diff --git a/src/TNL/Matrices/Dense.hpp b/src/TNL/Matrices/Dense.hpp
index 5504f6408ff02465f48673966a77d7b9427f6d48..21ae1bce6991a067653f63b66c76183632dc5fe0 100644
--- a/src/TNL/Matrices/Dense.hpp
+++ b/src/TNL/Matrices/Dense.hpp
@@ -990,6 +990,7 @@ operator=( const Dense< Real_, Device_, Index_, RowMajorOrder_, RealAllocator_ >
}
}
}
+ this->view = this->getView();
return *this;
}
diff --git a/src/TNL/Matrices/SparseMatrix.hpp b/src/TNL/Matrices/SparseMatrix.hpp
index c94506084f18d8d8d0bbe58ddc3c3029877de055..14495ad3d5fca5c7018bd67d352ad02f36104bd8 100644
--- a/src/TNL/Matrices/SparseMatrix.hpp
+++ b/src/TNL/Matrices/SparseMatrix.hpp
@@ -542,6 +542,7 @@ operator=( const SparseMatrix& matrix )
Matrix< Real, Device, Index >::operator=( matrix );
this->columnIndexes = matrix.columnIndexes;
this->segments = matrix.segments;
+ this->view = this->getView();
return *this;
}
@@ -581,7 +582,7 @@ operator=( const Dense< Real_, Device_, Index_, RowMajorOrder, RealAllocator_ >&
if( std::is_same< DeviceType, RHSDeviceType >::value )
{
const auto segments_view = this->segments.getView();
- auto f = [=] __cuda_callable__ ( RHSIndexType rowIdx, RHSIndexType columnIdx, RHSIndexType globalIndex, const RHSRealType& value, bool& compute ) mutable {
+ auto f = [=] __cuda_callable__ ( RHSIndexType rowIdx, RHSIndexType localIdx, RHSIndexType columnIdx, const RHSRealType& value, bool& compute ) mutable {
if( value != 0.0 )
{
IndexType thisGlobalIdx = segments_view.getGlobalIndex( rowIdx, rowLocalIndexes_view[ rowIdx ]++ );
@@ -650,6 +651,7 @@ operator=( const Dense< Real_, Device_, Index_, RowMajorOrder, RealAllocator_ >&
}
//std::cerr << "This matrix = " << std::endl << *this << std::endl;
}
+ this->view = this->getView();
return *this;
}
@@ -684,7 +686,7 @@ operator=( const RHSMatrix& matrix )
auto values_view = this->values.getView();
columns_view = paddingIndex;
- if( std::is_same< DeviceType, RHSDeviceType >::value )
+ /*if( std::is_same< DeviceType, RHSDeviceType >::value )
{
const auto segments_view = this->segments.getView();
auto f = [=] __cuda_callable__ ( RHSIndexType rowIdx, RHSIndexType localIdx, RHSIndexType columnIndex, const RHSRealType& value, bool& compute ) mutable {
@@ -697,7 +699,7 @@ operator=( const RHSMatrix& matrix )
};
matrix.forAllRows( f );
}
- else
+ else*/
{
const IndexType maxRowLength = max( rowLengths );
const IndexType bufferRowsCount( 128 );
@@ -747,11 +749,11 @@ operator=( const RHSMatrix& matrix )
value = thisValuesBuffer_view[ bufferIdx ];
}
};
- //this->forRows( baseRow, lastRow, f2 );
+ this->forRows( baseRow, lastRow, f2 );
baseRow += bufferRowsCount;
}
- //std::cerr << "This matrix = " << std::endl << *this << std::endl;
}
+ this->view = this->getView();
return *this;
}
diff --git a/src/TNL/Matrices/SparseMatrixView.hpp b/src/TNL/Matrices/SparseMatrixView.hpp
index 055a1d60e61a4696ba16d642d37725f1157479c3..965a51b8b6811ede34c7e957bf3299e12a1104b7 100644
--- a/src/TNL/Matrices/SparseMatrixView.hpp
+++ b/src/TNL/Matrices/SparseMatrixView.hpp
@@ -415,7 +415,7 @@ forRows( IndexType first, IndexType last, Function& function ) const
const auto values_view = this->values.getConstView();
const IndexType paddingIndex_ = this->getPaddingIndex();
auto f = [=] __cuda_callable__ ( IndexType rowIdx, IndexType localIdx, IndexType globalIdx, bool& compute ) mutable -> bool {
- function( rowIdx, localIdx, columns_view[ globalIdx ], globalIdx, compute );
+ function( rowIdx, localIdx, columns_view[ globalIdx ], values_view[ globalIdx ], compute );
