| /external/eigen/doc/snippets/ |
| PartialRedux_sum.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable
3 cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << endl;
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| MatrixBase_identity.cpp | 1 cout << Matrix<double, 3, 4>::Identity() << endl;
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| MatrixBase_identity_int_int.cpp | 1 cout << MatrixXd::Identity(4, 3) << endl;
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| MatrixBase_ones_int_int.cpp | 1 cout << MatrixXi::Ones(2,3) << endl;
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| MatrixBase_random.cpp | 1 cout << 100 * Matrix2i::Random() << endl;
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| MatrixBase_random_int.cpp | 1 cout << VectorXi::Random(2) << endl;
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| MatrixBase_random_int_int.cpp | 1 cout << MatrixXi::Random(2,3) << endl;
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| MatrixBase_triangularView.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable
3 cout << "Here is the upper-triangular matrix extracted from m:" << endl
4 << Matrix3i(m.triangularView<Eigen::Upper>()) << endl;
5 cout << "Here is the strictly-upper-triangular matrix extracted from m:" << endl
6 << Matrix3i(m.triangularView<Eigen::StrictlyUpper>()) << endl;
7 cout << "Here is the unit-lower-triangular matrix extracted from m:" << endl
8 << Matrix3i(m.triangularView<Eigen::UnitLower>()) << endl;
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| MatrixBase_zero_int_int.cpp | 1 cout << MatrixXi::Zero(2,3) << endl;
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| ComplexSchur_matrixT.cpp | 2 cout << "Here is a random 4x4 matrix, A:" << endl << A << endl << endl; variable
4 cout << "The triangular matrix T is:" << endl << schurOfA.matrixT() << endl;
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| ComplexSchur_matrixU.cpp | 2 cout << "Here is a random 4x4 matrix, A:" << endl << A << endl << endl; variable
4 cout << "The unitary matrix U is:" << endl << schurOfA.matrixU() << endl;
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| FullPivLU_image.cpp | 5 cout << "Here is the matrix m:" << endl << m << endl; variable
7 << "columns are linearly dependent." << endl; variable
9 << endl << m.fullPivLu().image(m) << endl;
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| MatrixBase_diagonal_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable
3 cout << "Here are the coefficients on the 1st super-diagonal and 2nd sub-diagonal of m:" << endl
4 << m.diagonal(1).transpose() << endl
5 << m.diagonal(-2).transpose() << endl;
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| MatrixBase_diagonal_template_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable
3 cout << "Here are the coefficients on the 1st super-diagonal and 2nd sub-diagonal of m:" << endl
4 << m.diagonal<1>().transpose() << endl
5 << m.diagonal<-2>().transpose() << endl;
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| MatrixBase_array_const.cpp | 2 cout << "the absolute values:" << endl << v.array().abs() << endl;
3 cout << "the absolute values plus one:" << endl << v.array().abs()+1 << endl;
4 cout << "sum of the squares: " << v.array().square().sum() << endl;
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| RealSchur_RealSchur_MatrixType.cpp | 2 cout << "Here is a random 6x6 matrix, A:" << endl << A << endl << endl; variable
5 cout << "The orthogonal matrix U is:" << endl << schur.matrixU() << endl;
6 cout << "The quasi-triangular matrix T is:" << endl << schur.matrixT() << endl << endl; variable
10 cout << "U * T * U^T = " << endl << U * T * U.transpose() << endl;
[all...] |
| FullPivLU_solve.cpp | 3 cout << "Here is the matrix m:" << endl << m << endl; variable
4 cout << "Here is the matrix y:" << endl << y << endl; variable
8 cout << "Here is a solution x to the equation mx=y:" << endl << x << endl; variable
11 cout << "The equation mx=y does not have any solution." << endl; variable
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| LLT_example.cpp | 3 cout << "The matrix A is" << endl << A << endl; variable
9 cout << "The Cholesky factor L is" << endl << L << endl; variable
10 cout << "To check this, let us compute L * L.transpose()" << endl; variable
11 cout << L * L.transpose() << endl;
12 cout << "This should equal the matrix A" << endl; variable
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| Matrix_resize_int_int.cpp | 3 cout << "here's the 2x3 matrix m:" << endl << m << endl; variable
4 cout << "let's resize m to 3x2. This is a conservative resizing because 2*3==3*2." << endl; variable
6 cout << "here's the 3x2 matrix m:" << endl << m << endl; variable
7 cout << "now let's resize m to size 2x2. This is NOT a conservative resizing, so it becomes uninitialized:" << endl; variable
9 cout << m << endl; variable
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| SelfAdjointEigenSolver_operatorInverseSqrt.cpp | 3 cout << "Here is a random positive-definite matrix, A:" << endl << A << endl << endl; variable
6 cout << "The inverse square root of A is: " << endl; variable
7 cout << es.operatorInverseSqrt() << endl;
8 cout << "We can also compute it with operatorSqrt() and inverse(). That yields: " << endl; variable
9 cout << es.operatorSqrt().inverse() << endl;
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| SelfAdjointEigenSolver_operatorSqrt.cpp | 3 cout << "Here is a random positive-definite matrix, A:" << endl << A << endl << endl; variable
7 cout << "The square root of A is: " << endl << sqrtA << endl; variable
8 cout << "If we square this, we get: " << endl << sqrtA*sqrtA << endl; variable
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| EigenSolver_pseudoEigenvectors.cpp | 2 cout << "Here is a random 6x6 matrix, A:" << endl << A << endl << endl; variable
7 cout << "The pseudo-eigenvalue matrix D is:" << endl << D << endl; variable
8 cout << "The pseudo-eigenvector matrix V is:" << endl << V << endl; variable
9 cout << "Finally, V * D * V^(-1) = " << endl << V * D * V.inverse() << endl;
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| /external/eigen/doc/examples/ |
| Tutorial_BlockOperations_corner.cpp | 13 cout << "m.leftCols(2) =" << endl << m.leftCols(2) << endl << endl; local
14 cout << "m.bottomRows<2>() =" << endl << m.bottomRows<2>() << endl << endl; local
16 cout << "After assignment, m = " << endl << m << endl; local
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| Tutorial_BlockOperations_vector.cpp | 10 cout << "v.head(3) =" << endl << v.head(3) << endl << endl; local
11 cout << "v.tail<3>() = " << endl << v.tail<3>() << endl << endl; local
13 cout << "after 'v.segment(1,4) *= 2', v =" << endl << v << endl; local
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| Tutorial_BlockOperations_block_assignment.cpp | 13 cout << "Here is the array a:" << endl << a << endl << endl; local
15 cout << "Here is now a with m copied into its central 2x2 block:" << endl << a << endl << endl; local
17 cout << "Here is now a with bottom-right 2x3 block copied into top-left 2x2 block:" << endl << a << endl << endl; local
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