Clang-format tests, examples, libraries, benchmarks, etc.

doc/snippets/AngleAxis_mimic_euler.cpp

@@ -1,5 +1,4 @@
 Matrix3f m;
-m = AngleAxisf(0.25*M_PI, Vector3f::UnitX())
-  * AngleAxisf(0.5*M_PI,  Vector3f::UnitY())
-  * AngleAxisf(0.33*M_PI, Vector3f::UnitZ());
+m = AngleAxisf(0.25 * M_PI, Vector3f::UnitX()) * AngleAxisf(0.5 * M_PI, Vector3f::UnitY()) *
+    AngleAxisf(0.33 * M_PI, Vector3f::UnitZ());
 cout << m << endl << "is unitary: " << m.isUnitary() << endl;

doc/snippets/Array_initializer_list_23_cxx11.cpp

@@ -1,5 +1,2 @@
-ArrayXXi a {
-  {1, 2, 3},
-  {3, 4, 5}
-};
+ArrayXXi a{{1, 2, 3}, {3, 4, 5}};
 cout << a << endl;

doc/snippets/Array_initializer_list_vector_cxx11.cpp

@@ -1,2 +1,2 @@
-Array<int, Dynamic, 1> v {{1, 2, 3, 4, 5}};
+Array<int, Dynamic, 1> v{{1, 2, 3, 4, 5}};
 cout << v << endl;

doc/snippets/Array_variadic_ctor_cxx11.cpp

@@ -1,3 +1,3 @@
 Array<int, 1, 6> a(1, 2, 3, 4, 5, 6);
-Array<int, 3, 1> b {1, 2, 3};
+Array<int, 3, 1> b{1, 2, 3};
 cout << a << "\n\n" << b << endl;

doc/snippets/BiCGSTAB_simple.cpp

@@ -1,11 +1,11 @@
-  int n = 10000;
-  VectorXd x(n), b(n);
-  SparseMatrix<double> A(n,n);
-  /* ... fill A and b ... */ 
-  BiCGSTAB<SparseMatrix<double> > solver;
-  solver.compute(A);
-  x = solver.solve(b);
-  std::cout << "#iterations:     " << solver.iterations() << std::endl;
-  std::cout << "estimated error: " << solver.error()      << std::endl;
-  /* ... update b ... */
-  x = solver.solve(b); // solve again
+int n = 10000;
+VectorXd x(n), b(n);
+SparseMatrix<double> A(n, n);
+/* ... fill A and b ... */
+BiCGSTAB<SparseMatrix<double> > solver;
+solver.compute(A);
+x = solver.solve(b);
+std::cout << "#iterations:     " << solver.iterations() << std::endl;
+std::cout << "estimated error: " << solver.error() << std::endl;
+/* ... update b ... */
+x = solver.solve(b);  // solve again

doc/snippets/BiCGSTAB_step_by_step.cpp

@@ -1,14 +1,14 @@
-  int n = 10000;
-  VectorXd x(n), b(n);
-  SparseMatrix<double> A(n,n);
-  /* ... fill A and b ... */ 
-  BiCGSTAB<SparseMatrix<double> > solver(A);
-  // start from a random solution
-  x = VectorXd::Random(n);
-  solver.setMaxIterations(1);
-  int i = 0;
-  do {
-    x = solver.solveWithGuess(b,x);
-    std::cout << i << " : " << solver.error() << std::endl;
-    ++i;
-  } while (solver.info()!=Success && i<100);
+int n = 10000;
+VectorXd x(n), b(n);
+SparseMatrix<double> A(n, n);
+/* ... fill A and b ... */
+BiCGSTAB<SparseMatrix<double> > solver(A);
+// start from a random solution
+x = VectorXd::Random(n);
+solver.setMaxIterations(1);
+int i = 0;
+do {
+  x = solver.solveWithGuess(b, x);
+  std::cout << i << " : " << solver.error() << std::endl;
+  ++i;
+} while (solver.info() != Success && i < 100);

doc/snippets/ColPivHouseholderQR_solve.cpp

@@ -4,5 +4,5 @@ cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is the matrix y:" << endl << y << endl;
 Matrix3f x;
 x = m.colPivHouseholderQr().solve(y);
-assert(y.isApprox(m*x));
+assert(y.isApprox(m* x));
 cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;

doc/snippets/ComplexEigenSolver_compute.cpp

@@ -1,4 +1,4 @@
-MatrixXcf A = MatrixXcf::Random(4,4);
+MatrixXcf A = MatrixXcf::Random(4, 4);
 cout << "Here is a random 4x4 matrix, A:" << endl << A << endl << endl;
 
