/external/eigen/doc/examples/ |
Tutorial_ReductionsVisitorsBroadcasting_colwise.cpp | 11 std::cout << "Column's maximum: " << std::endl 12 << mat.colwise().maxCoeff() << std::endl; member in class:std
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Tutorial_ReductionsVisitorsBroadcasting_reductions_bool.cpp | 14 cout << "(a > 0).all() = " << (a > 0).all() << endl; 15 cout << "(a > 0).any() = " << (a > 0).any() << endl; 16 cout << "(a > 0).count() = " << (a > 0).count() << endl; 17 cout << endl; local 18 cout << "(a > 2).all() = " << (a > 2).all() << endl; 19 cout << "(a > 2).any() = " << (a > 2).any() << endl; 20 cout << "(a > 2).count() = " << (a > 2).count() << endl;
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Tutorial_ReductionsVisitorsBroadcasting_reductions_norm.cpp | 18 cout << "v.squaredNorm() = " << v.squaredNorm() << endl; 19 cout << "v.norm() = " << v.norm() << endl; 20 cout << "v.lpNorm<1>() = " << v.lpNorm<1>() << endl; 21 cout << "v.lpNorm<Infinity>() = " << v.lpNorm<Infinity>() << endl; 23 cout << endl; local 24 cout << "m.squaredNorm() = " << m.squaredNorm() << endl; 25 cout << "m.norm() = " << m.norm() << endl; 26 cout << "m.lpNorm<1>() = " << m.lpNorm<1>() << endl; 27 cout << "m.lpNorm<Infinity>() = " << m.lpNorm<Infinity>() << endl;
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Tutorial_ReductionsVisitorsBroadcasting_rowwise.cpp | 11 std::cout << "Row's maximum: " << std::endl 12 << mat.rowwise().maxCoeff() << std::endl; member in class:std
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tut_matrix_coefficient_accessors.cpp | 13 std::cout << "Here is the matrix m:\n" << m << std::endl; member in class:std 17 std::cout << "Here is the vector v:\n" << v << std::endl; member in class:std
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tut_matrix_resize.cpp | 11 << m.rows() << "x" << m.cols() << std::endl; member in class:std 12 std::cout << "It has " << m.size() << " coefficients" << std::endl; member in class:std 15 std::cout << "The vector v is of size " << v.size() << std::endl; member in class:std 17 << v.rows() << "x" << v.cols() << std::endl; member in class:std
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/external/eigen/doc/snippets/ |
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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DenseBase_setLinSpaced.cpp | 3 cout << v << endl; variable
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DirectionWise_replicate.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "m.colwise().replicate<3>() = ..." << endl; variable 4 cout << m.colwise().replicate<3>() << endl;
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DirectionWise_replicate_int.cpp | 2 cout << "Here is the vector v:" << endl << v << endl; variable 3 cout << "v.rowwise().replicate(5) = ..." << endl; variable 4 cout << v.rowwise().replicate(5) << 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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JacobiSVD_basic.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 4 cout << "Its singular values are:" << endl << svd.singularValues() << endl; 5 cout << "Its left singular vectors are the columns of the thin U matrix:" << endl << svd.matrixU() << endl; 6 cout << "Its right singular vectors are the columns of the thin V matrix:" << endl << svd.matrixV() << endl; 8 cout << "Now consider this rhs vector:" << endl << rhs << endl; variable [all...] |
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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MatrixBase_adjoint.cpp | 2 cout << "Here is the 2x2 complex matrix m:" << endl << m << endl; variable 3 cout << "Here is the adjoint of m:" << endl << m.adjoint() << endl;
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MatrixBase_block_int_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here is m.block<2,2>(1,1):" << endl << m.block<2,2>(1,1) << endl; 5 cout << "Now the matrix m is:" << endl << m << endl; variable
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MatrixBase_block_int_int_int_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here is m.block(1, 1, 2, 2):" << endl << m.block(1, 1, 2, 2) << endl; 5 cout << "Now the matrix m is:" << endl << m << endl; variable
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MatrixBase_bottomLeftCorner_int_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here is m.bottomLeftCorner(2, 2):" << endl; variable 4 cout << m.bottomLeftCorner(2, 2) << endl; 6 cout << "Now the matrix m is:" << endl << m << endl; variable
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MatrixBase_bottomRightCorner_int_int.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here is m.bottomRightCorner(2, 2):" << endl; variable 4 cout << m.bottomRightCorner(2, 2) << endl; 6 cout << "Now the matrix m is:" << endl << m << endl; variable
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MatrixBase_bottomRows_int.cpp | 2 cout << "Here is the array a:" << endl << a << endl; variable 3 cout << "Here is a.bottomRows(2):" << endl; variable 4 cout << a.bottomRows(2) << endl; 6 cout << "Now the array a is:" << endl << a << endl; variable
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MatrixBase_col.cpp | 3 cout << m << endl; variable
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MatrixBase_colwise.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here is the sum of each column:" << endl << m.colwise().sum() << endl; 5 << endl << m.cwiseAbs().colwise().maxCoeff() << endl;
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MatrixBase_cwiseEqual.cpp | 4 cout << "Comparing m with identity matrix:" << endl; variable 5 cout << m.cwiseEqual(MatrixXi::Identity(2,2)) << endl; 7 cout << "Number of coefficients that are equal: " << count << endl; variable
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MatrixBase_cwiseNotEqual.cpp | 4 cout << "Comparing m with identity matrix:" << endl; variable 5 cout << m.cwiseNotEqual(MatrixXi::Identity(2,2)) << endl; 7 cout << "Number of coefficients that are not equal: " << count << endl; variable
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MatrixBase_diagonal.cpp | 2 cout << "Here is the matrix m:" << endl << m << endl; variable 3 cout << "Here are the coefficients on the main diagonal of m:" << endl 4 << m.diagonal() << endl;
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