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Add mean and mean's unit test.

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This commit is contained in:
Krad
2023-04-24 15:24:23 +08:00
parent 2855d5c9a0
commit a895785319
3 changed files with 219 additions and 18 deletions
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132
src/Function2D.cpp
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@@ -156,6 +156,7 @@ Matrix Aurora::min(const Matrix &aMatrix, FunctionDirection direction) {
return Matrix::New(ret,aMatrix.getDimSize(0),1);
}
case Column:
default:
{
Eigen::Map<Eigen::MatrixXd> srcMatrix(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
double * ret = malloc(aMatrix.getDimSize(0));
@@ -266,28 +267,139 @@ Matrix Aurora::sum(const Matrix &aMatrix, FunctionDirection direction) {
{
case All:
{
Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
double * ret = malloc(1);
ret[0] = cblas_dasum(aMatrix.getDataSize(),aMatrix.getData(),1);
ret[0] = srcV.array().sum();
return Matrix::New(ret,1);
}
case Row:
{
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
double * ret = malloc(aMatrix.getDimSize(0));
for (int i = 0; i < aMatrix.getDimSize(0); ++i) {
ret[i] = cblas_dasum(aMatrix.getDimSize(1), aMatrix.getData() + i,
aMatrix.getDimSize(0));
}
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
retV = srcM.rowwise().sum();
return Matrix::New(ret,aMatrix.getDimSize(0),1);
}
case Column:
default:
{
double * ret = malloc(aMatrix.getDimSize(0));
for (int i = 0; i < aMatrix.getDimSize(1); ++i) {
ret[i] = cblas_dasum(aMatrix.getDimSize(0), aMatrix.getData()+aMatrix.getDimSize(0)*i,
1);
}
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
double * ret = malloc(aMatrix.getDimSize(1));
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
retV = srcM.colwise().sum();
return Matrix::New(ret,1,aMatrix.getDimSize(1));
}
}
}
void excludeNan(double * aInput, int aInputSize,double* aOutput,int& aOutputSize){
aOutputSize = 0;
for (int i = 0; i < aInputSize; ++i) {
if (std::isnan(aInput[i])) continue;
aOutput[aOutputSize]=aInput[i];
aOutputSize++;
}
}
Matrix Aurora::mean(const Matrix &aMatrix, FunctionDirection direction, bool aIncludeNan) {
if (aMatrix.getDimSize(2)>1 || aMatrix.isComplex()) {
std::cerr
<< (aMatrix.getDimSize(2) > 1 ? "sum() not support 3D data!" : "sum() not support complex value type!")
<< std::endl;
return Matrix();
}
if (aIncludeNan){
switch (direction)
{
case All:
{
Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
double * ret = malloc(1);
ret[0] = srcV.array().mean();
return Matrix::New(ret,1);
}
case Row:
{
Eigen::Map<Eigen::MatrixXd> srcM(aMatrix.getData(),aMatrix.getDimSize(0),aMatrix.getDimSize(1));
double * ret = malloc(aMatrix.getDimSize(0));
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
retV = srcM.rowwise().mean();
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 * ret = malloc(aMatrix.getDimSize(1));
Eigen::Map<Eigen::VectorXd> retV(ret,aMatrix.getDimSize(0));
retV = srcM.colwise().mean();
return Matrix::New(ret,1,aMatrix.getDimSize(1));
}
}
}
else{
switch (direction)
{
case All:
{
Eigen::Map<Eigen::VectorXd> srcV(aMatrix.getData(),aMatrix.getDataSize());
double * retVd = malloc(aMatrix.getDataSize());
Eigen::Map<Eigen::VectorXd> retV(retVd,aMatrix.getDataSize());
Eigen::VectorXd ones = Eigen::VectorXd(aMatrix.getDataSize());
ones.setConstant(1.0);
retV = srcV.array().isNaN().select(0.0,ones);
int count = retV.sum();
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));
}
}
}
}

9
src/Function2D.h
View File

@@ -51,6 +51,15 @@ namespace Aurora {
*/
Matrix sum(const Matrix& aMatrix,FunctionDirection direction = Column);
/**
* 求矩阵平均值，可按行、列、单元, 目前不支持三维，不支持复数
* @param aMatrix 矩阵
* @param direction 方向，Column, Row, All
* @param aIncludeNan 是否包含nan
* @return
*/
Matrix mean(const Matrix& aMatrix,FunctionDirection direction = Column, bool aIncludeNan = true);
};