 ComplexEigenSolver<MatrixXcf> ces;

doc/snippets/ComplexEigenSolver_eigenvalues.cpp

@@ -1,4 +1,3 @@
-MatrixXcf ones = MatrixXcf::Ones(3,3);
+MatrixXcf ones = MatrixXcf::Ones(3, 3);
 ComplexEigenSolver<MatrixXcf> ces(ones, /* computeEigenvectors = */ false);
-cout << "The eigenvalues of the 3x3 matrix of ones are:" 
-     << endl << ces.eigenvalues() << endl;
+cout << "The eigenvalues of the 3x3 matrix of ones are:" << endl << ces.eigenvalues() << endl;

doc/snippets/ComplexEigenSolver_eigenvectors.cpp

@@ -1,4 +1,3 @@
-MatrixXcf ones = MatrixXcf::Ones(3,3);
+MatrixXcf ones = MatrixXcf::Ones(3, 3);
 ComplexEigenSolver<MatrixXcf> ces(ones);
-cout << "The first eigenvector of the 3x3 matrix of ones is:" 
-     << endl << ces.eigenvectors().col(0) << endl;
+cout << "The first eigenvector of the 3x3 matrix of ones is:" << endl << ces.eigenvectors().col(0) << endl;

doc/snippets/ComplexSchur_compute.cpp

@@ -1,4 +1,4 @@
-MatrixXcf A = MatrixXcf::Random(4,4);
+MatrixXcf A = MatrixXcf::Random(4, 4);
 ComplexSchur<MatrixXcf> schur(4);
 schur.compute(A);
 cout << "The matrix T in the decomposition of A is:" << endl << schur.matrixT() << endl;

doc/snippets/ComplexSchur_matrixT.cpp

@@ -1,4 +1,4 @@
-MatrixXcf A = MatrixXcf::Random(4,4);
+MatrixXcf A = MatrixXcf::Random(4, 4);
 cout << "Here is a random 4x4 matrix, A:" << endl << A << endl << endl;
-ComplexSchur<MatrixXcf> schurOfA(A, false); // false means do not compute U
+ComplexSchur<MatrixXcf> schurOfA(A, false);  // false means do not compute U
 cout << "The triangular matrix T is:" << endl << schurOfA.matrixT() << endl;

doc/snippets/ComplexSchur_matrixU.cpp

@@ -1,4 +1,4 @@
-MatrixXcf A = MatrixXcf::Random(4,4);
+MatrixXcf A = MatrixXcf::Random(4, 4);
 cout << "Here is a random 4x4 matrix, A:" << endl << A << endl << endl;
 ComplexSchur<MatrixXcf> schurOfA(A);
 cout << "The unitary matrix U is:" << endl << schurOfA.matrixU() << endl;

doc/snippets/Cwise_abs.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,-2,-3);
+Array3d v(1, -2, -3);
 cout << v.abs() << endl;

doc/snippets/Cwise_abs2.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,-2,-3);
+Array3d v(1, -2, -3);
 cout << v.abs2() << endl;

doc/snippets/Cwise_acos.cpp

@@ -1,2 +1,2 @@
-Array3d v(0, sqrt(2.)/2, 1);
+Array3d v(0, sqrt(2.) / 2, 1);
 cout << v.acos() << endl;

doc/snippets/Cwise_array_atan2_array.cpp

@@ -1,4 +1,3 @@
-Array<double,1,3> x(8,-25,3),
-                  y(1./3.,0.5,-2.);
-cout << "atan2([" << x << "], [" << y << "]) = " << x.atan2(y) << endl; // using ArrayBase::pow
-cout << "atan2([" << x << "], [" << y << "] = " << atan2(x,y) << endl; // using Eigen::pow
+Array<double, 1, 3> x(8, -25, 3), y(1. / 3., 0.5, -2.);
+cout << "atan2([" << x << "], [" << y << "]) = " << x.atan2(y) << endl;  // using ArrayBase::pow
+cout << "atan2([" << x << "], [" << y << "] = " << atan2(x, y) << endl;  // using Eigen::pow

doc/snippets/Cwise_array_power_array.cpp

@@ -1,4 +1,3 @@
-Array<double,1,3> x(8,25,3),
-                  e(1./3.,0.5,2.);
-cout << "[" << x << "]^[" << e << "] = " << x.pow(e) << endl; // using ArrayBase::pow
-cout << "[" << x << "]^[" << e << "] = " << pow(x,e) << endl; // using Eigen::pow
+Array<double, 1, 3> x(8, 25, 3), e(1. / 3., 0.5, 2.);
+cout << "[" << x << "]^[" << e << "] = " << x.pow(e) << endl;   // using ArrayBase::pow
+cout << "[" << x << "]^[" << e << "] = " << pow(x, e) << endl;  // using Eigen::pow

doc/snippets/Cwise_asin.cpp

@@ -1,2 +1,2 @@
-Array3d v(0, sqrt(2.)/2, 1);
+Array3d v(0, sqrt(2.) / 2, 1);
 cout << v.asin() << endl;

doc/snippets/Cwise_atan.cpp

@@ -1,2 +1,2 @@
-ArrayXd v = ArrayXd::LinSpaced(5,0,1);
+ArrayXd v = ArrayXd::LinSpaced(5, 0, 1);
 cout << v.atan() << endl;

doc/snippets/Cwise_boolean_and.cpp

@@ -1,2 +1,2 @@
-Array3d v(-1,2,1), w(-3,2,3);
-cout << ((v<w) && (v<0)) << endl;
+Array3d v(-1, 2, 1), w(-3, 2, 3);
+cout << ((v < w) && (v < 0)) << endl;

