utilities.h

Go to the documentation of this file.

#ifndef _DLR_NUMERIC_UTILITIES_H_
#define _DLR_NUMERIC_UTILITIES_H_

#include <cmath>
// #include <iostream>
#include <cstdlib>

#include <dlrCommon/tags.h>
#include <dlrNumeric/numericTraits.h>
#include <dlrNumeric/array1D.h>
#include <dlrNumeric/array2D.h>
#include <dlrNumeric/array3D.h>
#include <dlrNumeric/index2D.h>
#include <dlrNumeric/vector2D.h>
#include <dlrNumeric/vector3D.h>

namespace dlr {

  namespace numeric {
    
          namespace constants {

                  const double pi = 3.141592653589793238;

          } // nameapace contants

    template <class Type>
    Array1D<Type>
    abs(const Array1D<Type>& array0);

  
    template <class Type>
    Array2D<Type>
    abs(const Array2D<Type>& array0);


    template <class Type>
    bool
    allFalse(const Array1D<Type>& array0);

  
    template <class Type>
    bool
    allTrue(const Array1D<Type>& array0);

  
    template <class Type>
    inline bool
    anyFalse(const Array1D<Type>& array0);

  
    template <class Type>
    inline bool
    anyTrue(const Array1D<Type>& array0);

  
    template <class Type>
    inline size_t
    argmax(const Array1D<Type>& array0);


    template <class IterType>
    inline size_t
    argmax(IterType beginIter, IterType endIter);


    template <class Type, class Functor>
    inline size_t
    argmax(const Array1D<Type>& array0, Functor comparator);
  

    template <class IterType, class Functor>
    inline size_t
    argmax(IterType beginIter, IterType endIter, Functor comparator);
  

    template <class Type>
    inline Index2D
    argmax2D(const Array2D<Type>& array0);


    template <class Type, class Functor>
    inline Index2D
    argmax2D(const Array2D<Type>& array0, Functor comparator);
  

    template <class Type>
    inline size_t
    argmin(const Array1D<Type>& array0);


    template <class Type, class Functor>
    inline size_t
    argmin(const Array1D<Type>& array0, Functor comparator);


    template <class Type>
    inline Index2D
    argmin2D(const Array2D<Type>& array0);


    template <class Type, class Functor>
    inline Index2D
    argmin2D(const Array2D<Type>& array0, Functor comparator);


    template <class Type>
    Array1D<size_t>
    argsort(const Array1D<Type>& array0);

  
//   /** 
//    * This function returns an array of indices, result, so that the
//    * sequence (array0[result[0]], array0[result[1]],
//    * array0[result[2]], ...) is sorted from smallest to largest using
//    * the supplied comparison operator.
//    *
//    * @param array0 The of this array will be compared to each other in
//    * order to establish the correct sequence of indices.
//    *
//    * @param comparator This argument is a functor which supports the
//    * std::binary_function<Type, Type, bool> interface, and returns
//    * true if its first argument is smaller than its second argument.
//    *
//    * @return An array of indices as described above.
//    */
//   template <class Type, class Functor>
//   Array1D<size_t>
//   argsort(const Array1D<Type>& array0, Functor comparator);

  
    template <class Type>
    Array1D<Type>
    axisMax(const Array2D<Type>& array0,
            size_t axis) {return axisMaximum(array0, axis);}

    template <class Type>
    inline Array1D<Type>
    axisMaximum(const Array2D<Type>& array0, size_t axis);

    template <class Type, class Functor>
    Array1D<Type>
    axisMaximum(const Array2D<Type>& array0, size_t axis, Functor comparator);

    template <class Type>
    inline Array1D<Type>
    axisMin(const Array2D<Type>& array0,
            size_t axis) {return axisMinimum(array0, axis);}
  
    template <class Type>
    inline Array1D<Type>
    axisMinimum(const Array2D<Type>& array0, size_t axis);

    template <class Type, class Functor>
    Array1D<Type>
    axisMinimum(const Array2D<Type>& array0, size_t axis, Functor comparator);

    template <class Type>
    inline Array1D<typename NumericTraits<Type>::SumType>
    axisSum(const Array2D<Type>& array0, size_t axis);

    template <class Type, class ResultType>
    inline Array1D<ResultType>
    axisSum(const Array2D<Type>& array0, size_t axis,
            type_tag<ResultType> resultTag);

