();
|
|
return Matrix::New(output, aMatrix.getDimSize(0), aMatrix.getDimSize(1), aMatrix.getDimSize(2), Complex);
|
|
error:
|
|
std::cerr<<"FFT fail, error message:"<<DftiErrorMessage(status)<<std::endl;
|
|
return Matrix();
|
|
}
|
|
|
|
Matrix Aurora::ifft(const Matrix &aMatrix) {
|
|
if (!aMatrix.isComplex()){
|
|
std::cerr<<"ifft input must be complex value"<<std::endl;
|
|
return Matrix();
|
|
}
|
|
DFTI_DESCRIPTOR_HANDLE my_desc_handle = NULL;
|
|
mkl_free_buffers();
|
|
auto output = malloc(aMatrix.getDataSize(),true);
|
|
MKL_LONG status;
|
|
|
|
//创建 Descriptor, 精度 double , 输入类型实数, 维度1
|
|
int size = aMatrix.getDimSize(0);
|
|
status = DftiCreateDescriptor(&my_desc_handle, DFTI_DOUBLE, DFTI_COMPLEX, 1, size);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
//通过 setValue 配置Descriptor
|
|
//使用单独的输出数据缓存
|
|
status = DftiSetValue(my_desc_handle, DFTI_PLACEMENT, DFTI_NOT_INPLACE);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//设置DFTI_BACKWARD_SCALE ！！！很关键，不然值不对
|
|
status = DftiSetValue(my_desc_handle, DFTI_BACKWARD_SCALE, 1.0f / size);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
status = DftiSetValue(my_desc_handle,DFTI_INPUT_DISTANCE,size);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//每个傅里叶变换的输出距离
|
|
status = DftiSetValue(my_desc_handle,DFTI_OUTPUT_DISTANCE,size);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//傅里叶变换的数量
|
|
status = DftiSetValue(my_desc_handle,DFTI_NUMBER_OF_TRANSFORMS,aMatrix.getDimSize(1));
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
status = DftiCommitDescriptor(my_desc_handle);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//提交 修改配置后的Descriptor(实际上会进行FFT的计算初始化)
|
|
status = DftiCommitDescriptor(my_desc_handle);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
|
|
//执行计算
|
|
status = DftiComputeBackward(my_desc_handle, aMatrix.getData(), output);
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
//释放资源
|
|
status = DftiFreeDescriptor(&my_desc_handle);
|
|
mkl_free_buffers();
|
|
if (status != DFTI_NO_ERROR) goto error;
|
|
{
|
|
return Matrix::New(output, aMatrix.getDimSize(0), aMatrix.getDimSize(1), aMatrix.getDimSize(2), Complex);
|
|
}
|
|
error:
|
|
std::cerr<<"FFT fail, error message:"<<DftiErrorMessage(status)<<std::endl;
|
|
return Matrix();
|
|
}
|
|
|
|
Matrix Aurora::ifft_symmetric(const Matrix &aMatrix,long length)
|
|
{
|
|
if(!aMatrix.isVector()){
|
|
std::cerr<<"ifft_symmetric only support vector!"<<std::endl;
|
|
return Matrix();
|
|
}
|
|
int matrixLength = aMatrix.getDataSize();
|
|
int resultHalfLength = (int)std::ceil(((double)length*0.5));
|
|
int copyLength = resultHalfLength<matrixLength?resultHalfLength:matrixLength;
|
|
double* calcData = malloc(length,true);
|
|
double zero = 0.0;
|
|
//所有数据统一置0
|
|
cblas_dcopy(length*2,&zero,0,calcData,1);
|
|
//copy前半段数据
|
|
cblas_zcopy(copyLength,aMatrix.getData(),1,calcData,1);
|
|
//copy后半段数据,跳过index 0的值,并设置虚部共轭
|
|
vdAddI(copyLength-1,&zero,0,(aMatrix.getData()+2),2,(calcData+(length-1)*2),-2);