doc/snippets/Cwise_boolean_not.cpp

@@ -1,5 +1,5 @@
-Array3d v(1,2,3);
-v(1) *= 0.0/0.0;
+Array3d v(1, 2, 3);
+v(1) *= 0.0 / 0.0;
 v(2) /= 0.0;
 cout << v << endl << endl;
 cout << !isfinite(v) << endl;

doc/snippets/Cwise_boolean_or.cpp

@@ -1,2 +1,2 @@
-Array3d v(-1,2,1), w(-3,2,3);
-cout << ((v<w) || (v<0)) << endl;
+Array3d v(-1, 2, 1), w(-3, 2, 3);
+cout << ((v < w) || (v < 0)) << endl;

doc/snippets/Cwise_cbrt.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,4);
+Array3d v(1, 2, 4);
 cout << v.cbrt() << endl;

doc/snippets/Cwise_ceil.cpp

@@ -1,3 +1,3 @@
-ArrayXd v = ArrayXd::LinSpaced(7,-2,2);
+ArrayXd v = ArrayXd::LinSpaced(7, -2, 2);
 cout << v << endl << endl;
 cout << ceil(v) << endl;

doc/snippets/Cwise_cos.cpp

@@ -1,2 +1,2 @@
-Array3d v(M_PI, M_PI/2, M_PI/3);
+Array3d v(M_PI, M_PI / 2, M_PI / 3);
 cout << v.cos() << endl;

doc/snippets/Cwise_cosh.cpp

@@ -1,2 +1,2 @@
-ArrayXd v = ArrayXd::LinSpaced(5,0,1);
+ArrayXd v = ArrayXd::LinSpaced(5, 0, 1);
 cout << cosh(v) << endl;

doc/snippets/Cwise_cube.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4);
+Array3d v(2, 3, 4);
 cout << v.cube() << endl;

doc/snippets/Cwise_equal_equal.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v==w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v == w) << endl;

doc/snippets/Cwise_exp.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3);
+Array3d v(1, 2, 3);
 cout << v.exp() << endl;

doc/snippets/Cwise_floor.cpp

@@ -1,3 +1,3 @@
-ArrayXd v = ArrayXd::LinSpaced(7,-2,2);
+ArrayXd v = ArrayXd::LinSpaced(7, -2, 2);
 cout << v << endl << endl;
 cout << floor(v) << endl;

doc/snippets/Cwise_greater.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v>w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v > w) << endl;

doc/snippets/Cwise_greater_equal.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v>=w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v >= w) << endl;

doc/snippets/Cwise_inverse.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4);
+Array3d v(2, 3, 4);
 cout << v.inverse() << endl;

doc/snippets/Cwise_isFinite.cpp

@@ -1,5 +1,5 @@
-Array3d v(1,2,3);
-v(1) *= 0.0/0.0;
+Array3d v(1, 2, 3);
+v(1) *= 0.0 / 0.0;
 v(2) /= 0.0;
 cout << v << endl << endl;
 cout << isfinite(v) << endl;

doc/snippets/Cwise_isInf.cpp

@@ -1,5 +1,5 @@
-Array3d v(1,2,3);
-v(1) *= 0.0/0.0;
+Array3d v(1, 2, 3);
+v(1) *= 0.0 / 0.0;
 v(2) /= 0.0;
 cout << v << endl << endl;
 cout << isinf(v) << endl;

doc/snippets/Cwise_isNaN.cpp

@@ -1,5 +1,5 @@
-Array3d v(1,2,3);
-v(1) *= 0.0/0.0;
+Array3d v(1, 2, 3);
+v(1) *= 0.0 / 0.0;
 v(2) /= 0.0;
 cout << v << endl << endl;
 cout << isnan(v) << endl;

doc/snippets/Cwise_less.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v<w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v < w) << endl;

doc/snippets/Cwise_less_equal.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v<=w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v <= w) << endl;

doc/snippets/Cwise_log.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3);
+Array3d v(1, 2, 3);
 cout << v.log() << endl;

doc/snippets/Cwise_log10.cpp

@@ -1,2 +1,2 @@
-Array4d v(-1,0,1,2);
+Array4d v(-1, 0, 1, 2);
 cout << log10(v) << endl;

doc/snippets/Cwise_max.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4), w(4,2,3);
+Array3d v(2, 3, 4), w(4, 2, 3);
 cout << v.max(w) << endl;

doc/snippets/Cwise_min.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4), w(4,2,3);
+Array3d v(2, 3, 4), w(4, 2, 3);
 cout << v.min(w) << endl;

doc/snippets/Cwise_minus.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3);
-cout << v-5 << endl;
+Array3d v(1, 2, 3);
+cout << v - 5 << endl;

doc/snippets/Cwise_minus_equal.cpp

@@ -1,3 +1,3 @@
-Array3d v(1,2,3);
+Array3d v(1, 2, 3);
 v -= 5;
 cout << v << endl;

doc/snippets/Cwise_not_equal.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3), w(3,2,1);
-cout << (v!=w) << endl;
+Array3d v(1, 2, 3), w(3, 2, 1);
+cout << (v != w) << endl;