return true;
};
this->segments.forSegments( first, last, f );
@@ -435,7 +435,7 @@ forRows( IndexType first, IndexType last, Function& function )
auto values_view = this->values.getView();
const IndexType paddingIndex_ = this->getPaddingIndex();
auto f = [=] __cuda_callable__ ( IndexType rowIdx, IndexType localIdx, IndexType globalIdx, bool& compute ) mutable {
- function( rowIdx, localIdx, columns_view[ globalIdx ], globalIdx, compute );
+ function( rowIdx, localIdx, columns_view[ globalIdx ], values_view[ globalIdx ], compute );
};
this->segments.forSegments( first, last, f );
}
diff --git a/src/UnitTests/Matrices/SparseMatrixCopyTest.h b/src/UnitTests/Matrices/SparseMatrixCopyTest.h
index 8677443b296dc8933ed886834208c315ec8736ea..e9898bb393c21baf0a04c0f7e8462ac613ef81d6 100644
--- a/src/UnitTests/Matrices/SparseMatrixCopyTest.h
+++ b/src/UnitTests/Matrices/SparseMatrixCopyTest.h
@@ -40,7 +40,7 @@ using SE_host = TNL::Matrices::SparseMatrix< int, TNL::Devices::Host, int, TNL:
using SE_cuda = TNL::Matrices::SparseMatrix< int, TNL::Devices::Cuda, int, TNL::Matrices::GeneralMatrix, SlicedEllpackSegments >;
-#ifdef HAVE_GTEST
+#ifdef HAVE_GTEST
#include <gtest/gtest.h>
/*
@@ -99,7 +99,7 @@ void setupUnevenRowSizeMatrix( Matrix& m )
m.setElement( 7, 0, value++ ); // 7th row
- for( int i = 0; i < cols - 1; i++ ) // 8th row
+ for( int i = 0; i < cols - 1; i++ ) // 8th row
m.setElement( 8, i, value++ );
m.setElement( 9, 5, value++ ); // 9th row
@@ -159,21 +159,21 @@ void checkUnevenRowSizeMatrix( Matrix& m )
EXPECT_EQ( m.getElement( 6, 3 ), 0 );
EXPECT_EQ( m.getElement( 6, 4 ), 0 );
EXPECT_EQ( m.getElement( 6, 5 ), 0 );
-
+
EXPECT_EQ( m.getElement( 7, 0 ), 22 );
EXPECT_EQ( m.getElement( 7, 1 ), 0 );
EXPECT_EQ( m.getElement( 7, 2 ), 0 );
EXPECT_EQ( m.getElement( 7, 3 ), 0 );
EXPECT_EQ( m.getElement( 7, 4 ), 0 );
EXPECT_EQ( m.getElement( 7, 5 ), 0 );
-
+
EXPECT_EQ( m.getElement( 8, 0 ), 23 );
EXPECT_EQ( m.getElement( 8, 1 ), 24 );
EXPECT_EQ( m.getElement( 8, 2 ), 25 );
EXPECT_EQ( m.getElement( 8, 3 ), 26 );
EXPECT_EQ( m.getElement( 8, 4 ), 27 );
EXPECT_EQ( m.getElement( 8, 5 ), 0 );
-
+
EXPECT_EQ( m.getElement( 9, 0 ), 0 );
EXPECT_EQ( m.getElement( 9, 1 ), 0 );
EXPECT_EQ( m.getElement( 9, 2 ), 0 );
@@ -206,7 +206,7 @@ void setupAntiTriDiagMatrix( Matrix& m )
rowLengths.setElement( 0, 4);
rowLengths.setElement( 1, 4 );
m.setCompressedRowLengths( rowLengths );
-
+
int value = 1;
for( int i = 0; i < rows; i++ )
for( int j = cols - 1; j > 2; j-- )
@@ -397,39 +397,37 @@ void testCopyAssignment()
template< typename Matrix1, typename Matrix2 >
void testConversion()
{
-
{
SCOPED_TRACE("Tri Diagonal Matrix");
-
+
Matrix1 triDiag1;
setupTriDiagMatrix( triDiag1 );
checkTriDiagMatrix( triDiag1 );
-
+
Matrix2 triDiag2;
- //TNL::Matrices::copySparseMatrix( triDiag2, triDiag1 );
triDiag2 = triDiag1;
checkTriDiagMatrix( triDiag2 );
}
-
+
{
SCOPED_TRACE("Anti Tri Diagonal Matrix");
-
+
Matrix1 antiTriDiag1;
setupAntiTriDiagMatrix( antiTriDiag1 );
checkAntiTriDiagMatrix( antiTriDiag1 );
-
+
Matrix2 antiTriDiag2;
//TNL::Matrices::copySparseMatrix( antiTriDiag2, antiTriDiag1 );
antiTriDiag2 = antiTriDiag1;
checkAntiTriDiagMatrix( antiTriDiag2 );
}
-
+
{
SCOPED_TRACE("Uneven Row Size Matrix");
Matrix1 unevenRowSize1;
setupUnevenRowSizeMatrix( unevenRowSize1 );
checkUnevenRowSizeMatrix( unevenRowSize1 );
-
+
Matrix2 unevenRowSize2;
//TNL::Matrices::copySparseMatrix( unevenRowSize2, unevenRowSize1 );
unevenRowSize2 = unevenRowSize1;