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* update redux section
* fix output precision to 3 for the snippets
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@ -3,6 +3,8 @@ namespace Eigen {
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/** \page TutorialCore Tutorial 1/4 - Core features
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\ingroup Tutorial
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<p style="font-size:2em"> WARNING this page is deprecated, and will be removed soon, don't look at it :) </p>
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<div class="eimainmenu">\ref index "Overview"
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| \b Core \b features
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| \ref TutorialGeometry "Geometry"
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@ -143,53 +143,28 @@ When using complex numbers, Eigen's dot product is conjugate-linear in the first
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second variable.
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\section TutorialArithmeticRedux Basic arithmetic reduction operations
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Eigen also provides some reduction operations to obtain values such as the sum or the maximum
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or minimum of all the coefficients in a given matrix or vector.
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Eigen also provides some reduction operations to reduce a given matrix or vector to a single value such as the sum (<tt>a.sum()</tt>), product (<tt>a.sum()</tt>), or the maximum (<tt>a.maxCoeff()</tt>) and minimum (<tt>a.minCoeff()</tt>) of all its coefficients.
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TODO: convert this from table format to tutorial/examples format.
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<table class="tutorial_code"><tr><td>
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Example: \include tut_arithmetic_redux_basic.cpp
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</td>
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<td>
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Output: \include tut_arithmetic_redux_basic.out
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</td></tr></table>
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<table class="tutorial_code" align="center">
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<tr><td align="center">\b Reduction \b operation</td><td align="center">\b Usage \b example</td></tr>
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<tr><td>
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Sum of all the coefficients in a matrix</td><td>\code
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MatrixXf m;
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float totalSum = m.sum();\endcode</td></tr>
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<tr><td>
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Maximum coefficient in a matrix</td><td>\code
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MatrixXf m;
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int row, col;
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The \em trace of a matrix, as returned by the function \c trace(), is the sum of the diagonal coefficients and can also be computed as efficiently using <tt>a.diagonal().sum()</tt>, as we see later on.
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// minimum value will be stored in minValue
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// and the row and column where it was found in row and col,
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// (these two parameters are optional)
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float minValue = m.minCoeff(&row,&col);\endcode</td></tr>
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<tr><td>
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Maximum coefficient in a matrix</td><td>\code
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MatrixXf m;
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int row, col;
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There also exist variants of the \c minCoeff and \c maxCoeff functions returning the coordinates of the respective coefficient via the arguments:
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// maximum value will be stored in maxValue
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// and the row and column where it was found in row and col,
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// (these two parameters are optional)
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float maxValue = m.maxCoeff(&row,&col);\endcode</td></tr>
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<tr><td>
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Product between all coefficients in a matrix</td><td>\code
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MatrixXf m;
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<table class="tutorial_code"><tr><td>
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Example: \include tut_arithmetic_redux_minmax.cpp
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</td>
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<td>
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Output: \include tut_arithmetic_redux_minmax.out
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</td></tr></table>
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float product = m.prod();\endcode</td></tr>
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<tr><td>
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Mean of coefficients in a matrix</td><td>\code
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MatrixXf m;
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float mean = m.mean();\endcode</td></tr>
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<tr><td>
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Matrix's trace</td><td>\code
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MatrixXf m;
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float trace = m.trace();\endcode</td></tr>
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</table>
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\subsection TutorialArithmeticValidity Validity of operations
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\section TutorialArithmeticValidity Validity of operations
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Eigen checks the validity of the operations that you perform. When possible,
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it checks them at compile-time, producing compilation errors. These error messages can be long and ugly,
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but Eigen writes the important message in UPPERCASE_LETTERS_SO_IT_STANDS_OUT. For example:
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@ -35,6 +35,7 @@ namespace Eigen {
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<a href="#" class="top">top</a>
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\section QuickRef_Types Array, matrix and vector types
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\b Recall: Eigen provides two kinds of dense objects: mathematical matrices and vectors which are both represented by the template class Matrix, and general 1D and 2D arrays represented by the template class Array:
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\code
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typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, Options> MyMatrixType;
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@ -87,7 +88,6 @@ In the rest of this document we will use the following symbols to emphasize the
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\li <a name="matrixonly"><a/>\matrixworld linear algebra matrix and vector only
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\li <a name="arrayonly"><a/>\arrayworld array objects only
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\subsection QuickRef_Basics Basic matrix manipulation
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<table class="tutorial_code">
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16
doc/examples/tut_arithmetic_redux_basic.cpp
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16
doc/examples/tut_arithmetic_redux_basic.cpp
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@ -0,0 +1,16 @@
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#include <iostream>
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#include <Eigen/Dense>
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using namespace std;
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int main()
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{
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Eigen::Matrix2d mat;
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mat << 1, 2,
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3, 4;
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cout << "Here is mat.sum(): " << mat.sum() << endl;
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cout << "Here is mat.prod(): " << mat.prod() << endl;
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cout << "Here is mat.mean(): " << mat.mean() << endl;
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cout << "Here is mat.minCoeff(): " << mat.minCoeff() << endl;
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cout << "Here is mat.maxCoeff(): " << mat.maxCoeff() << endl;
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cout << "Here is mat.trace(): " << mat.trace() << endl;
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}
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@ -11,6 +11,7 @@ using namespace std;
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int main(int, char**)
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{
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cout.precision(3);
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${snippet_source_code}
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return 0;
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}
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10
doc/snippets/tut_arithmetic_redux_minmax.cpp
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10
doc/snippets/tut_arithmetic_redux_minmax.cpp
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@ -0,0 +1,10 @@
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Matrix3f m = Matrix3f::Random();
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std::ptrdiff_t i, j;
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float minOfM = m.minCoeff(&i,&j);
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cout << "Here is the matrix m:\n" << m << endl;
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cout << "Its minimum coefficient (" << minOfM << ") is at position (" << i << "," << j << ")\n\n";
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RowVector4i v = RowVector4i::Random();
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int maxOfV = v.maxCoeff(&i);
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cout << "Here is the vector v: " << v << endl;
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cout << "Its maximum coefficient (" << maxOfV << ") is at position " << i << endl;
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@ -1,4 +1,5 @@
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Matrix2i a; a << 1, 2, 3, 4;
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cout << "Here is the matrix a:\n" << a << endl;
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a = a.transpose(); // fails
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cout << "and the aliasing effect:\n" << a << endl;
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a = a.transpose(); // !!! do NOT do this !!!
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cout << "and the result of the aliasing effect:\n" << a << endl;
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@ -1,4 +1,6 @@
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MatrixXf a(2,3); a << 1, 2, 3, 4, 5, 6;
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cout << "Here is the initial matrix a:\n" << a << endl;
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a.transposeInPlace();
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cout << "and after being transposed:\n" << a << endl;
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