doc/snippets/Cwise_plus.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,3);
-cout << v+5 << endl;
+Array3d v(1, 2, 3);
+cout << v + 5 << endl;

doc/snippets/Cwise_plus_equal.cpp

@@ -1,3 +1,3 @@
-Array3d v(1,2,3);
+Array3d v(1, 2, 3);
 v += 5;
 cout << v << endl;

doc/snippets/Cwise_pow.cpp

@@ -1,2 +1,2 @@
-Array3d v(8,27,64);
+Array3d v(8, 27, 64);
 cout << v.pow(0.333333) << endl;

doc/snippets/Cwise_product.cpp

@@ -1,4 +1,3 @@
 Array33i a = Array33i::Random(), b = Array33i::Random();
 Array33i c = a * b;
 cout << "a:\n" << a << "\nb:\n" << b << "\nc:\n" << c << endl;
-

doc/snippets/Cwise_quotient.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4), w(4,2,3);
-cout << v/w << endl;
+Array3d v(2, 3, 4), w(4, 2, 3);
+cout << v / w << endl;

doc/snippets/Cwise_rint.cpp

@@ -1,3 +1,3 @@
-ArrayXd v = ArrayXd::LinSpaced(7,-2,2);
+ArrayXd v = ArrayXd::LinSpaced(7, -2, 2);
 cout << v << endl << endl;
 cout << rint(v) << endl;

doc/snippets/Cwise_round.cpp

@@ -1,3 +1,3 @@
-ArrayXd v = ArrayXd::LinSpaced(7,-2,2);
+ArrayXd v = ArrayXd::LinSpaced(7, -2, 2);
 cout << v << endl << endl;
 cout << round(v) << endl;

doc/snippets/Cwise_scalar_power_array.cpp

@@ -1,2 +1,2 @@
-Array<double,1,3> e(2,-3,1./3.);
-cout << "10^[" << e << "] = " << pow(10,e) << endl;
+Array<double, 1, 3> e(2, -3, 1. / 3.);
+cout << "10^[" << e << "] = " << pow(10, e) << endl;

doc/snippets/Cwise_sign.cpp

@@ -1,2 +1,2 @@
-Array3d v(-3,5,0);
+Array3d v(-3, 5, 0);
 cout << v.sign() << endl;

doc/snippets/Cwise_sin.cpp

@@ -1,2 +1,2 @@
-Array3d v(M_PI, M_PI/2, M_PI/3);
+Array3d v(M_PI, M_PI / 2, M_PI / 3);
 cout << v.sin() << endl;

doc/snippets/Cwise_sinh.cpp

@@ -1,2 +1,2 @@
-ArrayXd v = ArrayXd::LinSpaced(5,0,1);
+ArrayXd v = ArrayXd::LinSpaced(5, 0, 1);
 cout << sinh(v) << endl;

doc/snippets/Cwise_slash_equal.cpp

@@ -1,3 +1,3 @@
-Array3d v(3,2,4), w(5,4,2);
+Array3d v(3, 2, 4), w(5, 4, 2);
 v /= w;
 cout << v << endl;

doc/snippets/Cwise_sqrt.cpp

@@ -1,2 +1,2 @@
-Array3d v(1,2,4);
+Array3d v(1, 2, 4);
 cout << v.sqrt() << endl;

doc/snippets/Cwise_square.cpp

@@ -1,2 +1,2 @@
-Array3d v(2,3,4);
+Array3d v(2, 3, 4);
 cout << v.square() << endl;

doc/snippets/Cwise_tan.cpp

@@ -1,2 +1,2 @@
-Array3d v(M_PI, M_PI/2, M_PI/3);
+Array3d v(M_PI, M_PI / 2, M_PI / 3);
 cout << v.tan() << endl;

doc/snippets/Cwise_tanh.cpp

@@ -1,2 +1,2 @@
-ArrayXd v = ArrayXd::LinSpaced(5,0,1);
+ArrayXd v = ArrayXd::LinSpaced(5, 0, 1);
 cout << tanh(v) << endl;

doc/snippets/Cwise_times_equal.cpp

@@ -1,3 +1,3 @@
-Array3d v(1,2,3), w(2,3,0);
+Array3d v(1, 2, 3), w(2, 3, 0);
 v *= w;
 cout << v << endl;

doc/snippets/DenseBase_LinSpaced.cpp

@@ -1,2 +1,2 @@
-cout << VectorXi::LinSpaced(4,7,10).transpose() << endl;
-cout << VectorXd::LinSpaced(5,0.0,1.0).transpose() << endl;
+cout << VectorXi::LinSpaced(4, 7, 10).transpose() << endl;
+cout << VectorXd::LinSpaced(5, 0.0, 1.0).transpose() << endl;

