spelling + some krazy directives

.krazy

@@ -0,0 +1 @@
+IGNORESUBS disabled,bench,build

Eigen/src/Core/CacheFriendlyProduct.h

@@ -490,7 +490,7 @@ static EIGEN_DONT_INLINE void ei_cache_friendly_product_colmajor_times_vector(
             break;
         }
       }
-    } // end explit vectorization
+    } // end explicit vectorization
 
     /* process remaining coeffs (or all if there is no explicit vectorization) */
     for (int j=alignedSize; j<size; j++)

Eigen/src/Core/GenericPacketMath.h

@@ -30,7 +30,7 @@
   * \file GenericPacketMath.h
   *
   * Default implementation for types not supported by the vectorization.
-  * In practice these functions are provided to make easier the writting
+  * In practice these functions are provided to make easier the writing
   * of generic vectorized code.
   */
 

Eigen/src/Core/IO.h

@@ -36,9 +36,9 @@ enum { Raw, AlignCols };
   *  - \b flags can be either Raw (default) or AlignCols which aligns all the columns
   *  - \b coeffSeparator string printed between two coefficients of the same row
   *  - \b rowSeparator string printed between two rows
-  *  - \b rowPrefix string printed at the begining of each row
+  *  - \b rowPrefix string printed at the beginning of each row
   *  - \b rowSuffix string printed at the end of each row
-  *  - \b matPrefix string printed at the begining of the matrix
+  *  - \b matPrefix string printed at the beginning of the matrix
   *  - \b matSuffix string printed at the end of the matrix
   *
   * Example: \include IOFormat.cpp

Eigen/src/Core/SolveTriangular.h

@@ -251,7 +251,7 @@ void MatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other
   * Therefore, if \a other is not needed anymore, it is quite faster to call solveTriangularInPlace()
   * instead of solveTriangular().
   * 
-  * For users comming from BLAS, this function (and more specifically solveTriangularInPlace()) offer
+  * For users coming from BLAS, this function (and more specifically solveTriangularInPlace()) offer
   * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
   *
   * \b Tips: to perform a \em "right-inverse-multiply" you can simply transpose the operation, e.g.:

Eigen/src/Core/util/Constants.h

@@ -181,7 +181,7 @@ enum DirectionType { Vertical, Horizontal };
 enum ProductEvaluationMode { NormalProduct, CacheFriendlyProduct, DiagonalProduct, SparseProduct };
 
 enum {
-  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignement
+  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment
     * and good size */
   InnerVectorization,
   /** \internal Vectorization path using a single loop plus scalar loops for the

Eigen/src/Core/util/Memory.h

@@ -44,7 +44,7 @@ template <typename T, int Size> struct ei_aligned_array<T,Size,false>
   T array[Size];
 };
 
-/** \internal allocates \a size * sizeof(\a T) bytes with a 16 bytes based alignement */
+/** \internal allocates \a size * sizeof(\a T) bytes with a 16 bytes based alignment */
 template<typename T>
 inline T* ei_aligned_malloc(size_t size)
 {

Eigen/src/Geometry/Quaternion.h

@@ -322,7 +322,7 @@ inline Quaternion<Scalar>& Quaternion<Scalar>::setFromTwoVectors(const MatrixBas
 template <typename Scalar>
 inline Quaternion<Scalar> Quaternion<Scalar>::inverse() const
 {
-  // FIXME should this funtion be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
+  // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
   Scalar n2 = this->norm2();
   if (n2 > 0)
     return Quaternion(conjugate().coeffs() / n2);

Eigen/src/Geometry/Transform.h

@@ -127,6 +127,13 @@ public:
   inline QMatrix toQMatrix(void) const;
   #endif
 
+  /** shortcut for m_matrix(row,col);
+    * \sa MatrixBase::operaror(int,int) const */
+  Scalar operator() (int row, int col) const { return m_matrix(row,col); }
+  /** shortcut for m_matrix(row,col);
+    * \sa MatrixBase::operaror(int,int) */
+  Scalar& operator() (int row, int col) { return m_matrix(row,col); }
+
   /** \returns a read-only expression of the transformation matrix */
   inline const MatrixType& matrix() const { return m_matrix; }
   /** \returns a writable expression of the transformation matrix */

Eigen/src/QR/Tridiagonalization.h

@@ -263,7 +263,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
       /* end optimized selfadjoint - vector product */
 
