Pseudo expression providing broadcasting and partial reduction operations.
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:
MatrixXd(n,0).colwise().prod()
)typedef Eigen::Index | Index |
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 |
random_access_iterator_type | const_iterator |
random_access_iterator_type | iterator |
typedef Eigen::Index Eigen::VectorwiseOp< ExpressionType, Direction >::Index |
| inline |
true
. This expression can be assigned to a vector with entries of type bool
.
| inline |
true
. This expression can be assigned to a vector with entries of type bool
.
| inline |
| inline |
const version of begin()
| inline |
| inline |
const version of begin()
| inline |
const version of end()
| inline |
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
| inline |
const version of rbegin()
| inline |
const version of rend()
| inline |
| inline |
const version of end()
| inline |
| inline |
Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the norm of each column:" << endl << m.colwise().norm() << 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 norm of each column: 0.91 1.18 0.771
| inline |
*this
contains NaN.Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the maximum of each column:" << endl << m.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 maximum of each column: 0.68 0.823 0.536
| inline |
| inline |
*this
contains NaN.Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the minimum of each column:" << endl << m.colwise().minCoeff() << 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 minimum of each column: -0.211 -0.605 -0.444
| inline |
Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the norm of each column:" << endl << m.colwise().norm() << 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 norm of each column: 0.91 1.18 0.771
| inline |
Normalize in-place each row or columns of the referenced matrix.
| inline |
| inline |
Returns the expression where each subvector is the product of the vector other by the corresponding subvector of *this
| inline |
Multiples each subvector of *this
by the vector other
| inline |
Returns the expression of the sum of the vector other to each subvector of *this
| inline |
Adds the vector other to each subvector of *this
| inline |
Returns the expression of the difference between each subvector of *this
and the vector other
| inline |
Substracts the vector other to each subvector of *this
| inline |
Returns the expression where each subvector is the quotient of the corresponding subvector of *this
by the vector other
| inline |
Divides each subvector of *this
by the vector other
| inline |
Copies the vector other to each subvector of *this
| inline |
Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the product of each row:" << endl << m.rowwise().prod() << 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 product of each row: -0.134 -0.0933 0.152
| inline |
| inline |
const version of rbegin()
| inline |
*this
reduxed by func The template parameter BinaryOp is the type of the functor of the custom redux operator. Note that func must be an associative operator.
| inline |
| inline |
const version of rend()
const VectorwiseOp< ExpressionType, Direction >::ReplicateReturnType Eigen::VectorwiseOp< ExpressionType, Direction >::replicate | ( | Index | factor | ) | const |
*this
Example:
Vector3i v = Vector3i::Random(); cout << "Here is the vector v:" << endl << v << endl; cout << "v.rowwise().replicate(5) = ..." << endl; cout << v.rowwise().replicate(5) << endl;
Output:
Here is the vector v: 7 -2 6 v.rowwise().replicate(5) = ... 7 7 7 7 7 -2 -2 -2 -2 -2 6 6 6 6 6
| inline |
*this
Example:
MatrixXi m = MatrixXi::Random(2,3); cout << "Here is the matrix m:" << endl << m << endl; cout << "m.colwise().replicate<3>() = ..." << endl; cout << m.colwise().replicate<3>() << endl;
Output:
Here is the matrix m: 7 6 9 -2 6 -6 m.colwise().replicate<3>() = ... 7 6 9 -2 6 -6 7 6 9 -2 6 -6 7 6 9 -2 6 -6
| inline |
| inline |
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
| inline |
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:
m = m.reverse().eval();
| inline |
Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the square norm of each row:" << endl << m.rowwise().squaredNorm() << 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 square norm of each row: 0.928 1.01 0.884
| inline |
| inline |
Example:
Matrix3d m = Matrix3d::Random(); cout << "Here is the matrix m:" << endl << m << endl; cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << 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 row: 0.948 1.15 -0.483
random_access_iterator_type Eigen::VectorwiseOp< ExpressionType, Direction >::const_iterator |
This is the const version of iterator (aka read-only)
random_access_iterator_type Eigen::VectorwiseOp< ExpressionType, Direction >::iterator |
STL-like RandomAccessIterator iterator type over the columns or rows as returned by the begin() and end() methods.
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Licensed under the MPL2 License.
https://eigen.tuxfamily.org/dox/classEigen_1_1VectorwiseOp.html