doc/snippets/DenseBase_LinSpacedInt.cpp

@@ -1,8 +1,8 @@
 cout << "Even spacing inputs:" << endl;
-cout << VectorXi::LinSpaced(8,1,4).transpose() << endl;
-cout << VectorXi::LinSpaced(8,1,8).transpose() << endl;
-cout << VectorXi::LinSpaced(8,1,15).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 4).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 8).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 15).transpose() << endl;
 cout << "Uneven spacing inputs:" << endl;
-cout << VectorXi::LinSpaced(8,1,7).transpose() << endl;
-cout << VectorXi::LinSpaced(8,1,9).transpose() << endl;
-cout << VectorXi::LinSpaced(8,1,16).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 7).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 9).transpose() << endl;
+cout << VectorXi::LinSpaced(8, 1, 16).transpose() << endl;

doc/snippets/DenseBase_LinSpaced_seq_deprecated.cpp

@@ -1,2 +1,2 @@
-cout << VectorXi::LinSpaced(Sequential,4,7,10).transpose() << endl;
-cout << VectorXd::LinSpaced(Sequential,5,0.0,1.0).transpose() << endl;
+cout << VectorXi::LinSpaced(Sequential, 4, 7, 10).transpose() << endl;
+cout << VectorXd::LinSpaced(Sequential, 5, 0.0, 1.0).transpose() << endl;

doc/snippets/DenseBase_setLinSpaced.cpp

@@ -1,3 +1,3 @@
 VectorXf v;
-v.setLinSpaced(5,0.5f,1.5f);
+v.setLinSpaced(5, 0.5f, 1.5f);
 cout << v << endl;

doc/snippets/DirectionWise_hnormalized.cpp

@@ -1,6 +1,6 @@
-Matrix4Xd M = Matrix4Xd::Random(4,5);
+Matrix4Xd M = Matrix4Xd::Random(4, 5);
 Projective3d P(Matrix4d::Random());
 cout << "The matrix M is:" << endl << M << endl << endl;
 cout << "M.colwise().hnormalized():" << endl << M.colwise().hnormalized() << endl << endl;
-cout << "P*M:" << endl << P*M << endl << endl;
-cout << "(P*M).colwise().hnormalized():" << endl << (P*M).colwise().hnormalized() << endl << endl;
+cout << "P*M:" << endl << P * M << endl << endl;
+cout << "(P*M).colwise().hnormalized():" << endl << (P * M).colwise().hnormalized() << endl << endl;

doc/snippets/DirectionWise_replicate.cpp

@@ -1,4 +1,4 @@
-MatrixXi m = MatrixXi::Random(2,3);
+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;

doc/snippets/EigenSolver_EigenSolver_MatrixType.cpp

@@ -1,4 +1,4 @@
-MatrixXd A = MatrixXd::Random(6,6);
+MatrixXd A = MatrixXd::Random(6, 6);
 cout << "Here is a random 6x6 matrix, A:" << endl << A << endl << endl;
 
 EigenSolver<MatrixXd> es(A);

doc/snippets/EigenSolver_compute.cpp

@@ -1,6 +1,6 @@
 EigenSolver<MatrixXf> es;
-MatrixXf A = MatrixXf::Random(4,4);
+MatrixXf A = MatrixXf::Random(4, 4);
 es.compute(A, /* computeEigenvectors = */ false);
 cout << "The eigenvalues of A are: " << es.eigenvalues().transpose() << endl;
-es.compute(A + MatrixXf::Identity(4,4), false); // re-use es to compute eigenvalues of A+I
+es.compute(A + MatrixXf::Identity(4, 4), false);  // re-use es to compute eigenvalues of A+I
 cout << "The eigenvalues of A+I are: " << es.eigenvalues().transpose() << endl;

doc/snippets/EigenSolver_eigenvalues.cpp

@@ -1,4 +1,3 @@
-MatrixXd ones = MatrixXd::Ones(3,3);
+MatrixXd ones = MatrixXd::Ones(3, 3);
 EigenSolver<MatrixXd> es(ones, false);
-cout << "The eigenvalues of the 3x3 matrix of ones are:" 
-     << endl << es.eigenvalues() << endl;
+cout << "The eigenvalues of the 3x3 matrix of ones are:" << endl << es.eigenvalues() << endl;

doc/snippets/EigenSolver_eigenvectors.cpp

@@ -1,4 +1,3 @@
-MatrixXd ones = MatrixXd::Ones(3,3);
+MatrixXd ones = MatrixXd::Ones(3, 3);
 EigenSolver<MatrixXd> es(ones);
-cout << "The first eigenvector of the 3x3 matrix of ones is:"
-     << endl << es.eigenvectors().col(0) << endl;
+cout << "The first eigenvector of the 3x3 matrix of ones is:" << endl << es.eigenvectors().col(0) << endl;

doc/snippets/EigenSolver_pseudoEigenvectors.cpp

@@ -1,4 +1,4 @@
-MatrixXd A = MatrixXd::Random(6,6);
+MatrixXd A = MatrixXd::Random(6, 6);
 cout << "Here is a random 6x6 matrix, A:" << endl << A << endl << endl;
 