       /* Another interesting note: the above rank-2 update is much slower than the following hand written loop.
-       * After an analyse of the ASM, it seems GCC (4.2) generate poor code because of the Block. Moreover,
+       * After an analyze of the ASM, it seems GCC (4.2) generate poor code because of the Block. Moreover,
        * if we remove the specialization of Block for Matrix then it is even worse, much worse ! */
       #ifdef EIGEN_NEVER_DEFINED
       for (int j1=i+1; j1<n; ++j1)
@@ -312,7 +312,7 @@ void Tridiagonalization<MatrixType>::_compute(MatrixType& matA, CoeffVectorType&
           matA.coeffRef(i1,j1) -= matA.coeff(i1,i)*ei_conj(hCoeffs.coeff(j1-1))
                                 + hCoeffs.coeff(i1-1)*ei_conj(matA.coeff(j1,i));
       }
-      /* end optimized implemenation */
+      /* end optimized implementation */
 
       // note: at that point matA(i+1,i+1) is the (i+1)-th element of the final diagonal
       // note: the sequence of the beta values leads to the subdiagonal entries

Eigen/src/Sparse/HashMatrix.h

@@ -76,7 +76,7 @@ class HashMatrix
 
   public:
 
-    inline void startFill(int /*reserveSize = 1000 --- currenty unused, don't generate a warning*/) {}
+    inline void startFill(int /*reserveSize = 1000 --- currently unused, don't generate a warning*/) {}
 
     inline Scalar& fill(int row, int col) { return coeffRef(row, col); }
 

Eigen/src/Sparse/SparseProduct.h

@@ -297,11 +297,11 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
 //   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
 //   {
 //     // trivial product as lhs.row/rhs.col dot products
-//     // loop over the prefered order of the result
+//     // loop over the preferred order of the result
 //   }
 // };
 
-// NOTE the 2 others cases (col row *) must never occurs since they are catched
+// NOTE the 2 others cases (col row *) must never occurs since they are caught
 // by ProductReturnType which transform it to (col col *) by evaluating rhs.
 
 

disabled/ompbenchmark.cpp

@@ -46,7 +46,7 @@
 
 int main(int argc, char *argv[])
 {
-  // disbale floating point exceptions
+  // disable floating point exceptions
   // this leads to more stable bench results
   {
     int aux;

doc/examples/Tutorial_simple_example_fixed_size.cpp

@@ -11,4 +11,4 @@ int main(int, char *[])
   Vector4i v4(1, 2, 3, 4);
 
   std::cout << "m3\n" << m3 << "\nm4:\n" << m4 << "\nv4:\n" << v4 << std::endl;
-}
+}

doc/snippets/.krazy

@@ -0,0 +1 @@
+EXCLUDE copyright,license

doc/snippets/MatrixBase_set.cpp

@@ -10,4 +10,4 @@ Vector2i v1;
 v1 << 14, 15;
 m2 << v1.transpose(), 16,
       v1, m1.block(1,1,2,2);
-cout << m2 << endl;
+cout << m2 << endl;

doc/snippets/PartialRedux_maxCoeff.cpp

@@ -1,3 +1,3 @@
 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;
+cout << "Here is the maximum of each column:" << endl << m.colwise().maxCoeff() << endl;

doc/snippets/PartialRedux_minCoeff.cpp

@@ -1,3 +1,3 @@
 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;
+cout << "Here is the minimum of each column:" << endl << m.colwise().minCoeff() << endl;

doc/snippets/PartialRedux_norm.cpp

@@ -1,3 +1,3 @@
 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;
+cout << "Here is the norm of each column:" << endl << m.colwise().norm() << endl;

doc/snippets/PartialRedux_sum.cpp

@@ -1,3 +1,3 @@
 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;
+cout << "Here is the sum of each row:" << endl << m.rowwise().sum() << endl;

doc/snippets/Tutorial_commainit_01.cpp

@@ -2,4 +2,4 @@ Matrix3f m;
 m << 1, 2, 3,
      4, 5, 6,
      7, 8, 9;
-cout << m;
+cout << m;

doc/snippets/Tutorial_commainit_02.cpp

@@ -4,4 +4,4 @@ m << (Matrix3f() << 1, 2, 3, 4, 5, 6, 7, 8, 9).finished(),
      MatrixXf::Zero(3,cols-3),
      MatrixXf::Zero(rows-3,3),
      MatrixXf::Identity(rows-3,cols-3);
-cout << m;
+cout << m;

doc/tutorial.cpp

@@ -59,4 +59,4 @@ int main(int argc, char *argv[])
   m4 = m4 * m4.transpose().eval(); // forces immediate evaluation of the transpose
 
   std::cout << "*** Step 8 ***\nm3:\n" << m3 << "\nm4:\n" << m4 << std::endl;
-}
+}

test/geometry.cpp

@@ -152,6 +152,7 @@ template<typename Scalar> void geometry(void)
 
   t1.fromPositionOrientationScale(v0, q1, v1);
   VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
+  VERIFY_IS_APPROX(t1*v1, t0*v1);
 
   // 2D transformation
   Transform2 t20, t21;