template<typename ExpressionType, int Direction>
class Eigen::VectorwiseOp< ExpressionType, Direction >
Pseudo expression providing broadcasting and partial reduction operations.
- Template Parameters
-
ExpressionType |
the type of the object on which to do partial reductions |
Direction |
indicates whether to operate on columns (Vertical) or rows (Horizontal) |
This class represents a pseudo expression with broadcasting and partial reduction features. It is the return type of DenseBase::colwise() and DenseBase::rowwise() and most of the time this is the only way it is explicitly used.
To understand the logic of rowwise/colwise expression, let's consider a generic case A.colwise().foo()
where foo
is any method of VectorwiseOp
. This expression is equivalent to applying foo()
to each column of A
and then re-assemble the outputs in a matrix expression:
[A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()]
Example:
Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the sum of each column:" << endl << m.colwise().sum() << endl;
cout << "Here is the maximum absolute value of each column:"
<< endl << m.cwiseAbs().colwise().maxCoeff() << endl;
Output:
Here is the matrix m:
0.68 0.597 -0.33
-0.211 0.823 0.536
0.566 -0.605 -0.444
Here is the sum of each column:
1.04 0.815 -0.238
Here is the maximum absolute value of each column:
0.68 0.823 0.536
The begin() and end() methods are obviously exceptions to the previous rule as they return STL-compatible begin/end iterators to the rows or columns of the nested expression. Typical use cases include for-range-loop and calls to STL algorithms:
Example:
Matrix3i m = Matrix3i::Random();
cout << "Here is the initial matrix m:" << endl << m << endl;
int i = -1;
for(auto c: m.colwise()) {
c *= i;
++i;
}
cout << "Here is the matrix m after the for-range-loop:" << endl << m << endl;
auto cols = m.colwise();
auto it = std::find_if(cols.cbegin(), cols.cend(),
[](Matrix3i::ConstColXpr x) { return x.squaredNorm() == 0; });
cout << "The first empty column is: " << distance(cols.cbegin(),it) << endl;
Output:
Here is the initial matrix m:
7 6 -3
-2 9 6
6 -6 -5
Here is the matrix m after the for-range-loop:
-7 0 -3
2 0 6
-6 0 -5
The first empty column is: 1
For a partial reduction on an empty input, some rules apply. For the sake of clarity, let's consider a vertical reduction:
- If the number of columns is zero, then a 1x0 row-major vector expression is returned.
- Otherwise, if the number of rows is zero, then
- a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
- a row vector of ones is returned for a product reduction (e.g.,
MatrixXd(n,0).colwise().prod()
) - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
- See also
-
DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
|
const AllReturnType |
all () const |
|
const AnyReturnType |
any () const |
|
iterator |
begin () |
|
const_iterator |
begin () const |
|
const BlueNormReturnType |
blueNorm () const |
|
const_iterator |
cbegin () const |
|
const_iterator |
cend () const |
|
const CountReturnType |
count () const |
|
const_reverse_iterator |
crbegin () const |
|
const_reverse_iterator |
crend () const |
|
template<typename OtherDerived > |
const CrossReturnType |
cross (const MatrixBase< OtherDerived > &other) const |
|
iterator |
end () |
|
const_iterator |
end () const |
|
const HNormalizedReturnType |
hnormalized () const |
|
column or row-wise homogeneous normalization More...