 EigenSolver<MatrixXd> es(A);

doc/snippets/FullPivHouseholderQR_solve.cpp

@@ -4,5 +4,5 @@ cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is the matrix y:" << endl << y << endl;
 Matrix3f x;
 x = m.fullPivHouseholderQr().solve(y);
-assert(y.isApprox(m*x));
+assert(y.isApprox(m* x));
 cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;

doc/snippets/FullPivLU_image.cpp

@@ -1,9 +1,7 @@
 Matrix3d m;
-m << 1,1,0,
-     1,3,2,
-     0,1,1;
+m << 1, 1, 0, 1, 3, 2, 0, 1, 1;
 cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Notice that the middle column is the sum of the two others, so the "
      << "columns are linearly dependent." << endl;
-cout << "Here is a matrix whose columns have the same span but are linearly independent:"
-     << endl << m.fullPivLu().image(m) << endl;
+cout << "Here is a matrix whose columns have the same span but are linearly independent:" << endl
+     << m.fullPivLu().image(m) << endl;

doc/snippets/FullPivLU_kernel.cpp

@@ -1,7 +1,5 @@
-MatrixXf m = MatrixXf::Random(3,5);
+MatrixXf m = MatrixXf::Random(3, 5);
 cout << "Here is the matrix m:" << endl << m << endl;
 MatrixXf ker = m.fullPivLu().kernel();
-cout << "Here is a matrix whose columns form a basis of the kernel of m:"
-     << endl << ker << endl;
-cout << "By definition of the kernel, m*ker is zero:"
-     << endl << m*ker << endl;
+cout << "Here is a matrix whose columns form a basis of the kernel of m:" << endl << ker << endl;
+cout << "By definition of the kernel, m*ker is zero:" << endl << m * ker << endl;

doc/snippets/FullPivLU_solve.cpp

@@ -1,11 +1,9 @@
-Matrix<float,2,3> m = Matrix<float,2,3>::Random();
+Matrix<float, 2, 3> m = Matrix<float, 2, 3>::Random();
 Matrix2f y = Matrix2f::Random();
 cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is the matrix y:" << endl << y << endl;
-Matrix<float,3,2> x = m.fullPivLu().solve(y);
-if((m*x).isApprox(y))
-{
+Matrix<float, 3, 2> x = m.fullPivLu().solve(y);
+if ((m * x).isApprox(y)) {
   cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;
-}
-else
+} else
   cout << "The equation mx=y does not have any solution." << endl;

doc/snippets/GeneralizedEigenSolver.cpp

@@ -1,6 +1,6 @@
 GeneralizedEigenSolver<MatrixXf> ges;
-MatrixXf A = MatrixXf::Random(4,4);
-MatrixXf B = MatrixXf::Random(4,4);
+MatrixXf A = MatrixXf::Random(4, 4);
+MatrixXf B = MatrixXf::Random(4, 4);
 ges.compute(A, B);
 cout << "The (complex) numerators of the generalzied eigenvalues are: " << ges.alphas().transpose() << endl;
 cout << "The (real) denominatore of the generalzied eigenvalues are: " << ges.betas().transpose() << endl;

doc/snippets/HessenbergDecomposition_compute.cpp

@@ -1,6 +1,6 @@
-MatrixXcf A = MatrixXcf::Random(4,4);
+MatrixXcf A = MatrixXcf::Random(4, 4);
 HessenbergDecomposition<MatrixXcf> hd(4);
 hd.compute(A);
 cout << "The matrix H in the decomposition of A is:" << endl << hd.matrixH() << endl;
-hd.compute(2*A); // re-use hd to compute and store decomposition of 2A
+hd.compute(2 * A);  // re-use hd to compute and store decomposition of 2A
 cout << "The matrix H in the decomposition of 2A is:" << endl << hd.matrixH() << endl;

doc/snippets/HessenbergDecomposition_matrixH.cpp

@@ -1,4 +1,4 @@
-Matrix4f A = MatrixXf::Random(4,4);
+Matrix4f A = MatrixXf::Random(4, 4);
 cout << "Here is a random 4x4 matrix:" << endl << A << endl;
 HessenbergDecomposition<MatrixXf> hessOfA(A);
 MatrixXf H = hessOfA.matrixH();

doc/snippets/HessenbergDecomposition_packedMatrix.cpp

@@ -1,9 +1,8 @@
-Matrix4d A = Matrix4d::Random(4,4);
+Matrix4d A = Matrix4d::Random(4, 4);
 cout << "Here is a random 4x4 matrix:" << endl << A << endl;
 HessenbergDecomposition<Matrix4d> hessOfA(A);
 Matrix4d pm = hessOfA.packedMatrix();
 cout << "The packed matrix M is:" << endl << pm << endl;
-cout << "The upper Hessenberg part corresponds to the matrix H, which is:" 
-     << endl << hessOfA.matrixH() << endl;
+cout << "The upper Hessenberg part corresponds to the matrix H, which is:" << endl << hessOfA.matrixH() << endl;
 Vector3d hc = hessOfA.householderCoefficients();
 cout << "The vector of Householder coefficients is:" << endl << hc << endl;

doc/snippets/HouseholderQR_householderQ.cpp

@@ -1,4 +1,4 @@
-MatrixXf A(MatrixXf::Random(5,3)), thinQ(MatrixXf::Identity(5,3)), Q;
+MatrixXf A(MatrixXf::Random(5, 3)), thinQ(MatrixXf::Identity(5, 3)), Q;
 A.setRandom();
 HouseholderQR<MatrixXf> qr(A);
 Q = qr.householderQ();