    template <class Type, class ResultType, class Functor>
    Array1D<ResultType>
    axisSum(const Array2D<Type>& array0, size_t axis,
            type_tag<ResultType> resultTag,
            const ResultType& initialValue, Functor adder);

    template <class Type>
    Array2D<Type>
    columnIndices(size_t rows, size_t columns, type_tag<Type> typeTag);

    template <class Type0, class Type1>
    inline Array1D<Type1>
    compress(const Array1D<Type0>& condition, const Array1D<Type1>& input);

    template <class Type0, class Type1>
    Array1D<Type1>
    compress(const Array1D<Type0>& condition, const Array1D<Type1>& input,
             size_t numTrue);


    template <class Type>
    inline size_t
    count(const Array1D<Type>& array0);
  
    inline Vector3D
    cross(const Vector3D& vector0, const Vector3D& vector1);
  
    template <class Type>
    inline typename NumericTraits<Type>::ProductType
    dot(const Array1D<Type>& array0, const Array1D<Type>& array1);

    template <class Type0, class Type1, class Type2>
    inline Type2
    dot(const Array1D<Type0>& array0, const Array1D<Type1>& array1,
        type_tag<Type2> typeTag);

    inline double
    dot(const Vector2D& vector0, const Vector2D& vector1);

    inline double
    dot(const Vector3D& vector0, const Vector3D& vector1);

    template <class Type>
    Array2D<Type>
    equivalentMatrix(const Array1D<Type>& vector0, size_t rowsInMatrix);
  

    template <class Type>
    void
    getMeanAndCovariance(const Array2D<Type>& sampleArray,
                         Array1D<double>& mean,
                         Array2D<double>& covariance,
                         size_t majorAxis=0);
  
                     
    template <class Type>
    Array2D<Type>
    identity(size_t rows, size_t columns, type_tag<Type> typeTag);


    template <class Type>
    Array1D<Type>
    ln(const Array1D<Type>& array0);
  

    template <class Type>
    Array2D<Type>
    ln(const Array2D<Type>& array0);
  

    template <class Type>
    Array2D<Type>
    logicalNot(const Array2D<Type>& array0);

    template <class Type>
    inline Type
    magnitude(const Array1D<Type>& array0);

    inline double
    magnitude(const Vector2D& vector0);

    inline double
    magnitude(const Vector3D& vector0);

  
    template <class Type>
    inline typename NumericTraits<Type>::ProductType
    magnitudeSquared(const Array1D<Type>& array0);

  
    inline double
    magnitudeSquared(const Vector2D& vector0);

  
    inline double
    magnitudeSquared(const Vector3D& vector0);
  

    template <class Type>
    inline Array1D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array1D<Type>& vector0, const Array2D<Type>& matrix0);

    template <class Type0, class Type1, class Type2>
    Array1D<Type2>
    matrixMultiply(const Array1D<Type0>& vector0, const Array2D<Type0>& matrix0,
                   type_tag<Type2> typeTag);

    template <class Type>
    inline Array1D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array2D<Type>& matrix0, const Array1D<Type>& vector0);

    template <class Type0, class Type1, class Type2>
    Array1D<Type2>
    matrixMultiply(const Array2D<Type0>& matrix0, const Array1D<Type1>& vector0,
                   type_tag<Type2> typeTag);


    template <class Type>
    inline Array2D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array2D<Type>& matrix0, const Array2D<Type>& matrix1);
    
    template <class Type0, class Type1, class Type2>
    Array2D<Type2>
    matrixMultiply(const Array2D<Type0>& matrix0, const Array2D<Type1>& matrix1,
                   type_tag<Type2> typeTag);


    // The next function is commented out because some environments use
    // #define directives for min() and max(), which don't obey
    // namespaces, and makes the code not compile if we define our own
    // min/max.
    // 
    // 
    // /** 
    // * This function is an alias for the function maximum(const
    // * Array1D&) below.
    // * 
    // * @param array0 See documentation for maximum(const Array1D&).
    // *
    // * @return See documentation for maximum(const Array1D&).
    // */
    // template <class Type>
    // inline Type
    // max(const Array1D<Type>& array0) {return maximum(array0);}

    template <class Type>
    inline Type
    maximum(const Array1D<Type>& array0);

    template <class Type, class Functor>
    Type
    maximum(const Array1D<Type>& array0, Functor comparator);

    
    template <class Iterator, class Type>
    Type
    mean(const Iterator& beginIter, const Iterator& endIter);

    
    template <class Type>
    inline double
    mean(const Array1D<Type>& array0);

    
    template <class Type0, class Type1>
    inline Type1
    mean(const Array1D<Type0>& array0, type_tag<Type1> typeTag);
  