|
|
HomogeneousReturnType |
homogeneous () const |
|
const HypotNormReturnType |
hypotNorm () const |
|
template<int p> |
const LpNormReturnType< p >::Type |
lpNorm () const |
|
const MaxCoeffReturnType |
maxCoeff () const |
|
const MeanReturnType |
mean () const |
|
const MinCoeffReturnType |
minCoeff () const |
|
const NormReturnType |
norm () const |
|
void |
normalize () |
|
CwiseBinaryOp< internal::scalar_quotient_op< Scalar >, const ExpressionTypeNestedCleaned, const typename OppositeExtendedType< NormReturnType >::Type > |
normalized () const |
|
template<typename OtherDerived > |
CwiseBinaryOp< internal::scalar_product_op< Scalar >, const ExpressionTypeNestedCleaned, const typename ExtendedType< OtherDerived >::Type > |
operator* (const DenseBase< OtherDerived > &other) const |
|
template<typename OtherDerived > |
ExpressionType & |
operator*= (const DenseBase< OtherDerived > &other) |
|
template<typename OtherDerived > |
CwiseBinaryOp< internal::scalar_sum_op< Scalar, typename OtherDerived::Scalar >, const ExpressionTypeNestedCleaned, const typename ExtendedType< OtherDerived >::Type > |
operator+ (const DenseBase< OtherDerived > &other) const |
|
template<typename OtherDerived > |
ExpressionType & |
operator+= (const DenseBase< OtherDerived > &other) |
|
template<typename OtherDerived > |
CwiseBinaryOp< internal::scalar_difference_op< Scalar, typename OtherDerived::Scalar >, const ExpressionTypeNestedCleaned, const typename ExtendedType< OtherDerived >::Type > |
operator- (const DenseBase< OtherDerived > &other) const |
|
template<typename OtherDerived > |
ExpressionType & |
operator-= (const DenseBase< OtherDerived > &other) |
|
template<typename OtherDerived > |
CwiseBinaryOp< internal::scalar_quotient_op< Scalar >, const ExpressionTypeNestedCleaned, const typename ExtendedType< OtherDerived >::Type > |
operator/ (const DenseBase< OtherDerived > &other) const |
|
template<typename OtherDerived > |
ExpressionType & |
operator/= (const DenseBase< OtherDerived > &other) |
|
template<typename OtherDerived > |
ExpressionType & |
operator= (const DenseBase< OtherDerived > &other) |
|
const ProdReturnType |
prod () const |
|
reverse_iterator |
rbegin () |
|
const_reverse_iterator |
rbegin () const |
|
template<typename BinaryOp > |
const ReduxReturnType< BinaryOp >::Type |
redux (const BinaryOp &func=BinaryOp()) const |
|
reverse_iterator |
rend () |
|
const_reverse_iterator |
rend () const |
|
const ReplicateReturnType |
replicate (Index factor) const |
|
template<int Factor> |
const Replicate< ExpressionType, isVertical *Factor+isHorizontal, isHorizontal *Factor+isVertical > |
replicate (Index factor=Factor) const |
|
ReverseReturnType |
reverse () |
|
const ConstReverseReturnType |
reverse () const |
|
void |
reverseInPlace () |
|
const SquaredNormReturnType |
squaredNorm () const |
|
const StableNormReturnType |
stableNorm () const |
|
const SumReturnType |
sum () const |
|
template<typename ExpressionType , int Direction>
- Returns
- a row (or column) vector expression representing the number of
true
coefficients of each respective column (or row). This expression can be assigned to a vector whose entries have the same type as is used to index entries of the original matrix; for dense matrices, this is std::ptrdiff_t
.
Example:
Matrix3d m = Matrix3d::Random();
cout << "Here is the matrix m:" << endl << m << endl;
Matrix<ptrdiff_t, 3, 1> res = (m.array() >= 0.5).rowwise().count();
cout << "Here is the count of elements larger or equal than 0.5 of each row:" << endl;
cout << res << endl;
Output:
Here is the matrix m:
0.68 0.597 -0.33
-0.211 0.823 0.536
0.566 -0.605 -0.444
Here is the count of elements larger or equal than 0.5 of each row:
2
2
1
- See also
-
DenseBase::count()
template<typename ExpressionType , int Direction>
- Returns
- a matrix expression where each column (or row) are reversed.
Example:
MatrixXi m = MatrixXi::Random(3,4);
cout << "Here is the matrix m:" << endl << m << endl;
cout << "Here is the rowwise reverse of m:" << endl << m.rowwise().reverse() << endl;
cout << "Here is the colwise reverse of m:" << endl << m.colwise().reverse() << endl;
cout << "Here is the coefficient (1,0) in the rowise reverse of m:" << endl
<< m.rowwise().reverse()(1,0) << endl;
cout << "Let us overwrite this coefficient with the value 4." << endl;
//m.colwise().reverse()(1,0) = 4;
cout << "Now the matrix m is:" << endl << m << endl;
Output:
Here is the matrix m:
7 6 -3 1
-2 9 6 0
6 -6 -5 3
Here is the rowwise reverse of m:
1 -3 6 7
0 6 9 -2
3 -5 -6 6
Here is the colwise reverse of m:
6 -6 -5 3
-2 9 6 0
7 6 -3 1
Here is the coefficient (1,0) in the rowise reverse of m:
0
Let us overwrite this coefficient with the value 4.
Now the matrix m is:
7 6 -3 1
-2 9 6 0
6 -6 -5 3
- See also
-
DenseBase::reverse()
template<typename ExpressionType , int Direction>
This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of *this
.
In most cases it is probably better to simply use the reversed expression of a matrix. However, when reversing the matrix data itself is really needed, then this "in-place" version is probably the right choice because it provides the following additional benefits:
- less error prone: doing the same operation with .reverse() requires special care:
m = m.reverse().eval();
- this API enables reverse operations without the need for a temporary
- See also
-
DenseBase::reverseInPlace(), reverse()