doc/snippets/HouseholderQR_solve.cpp

@@ -1,9 +1,9 @@
-typedef Matrix<float,3,3> Matrix3x3;
+typedef Matrix<float, 3, 3> Matrix3x3;
 Matrix3x3 m = Matrix3x3::Random();
 Matrix3f y = Matrix3f::Random();
 cout << "Here is the matrix m:" << endl << m << endl;
 cout << "Here is the matrix y:" << endl << y << endl;
 Matrix3f x;
 x = m.householderQr().solve(y);
-assert(y.isApprox(m*x));
+assert(y.isApprox(m* x));
 cout << "Here is a solution x to the equation mx=y:" << endl << x << endl;

doc/snippets/HouseholderSequence_HouseholderSequence.cpp

@@ -2,10 +2,10 @@ Matrix3d v = Matrix3d::Random();
 cout << "The matrix v is:" << endl;
 cout << v << endl;
 
-Vector3d v0(1, v(1,0), v(2,0));
+Vector3d v0(1, v(1, 0), v(2, 0));
 cout << "The first Householder vector is: v_0 = " << v0.transpose() << endl;
-Vector3d v1(0, 1, v(2,1));
-cout << "The second Householder vector is: v_1 = " << v1.transpose()  << endl;
+Vector3d v1(0, 1, v(2, 1));
+cout << "The second Householder vector is: v_1 = " << v1.transpose() << endl;
 Vector3d v2(0, 0, 1);
 cout << "The third Householder vector is: v_2 = " << v2.transpose() << endl;
 

doc/snippets/JacobiSVD_basic.cpp

@@ -1,4 +1,4 @@
-MatrixXf m = MatrixXf::Random(3,2);
+MatrixXf m = MatrixXf::Random(3, 2);
 cout << "Here is the matrix m:" << endl << m << endl;
 JacobiSVD<MatrixXf, ComputeThinU | ComputeThinV> svd(m);
 cout << "Its singular values are:" << endl << svd.singularValues() << endl;

doc/snippets/LLT_example.cpp

@@ -1,9 +1,9 @@
-MatrixXd A(3,3);
-A << 4,-1,2, -1,6,0, 2,0,5;
+MatrixXd A(3, 3);
+A << 4, -1, 2, -1, 6, 0, 2, 0, 5;
 cout << "The matrix A is" << endl << A << endl;
 
-LLT<MatrixXd> lltOfA(A); // compute the Cholesky decomposition of A
-MatrixXd L = lltOfA.matrixL(); // retrieve factor L  in the decomposition
+LLT<MatrixXd> lltOfA(A);        // compute the Cholesky decomposition of A
+MatrixXd L = lltOfA.matrixL();  // retrieve factor L  in the decomposition
 // The previous two lines can also be written as "L = A.llt().matrixL()"
 
 cout << "The Cholesky factor L is" << endl << L << endl;

doc/snippets/LLT_solve.cpp

@@ -1,8 +1,7 @@
-typedef Matrix<float,Dynamic,2> DataMatrix;
+typedef Matrix<float, Dynamic, 2> DataMatrix;
 // let's generate some samples on the 3D plane of equation z = 2x+3y (with some noise)
-DataMatrix samples = DataMatrix::Random(12,2);
-VectorXf elevations = 2*samples.col(0) + 3*samples.col(1) + VectorXf::Random(12)*0.1;
+DataMatrix samples = DataMatrix::Random(12, 2);
+VectorXf elevations = 2 * samples.col(0) + 3 * samples.col(1) + VectorXf::Random(12) * 0.1;
 // and let's solve samples * [x y]^T = elevations in least square sense:
-Matrix<float,2,1> xy
- = (samples.adjoint() * samples).llt().solve((samples.adjoint()*elevations));
+Matrix<float, 2, 1> xy = (samples.adjoint() * samples).llt().solve((samples.adjoint() * elevations));
 cout << xy << endl;

doc/snippets/LeastSquaresNormalEquations.cpp

@@ -1,4 +1,3 @@
 MatrixXf A = MatrixXf::Random(3, 2);
 VectorXf b = VectorXf::Random(3);
-cout << "The solution using normal equations is:\n"
-     << (A.transpose() * A).ldlt().solve(A.transpose() * b) << endl;
+cout << "The solution using normal equations is:\n" << (A.transpose() * A).ldlt().solve(A.transpose() * b) << endl;

doc/snippets/LeastSquaresQR.cpp

@@ -1,4 +1,3 @@
 MatrixXf A = MatrixXf::Random(3, 2);
 VectorXf b = VectorXf::Random(3);
-cout << "The solution using the QR decomposition is:\n"
-     << A.colPivHouseholderQr().solve(b) << endl;
+cout << "The solution using the QR decomposition is:\n" << A.colPivHouseholderQr().solve(b) << endl;