    // The next function is commented out because some environments use
    // #define directives for min() and max(), which don't obey
    // namespaces, and makes the code not compile if we define our own
    // min/max.
    // 
    // 
    // /** 
    // * This function is an alias for the function minimum(const
    // * Array1D&) below.
    // * 
    // * @param array0 See documentation for minimum(const Array1D&).
    // *
    // * @return See documentation for minimum(const Array1D&).
    // */
    // template <class Type>
    // inline Type
    // min(const Array1D<Type>& array0) {return minimum(array0);}

    template <class Type>
    inline Type
    minimum(const Array1D<Type>& array0);

    template <class Type, class Functor>
    Type
    minimum(const Array1D<Type>& array0, Functor comparator);


    template <class FUNCTOR>
    double newtonRaphson(double startPoint, FUNCTOR objectiveFunction,
                         double epsilon, size_t maxIterations);
  
    template <class Type>
    inline double
    normalizedCorrelation(const Array1D<Type>& signal0,
                          const Array1D<Type>& signal1);

    
    template <class Type, class Type2>
    Type2
    normalizedCorrelation(const Array1D<Type>& signal0,
                          const Array1D<Type>& signal1,
                          type_tag<Type2> typeTag);

    template <class Type>
    inline Array1D<Type>
    ones(int size, type_tag<Type> typeTag);

    template <class Type>
    inline Array2D<Type>
    ones(int rows, int columns, type_tag<Type> typeTag);

    template <class Type>
    inline Array2D<typename NumericTraits<Type>::ProductType>
    outerProduct(const Array1D<Type>& array0, const Array1D<Type>& array1);

    template <class Type0, class Type1, class Type2>
    Array2D<Type2>
    outerProduct(const Array1D<Type0>& array0, const Array1D<Type1>& array1,
                 type_tag<Type2> typeTag);

    template <class Type>
    Array1D<Type>
    range(Type start, Type stop, Type stride=1);

    template <class Type>
    inline Array1D<Type>
    ravel(Array2D<Type>& inputArray) {return inputArray.ravel();}

    template <class Type>
    inline const Array1D<Type>
    ravel(const Array2D<Type>& inputArray) {return inputArray.ravel();}


    template <class Type>
    inline Type
    rms(const Array1D<Type>& array0);


    template <class Type0, class Type1>
    Type1
    rms(const Array1D<Type0>& array0, type_tag<Type1> typeTag);


    template <class Type>
    Array2D<Type>
    rowIndices(size_t rows, size_t columns, type_tag<Type> typeTag);


    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array1D<Type0>& array0, const Array1D<Type1>& array1);


    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array2D<Type0>& array0, const Array2D<Type1>& array1);


    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array3D<Type0>& array0, const Array3D<Type1>& array1);


    template <class Type>
    inline Array2D<Type>
    skewSymmetric(const Array1D<Type>& vector0);


    template <class Type>
    void
    solveQuadratic(Type c0, Type c1, Type c2,
                   Type& root0, Type& root1, bool& valid,
                   bool checkValidity=true);

  
    template <class Type>
    inline double
    standardDeviation(const Array1D<Type>& array0);

    template <class Type0, class Type1>
    inline Type1
    standardDeviation(const Array1D<Type0>& array0, type_tag<Type1> typeTag);
  

    template <class Type>
    inline typename NumericTraits<Type>::SumType
    sum(const Array1D<Type>& array0);

    
    template <class Type, class Type2>
    Type2
    sum(const Array1D<Type>& array0, type_tag<Type2> typeTag);


    template <class Type>
    inline typename NumericTraits<Type>::SumType
    sum(const Array2D<Type>& array0,
        const Index2D& upperLeftCorner,
        const Index2D& lowerRightCorner);

    
    template <class Type, class Type2>
    Type2
    sum(const Array2D<Type>& array0,
        const Index2D& upperLeftCorner,
        const Index2D& lowerRightCorner,
        type_tag<Type2> typeTag);


    template <class Type>
    inline double
    variance(const Array1D<Type>& array0);

    template <class Type0, class Type1>
    inline Type1
    variance(const Array1D<Type0>& array0, type_tag<Type1> typeTag);

    template <class Type>
    inline Array1D<Type>
    zeros(size_t size, type_tag<Type> typeTag);

    template <class Type>
    inline Array2D<Type>
    zeros(size_t rows, size_t columns, type_tag<Type> typeTag);

    template <class Type>
    inline Array3D<Type>
    zeros(size_t shape0, size_t shape1, size_t shape2, type_tag<Type> typeTag);

