Add MatDense::rowwise_min(), rowwise_min(int* col_indices), MatDense::min() and a test.

It will be useful for FGFT Jocobi algorithms.

misc/test/CMakeLists.txt

@@ -156,7 +156,7 @@ endif()
 
 
 foreach(TEST_FPP float double)
-	foreach(FILE faust_mult faust_mult_cplx test_Vect_min)
+	foreach(FILE faust_mult faust_mult_cplx test_Vect_min test_MatDense_min)
 		set(TEST_BIN_FILE ${FILE}_${TEST_FPP})
 		set(TEST_FILE_CPP ${TEST_BIN_FILE}.cpp)
 		message(STATUS ${TEST_FILE_CPP})

misc/test/src/C++/unit/test_MatDense_min.cpp.in

+
+#include "faust_MatDense.h"
+
+
+#include <complex>
+#include <random>
+
+using namespace Faust;
+typedef @TEST_FPP@ FPP;
+
+
+int main()
+{
+	std::random_device rd; //random device gives the seed
+	std::mt19937 gen(rd()); //Standard mersenne_twister_engine seeded with rd()
+	std::uniform_real_distribution<> dis(1.0, 200);
+
+	FPP *data = new FPP[9];
+	for(int i=0;i<3;i++) {
+		for(int j=0;j<3;j++)
+			data[3*j+i] = dis(gen);
+	}
+	MatDense<FPP,Cpu> M(data, 3, 3);
+	cout << "Random matrix for the test:" << endl;
+	for(int i=0;i<3;i++) {
+		for(int j=0;j<3;j++)
+			cout << M.getData()[j*3+i] << " ";
+		cout << endl;
+	}
+	cout << "M.min():" << M.min() << endl;
+	int indices[3];
+
+	cout << "M.rowwise_min()" << endl;
+	Faust::Vect<FPP, Cpu> vec = M.rowwise_min();
+	vec.Display();
+
+	cout << "M.rowwise_min(indices)" << endl;
+	Faust::Vect<FPP, Cpu> vec2 = M.rowwise_min(indices);
+	vec2.Display();
+	cout << "indices: " << endl;
+	for(int i=0;i<3;i++)
+		cout << " " << indices[i] << endl;
+
+	return 0;
+}

src/faust_linear_operator/CPU/faust_MatDense.h

@@ -398,6 +398,11 @@ void spgemm(const Faust::MatSparse<FPP,Cpu> & A,const Faust::MatDense<FPP,Cpu> &
 		Faust::MatDense<FPP,Cpu>* get_cols(faust_unsigned_int* col_ids, faust_unsigned_int n) const;
 		Faust::MatDense<FPP,Cpu>* get_rows(faust_unsigned_int start_row_id, faust_unsigned_int num_rows) const;
         Faust::MatDense<FPP,Cpu>* get_rows(faust_unsigned_int* row_ids, faust_unsigned_int n) const;
+
+		FPP min() const {return mat.minCoeff();}
+		Faust::Vect<FPP,Cpu> rowwise_min() const;
+		Faust::Vect<FPP,Cpu> rowwise_min(int* col_indices) const;
+
 		void operator=(MatDense<FPP,Cpu> const& A);
 
         template<typename FPP1>

src/faust_linear_operator/CPU/faust_MatDense.hpp

@@ -167,7 +167,7 @@ void Faust::MatDense<FPP,Cpu>::setEyes()
 {
 	setZeros();
 	FPP* ptr_data = getData();
-	for (int i=0 ; i<min(this->dim1,this->dim2); i++)
+	for (int i=0 ; i<std::min(this->dim1,this->dim2); i++)
 		ptr_data[i*this->dim1+i] = FPP(1.0);
 	if (this->dim1 == this->dim2)
 		isIdentity = true;
@@ -920,6 +920,22 @@ Faust::MatDense<FPP,Cpu>* Faust::MatDense<FPP,Cpu>::get_rows(faust_unsigned_int*
 	return rows;
 }
 
+
+template<typename FPP>
+Faust::Vect<FPP,Cpu> Faust::MatDense<FPP,Cpu>::rowwise_min() const
+{
+	return Faust::Vect<FPP,Cpu>(this->getNbCol(), mat.rowwise().minCoeff().eval().data());
+}
+
+template<typename FPP>
+Faust::Vect<FPP,Cpu> Faust::MatDense<FPP,Cpu>::rowwise_min(int* col_indices) const
+{
+	Faust::Vect<FPP,Cpu> vec(this->getNbRow());
+	for(int i=0;i<this->getNbRow();i++)
+		vec.getData()[i] = mat.row(i).minCoeff(col_indices+i);
+	return vec;
+}
+
 	template<typename FPP>
 Faust::MatDense<FPP, Cpu>* Faust::MatDense<FPP, Cpu>::randMat(faust_unsigned_int num_rows, faust_unsigned_int num_cols)
 {