doc/snippets/Map_general_stride.cpp

@@ -1,5 +1,3 @@
 int array[24];
-for(int i = 0; i < 24; ++i) array[i] = i;
-cout << Map<MatrixXi, 0, Stride<Dynamic,2> >
-         (array, 3, 3, Stride<Dynamic,2>(8, 2))
-     << endl;
+for (int i = 0; i < 24; ++i) array[i] = i;
+cout << Map<MatrixXi, 0, Stride<Dynamic, 2> >(array, 3, 3, Stride<Dynamic, 2>(8, 2)) << endl;

doc/snippets/Map_inner_stride.cpp

@@ -1,5 +1,4 @@
 int array[12];
-for(int i = 0; i < 12; ++i) array[i] = i;
-cout << Map<VectorXi, 0, InnerStride<2> >
-         (array, 6) // the inner stride has already been passed as template parameter
+for (int i = 0; i < 12; ++i) array[i] = i;
+cout << Map<VectorXi, 0, InnerStride<2> >(array, 6)  // the inner stride has already been passed as template parameter
      << endl;

doc/snippets/Map_outer_stride.cpp

@@ -1,3 +1,3 @@
 int array[12];
-for(int i = 0; i < 12; ++i) array[i] = i;
+for (int i = 0; i < 12; ++i) array[i] = i;
 cout << Map<MatrixXi, 0, OuterStride<> >(array, 3, 3, OuterStride<>(4)) << endl;

doc/snippets/Map_placement_new.cpp

@@ -1,5 +1,5 @@
-int data[] = {1,2,3,4,5,6,7,8,9};
-Map<RowVectorXi> v(data,4);
+int data[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
+Map<RowVectorXi> v(data, 4);
 cout << "The mapped vector v is: " << v << "\n";
-new (&v) Map<RowVectorXi>(data+4,5);
+new (&v) Map<RowVectorXi>(data + 4, 5);
 cout << "Now v is: " << v << "\n";

doc/snippets/Map_simple.cpp

@@ -1,3 +1,3 @@
 int array[9];
-for(int i = 0; i < 9; ++i) array[i] = i;
+for (int i = 0; i < 9; ++i) array[i] = i;
 cout << Map<Matrix3i>(array) << endl;

doc/snippets/MatrixBase_all.cpp

@@ -1,7 +1,7 @@
 Vector3f boxMin(Vector3f::Zero()), boxMax(Vector3f::Ones());
 Vector3f p0 = Vector3f::Random(), p1 = Vector3f::Random().cwiseAbs();
 // let's check if p0 and p1 are inside the axis aligned box defined by the corners boxMin,boxMax:
-cout << "Is (" << p0.transpose() << ") inside the box: "
-     << ((boxMin.array()<p0.array()).all() && (boxMax.array()>p0.array()).all()) << endl;
-cout << "Is (" << p1.transpose() << ") inside the box: "
-     << ((boxMin.array()<p1.array()).all() && (boxMax.array()>p1.array()).all()) << endl;
+cout << "Is (" << p0.transpose()
+     << ") inside the box: " << ((boxMin.array() < p0.array()).all() && (boxMax.array() > p0.array()).all()) << endl;
+cout << "Is (" << p1.transpose()
+     << ") inside the box: " << ((boxMin.array() < p1.array()).all() && (boxMax.array() > p1.array()).all()) << endl;

doc/snippets/MatrixBase_applyOnTheLeft.cpp

@@ -1,7 +1,5 @@
-Matrix3f A = Matrix3f::Random(3,3), B;
-B << 0,1,0,  
-     0,0,1,  
-     1,0,0;
+Matrix3f A = Matrix3f::Random(3, 3), B;
+B << 0, 1, 0, 0, 0, 1, 1, 0, 0;
 cout << "At start, A = " << endl << A << endl;
-A.applyOnTheLeft(B); 
+A.applyOnTheLeft(B);
 cout << "After applyOnTheLeft, A = " << endl << A << endl;

doc/snippets/MatrixBase_applyOnTheRight.cpp

@@ -1,7 +1,5 @@
-Matrix3f A = Matrix3f::Random(3,3), B;
-B << 0,1,0,  
-     0,0,1,  
-     1,0,0;
+Matrix3f A = Matrix3f::Random(3, 3), B;
+B << 0, 1, 0, 0, 0, 1, 1, 0, 0;
 cout << "At start, A = " << endl << A << endl;
 A *= B;
 cout << "After A *= B, A = " << endl << A << endl;

doc/snippets/MatrixBase_array.cpp

@@ -1,4 +1,4 @@
-Vector3d v(1,2,3);
+Vector3d v(1, 2, 3);
 v.array() += 3;
 v.array() -= 2;
 cout << v << endl;

doc/snippets/MatrixBase_array_const.cpp

@@ -1,4 +1,4 @@
-Vector3d v(-1,2,-3);
+Vector3d v(-1, 2, -3);
 cout << "the absolute values:" << endl << v.array().abs() << endl;
-cout << "the absolute values plus one:" << endl << v.array().abs()+1 << endl;
+cout << "the absolute values plus one:" << endl << v.array().abs() + 1 << endl;
 cout << "sum of the squares: " << v.array().square().sum() << endl;