  } // namespace numeric

} // namespace dlr


/* ======= Declarations to maintain compatibility with legacy code. ======= */

namespace dlr {

  using numeric::abs;
  using numeric::abs;
  using numeric::allFalse;
  using numeric::allTrue;
  using numeric::anyFalse;
  using numeric::anyTrue;
  using numeric::argmax;
  using numeric::argmin;
  using numeric::argsort;
  using numeric::axisMax;
  using numeric::axisMaximum;
  using numeric::axisMin;
  using numeric::axisMinimum;
  using numeric::axisSum;
  using numeric::columnIndices;
  using numeric::compress;
  using numeric::count;
  using numeric::cross;
  using numeric::dot;
  using numeric::equivalentMatrix;
  using numeric::getMeanAndCovariance;
  using numeric::identity;
  using numeric::ln;
  using numeric::logicalNot;
  using numeric::magnitude;
  using numeric::magnitudeSquared;
  using numeric::matrixMultiply;
  using numeric::maximum;
  using numeric::mean;
  using numeric::minimum;
  using numeric::newtonRaphson;
  using numeric::normalizedCorrelation;
  using numeric::ones;
  using numeric::outerProduct;
  using numeric::range;
  using numeric::ravel;
  using numeric::rms;
  using numeric::rowIndices;
  using numeric::shapeMatch;
  using numeric::skewSymmetric;
  using numeric::solveQuadratic;
  using numeric::standardDeviation;
  using numeric::sum;
  using numeric::variance;
  using numeric::zeros;

} // namespace dlr


/* ================ Definitions follow ================ */

#include <cmath>
#include <sstream>
#include <numeric>
//#include <algorithm>

namespace dlr {

  namespace numeric {
    
    namespace privateCode {

      template <class Type>
      struct absFunctor : public std::unary_function<Type, Type> {
        inline Type operator()(const Type& input) {
          DLR_THROW3(NotImplementedException,
                     "absFunctor<Type>::operator()(const Type&)",
                     "absFunctor must be specialized for each type.");
          return static_cast<Type>(0);
        }
      };

      template <>
      struct absFunctor<long double>
        : public std::unary_function<long double, long double> {
        inline double operator()(const long double& input) {
          // fabsl doesn't appear to be commonly available.
          // return std::fabsl(input);
          return (input > 0.0) ? input : -input;
        }
      };


      template <>
      struct absFunctor<double> : public std::unary_function<double, double> {
        inline double operator()(const double& input) {
          return std::fabs(input);
        }
      };


      template <>
      struct absFunctor<float> : public std::unary_function<float, float> {
        inline float operator()(const float& input) {
          // Strange.  fabsf shows up in the global namespace.  Is this
          // a bug in the g++ 3.3 stand library?
          return fabsf(input);
        }
      };


      template <>
      struct absFunctor<long int>
        : public std::unary_function<long int, long int> {
        inline long int operator()(const long int& input) {
          return std::labs(input);
        }
      };


      template <>
      struct absFunctor<long long int>
        : public std::unary_function<long long int, long long int> {
        inline long long int operator()(const long long int& input) {
          // Many compilers don't support C99.
          // return std::llabs(input);
          return input >= 0LL ? input : -input;
        }
      };


      template <>
      struct absFunctor<int> : public std::unary_function<int, int> {
        inline int operator()(const int& input) {
          return std::abs(input);
        }
      };


    }
    // This function returns an array of the same size and element type
    // as its input argument, in which each element is set to the
    // absolute value of the corresponding element of the input array.
    template <class Type>
    Array1D<Type>
    abs(const Array1D<Type>& array0)
    {
      Array1D<Type> result(array0.size());
      std::transform(array0.begin(), array0.end(), result.begin(),
                     privateCode::absFunctor<Type>());
      return result;
    }