|
|
vdSubI(copyLength-1,&zero,0,(aMatrix.getData()+2+1),2,(calcData+(length-1)*2+1),-2);
|
|
|
|
return real(ifft(Matrix::New(calcData,length,1,1,Complex)));
|
|
}
|
|
|
|
Matrix Aurora::hilbert(const Matrix &aMatrix) {
|
|
auto x = fft(aMatrix);
|
|
auto h = malloc(aMatrix.getDimSize(0));
|
|
auto two = 2.0;
|
|
auto zero = 0.0;
|
|
cblas_dcopy(aMatrix.getDimSize(0), &zero, 0, h, 1);
|
|
cblas_dcopy(aMatrix.getDimSize(0) / 2, &two, 0, h, 1);
|
|
h[aMatrix.getDimSize(0) / 2] = ((aMatrix.getDimSize(0) << 31) >> 31) ? 2.0 : 1.0;
|
|
h[0] = 1.0;
|
|
for (int i = 0; i < aMatrix.getDimSize(1); ++i) {
|
|
auto p = (double *)(x.getData() + aMatrix.getDimSize(0)* i*2);
|
|
vdMulI(aMatrix.getDimSize(0), p, 2, h, 1, p, 2);
|
|
vdMulI(aMatrix.getDimSize(0), p + 1, 2, h, 1, p + 1, 2);
|
|
}
|
|
auto result = ifft( x);
|
|
free(h);
|
|
return result;
|
|
}
|
|
|
|
Matrix Aurora::prod(const Matrix &aMatrix) {
|
|
if (aMatrix.getDimSize(2) > 1 ) {
|
|
std::cerr<< "prod() not support 3D data!"<< std::endl;
|
|
return Matrix();
|
|
}
|
|
int row = aMatrix.getDimSize(0)==1?aMatrix.getDimSize(1):aMatrix.getDimSize(0);
|
|
int col = aMatrix.getDimSize(0)==1?1:aMatrix.getDimSize(1);
|
|
if (aMatrix.isComplex()){
|
|
Eigen::Map<Eigen::MatrixXcd> srcM((std::complex<double>*)aMatrix.getData(),row,col);
|
|
auto ret = malloc(col,true);
|
|
Eigen::Map<Eigen::VectorXcd> retV((std::complex<double>*)ret,col);
|
|
retV = srcM.colwise().prod();
|
|
return Matrix::New(ret,1,col,1,Complex);
|
|
}
|
|
else{
|
|
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),row,col);
|
|
auto ret = malloc(col);
|
|
Eigen::Map<Eigen::VectorXd> retV(ret,col);
|
|
retV = srcM.colwise().prod();
|
|
return Matrix::New(ret,1,col);
|
|
}
|
|
}
|
|
|
|
Matrix Aurora::dot(const Matrix &aMatrix,const Matrix& aOther,FunctionDirection direction ) {
|
|
if ( direction == All){
|
|
std::cerr<< "dot() not support 3D data!"<< std::endl;
|
|
return Matrix();
|
|
}
|
|
if (!aMatrix.compareShape(aOther)){
|
|
std::cerr<< "dot() matrix must be same shape!"<< std::endl;
|
|
return Matrix();
|
|
}
|
|
//针对向量行等于列
|
|
if (direction == Column && aMatrix.getDimSize(0)==1){
|
|
direction = Row;
|
|
}
|
|
if (aMatrix.isComplex()){
|
|
return sum(conj(aMatrix)*aOther,direction);
|
|
}
|
|
else{
|
|
if (direction == Column)
|
|
{
|
|
auto ret = malloc(aMatrix.getDimSize(1));
|
|
for (int i = 0; i < aMatrix.getDimSize(1); ++i) {
|
|
ret[i]=cblas_ddot(aMatrix.getDimSize(0),aMatrix.getData()+i*aMatrix.getDimSize(0),1,
|
|
aOther.getData()+i*aMatrix.getDimSize(0),1);
|
|
}
|
|
return Matrix::New(ret,1,aMatrix.getDimSize(1),1);
|
|
}
|
|
else{
|
|
auto ret = malloc(aMatrix.getDimSize(0));
|
|
for (int i = 0; i < aMatrix.getDimSize(0); ++i) {
|
|
ret[i] = cblas_ddot(aMatrix.getDimSize(1),aMatrix.getData()+i,aMatrix.getDimSize(0),
|
|
aOther.getData()+i,aMatrix.getDimSize(0));
|
|
}
|
|
return Matrix::New(ret,aMatrix.getDimSize(1),1,1);
|
|
}
|
|
}
|
|
}
|
|
|