    // This function returns an array of the same size and element type
    // as its input argument, in which each element is set to the
    // absolute value of the corresponding element of the input array.
    template <class Type>
    Array2D<Type>
    abs(const Array2D<Type>& array0)
    {
      Array2D<Type> result(array0.rows(), array0.columns());
      std::transform(array0.begin(), array0.end(), result.begin(),
                     privateCode::absFunctor<Type>());
      return result;
    }


    // This function returns true if each element of its argument is
    // false, and returns false otherwise.
    template <class Type>
    bool
    allFalse(const Array1D<Type>& array0)
    {
      for(typename Array1D<Type>::const_iterator iter = array0.begin();
          iter != array0.end();
          ++iter) {
        if(*iter) {
          return false;
        }
      }
      return true;
    }

  
    // This function returns true if each element of its argument is
    // true, and returns false otherwise.
    template <class Type>
    bool
    allTrue(const Array1D<Type>& array0)
    {
      for(typename Array1D<Type>::const_iterator iter = array0.begin();
          iter != array0.end();
          ++iter) {
        if(!(*iter)) {
          return false;
        }
      }
      return true;
    }

  
    // This function returns true if any element of its argument is
    // false, and returns false otherwise.
    template <class Type>
    inline bool
    anyFalse(const Array1D<Type>& array0)
    {
      return !allTrue(array0);
    }

  
    // This function returns true if any element of its argument is
    // true, and returns false otherwise.
    template <class Type>
    inline bool
    anyTrue(const Array1D<Type>& array0)
    {
      return !allFalse(array0);
    }

  
    // This function returns the index of the largest element of its input
    // array.
    template <class Type>
    inline size_t
    argmax(const Array1D<Type>& array0)
    {
      return argmax(array0, std::less<Type>());
    }


    // This function returns the index of the largest element of its input
    // sequence.
    template <class IterType>
    inline size_t
    argmax(IterType beginIter, IterType endIter)
    {
      return argmax(beginIter, endIter, std::less<IterType>());
    }


    // This function returns the index of the largest element of its
    // input array, where largeness is defined by the second argument.
    template <class Type, class Functor>
    inline size_t
    argmax(const Array1D<Type>& array0, Functor comparator)
    {
      return argmax(array0.begin(), array0.end(), comparator);
    }
  

    // This function returns the index of the largest element of its
    // input sequence, where largeness is defined by the second argument.
    template <class IterType, class Functor>
    inline size_t
    argmax(IterType beginIter, IterType endIter, Functor comparator)
    {
      return static_cast<size_t>(
        std::max_element(beginIter, endIter, comparator) - beginIter);
    }


    // This function returns an Index2D instance indicating which is
    // the largest element of its input array.
    template <class Type>
    inline Index2D
    argmax2D(const Array2D<Type>& array0)
    {
      return argmax2D(array0, std::less<Type>());
    }


    // This function returns an Index2D instance indicating which is
    // the largest element of its input array, where largeness is
    // defined by the second argument.
    template <class Type, class Functor>
    inline Index2D
    argmax2D(const Array2D<Type>& array0, Functor comparator)
    {
      size_t ravelIndex = argmax(array0.ravel(), comparator);
      size_t row = ravelIndex / array0.columns(); // Int division.
      size_t column = ravelIndex - row * array0.columns();
      return Index2D(row, column);
    }
  

    // This function returns the index of the smallest element of its input
    // array.
    template <class Type>
    inline size_t
    argmin(const Array1D<Type>& array0)
    {
      return argmin(array0, std::less<Type>());
    }


    // This function returns the index of the smallest element of its
    // input array, where largeness is defined by the second argument.
    template <class Type, class Functor>
    inline size_t
    argmin(const Array1D<Type>& array0, Functor comparator)
    {
      const Type* minPtr = std::min_element(array0.begin(), array0.end(),
                                            comparator);
      return static_cast<size_t>(minPtr - array0.begin());
    }


    // This function returns an Index2D instance indicating which is
    // the smallest element of its input array.
    template <class Type>
    inline Index2D
    argmin2D(const Array2D<Type>& array0)
    {
      return argmin2D(array0, std::less<Type>());
    }


    // This function returns an Index2D instance indicating which is
    // the smallest element of its input array, where smallness is
    // defined by the second argument.
    template <class Type, class Functor>
    inline Index2D
    argmin2D(const Array2D<Type>& array0, Functor comparator)
    {
      size_t ravelIndex = argmin(array0.ravel(), comparator);
      size_t row = ravelIndex / array0.columns(); // Int division.
      size_t column = ravelIndex - row * array0.columns();
      return Index2D(row, column);
    }


    // This function returns an array of indices, result, so that the
    // sequence (array0[result[0]], array0[result[1]],
    // array0[result[2]], ...) is sorted from smallest to largest using
    // operator<().
    template <class Type>
    Array1D<size_t>
    argsort(const Array1D<Type>& array0)
    {
      Array1D< std::pair<Type, size_t> > sortVector(array0.size());
      Array1D<size_t> resultVector(array0.size());

      for(size_t index0 = 0; index0 < array0.size(); ++index0) {
        sortVector[index0] = std::pair<Type, size_t>(array0[index0], index0);
      }
      std::sort(sortVector.begin(), sortVector.end());
      std::transform(sortVector.begin(), sortVector.end(), resultVector.begin(),
                     ExtractSecondFunctor<Type, size_t>());
      return resultVector;    
    }

  
//   // This function returns an array of indices, result, so that the
//   // sequence (array0[result[0]], array0[result[1]],
//   // array0[result[2]], ...) is sorted from smallest to largest using
//   // the supplied comparison operator.
//   template <class Type, class Functor>
//   Array1D<size_t>
//   argsort(const Array1D<Type>& array0, Functor comparator)
//   {
//     Array1D< std::pair<Type, size_t> > sortVector(array0.size());
//     Array1D<size_t> resultVector(array0.size());

//     for(size_t index0 = 0; index0 < array0.size(); ++index0) {
//       sortVector[index0] = std::pair<Type, size_t>(array0[index0], index0);
//     }
//     std::sort(sortVector.begin(), sortVector.end(), comparator);
//     std::transform(sortVector.begin(), sortVector.end(), resultVector.begin(),
//                    ExtractSecondFunctor<Type, size_t>());
//     return resultVector;    
//   }

  
    // This function returns an Array1D in which each element has the
    // value of the largest element in one row or column of the input
    // Array2D.
    template <class Type>
    inline Array1D<Type>
    axisMaximum(const Array2D<Type>& array0, size_t axis)
    {
      return axisMaximum(array0, axis, std::less<Type>());
    }


    // This function returns an Array1D in which each element has the
    // value of the largest element in one row or column of the input
    // Array2D, where largeness is defined by the third argument.
    template <class Type, class Functor>
    Array1D<Type>
    axisMaximum(const Array2D<Type>& array0, size_t axis, Functor comparator)
    {
      Array1D<Type> result;
      switch(axis) {
      case 0:
        result.reinit(array0.columns());
        for(size_t column = 0; column < array0.columns(); ++column) {
          const Type* dataPtr = array0.data(0, column);
          Type columnMax = *dataPtr;
          size_t stride = array0.columns();
          for(size_t row = 0; row < array0.rows(); ++row) {
            if(!comparator(*dataPtr, columnMax)) {
              columnMax = *dataPtr;
            }
            dataPtr += stride;
          }
          result(column) = columnMax;
        }
        break;
      case 1:
        result.reinit(array0.rows());
        for(size_t row = 0; row < array0.rows(); ++row) {
          const Type* dataPtr = array0.data(row, 0);
          Type rowMax = *dataPtr;
          for(size_t column = 0; column < array0.columns(); ++column) {
            if(!comparator(*dataPtr, rowMax)) {
              rowMax = *dataPtr;
            }
            ++dataPtr;
          }
          result(row) = rowMax;
        }
        break;
      default:
        std::ostringstream message;
        message << "Axis " << axis << " is invalid for an Array2D.";
        DLR_THROW3(IndexException, "axisMaximum(const Array2D&, size_t, ...)",
                   message.str().c_str());
        break;
      }
      return result;  
    }


    // This function returns an Array1D in which each element has the
    // value of the smallest element in one row or column of the input
    // Array2D.
    template <class Type>
    inline Array1D<Type>
    axisMinimum(const Array2D<Type>& array0, size_t axis)
    {
      return axisMinimum(array0, axis, std::less<Type>());
    }


    // This function returns an Array1D in which each element has the
    // value of the smallest element in one row or column of the input
    // Array2D, where smallness is defined by the third argument.
    template <class Type, class Functor>
    Array1D<Type>
    axisMinimum(const Array2D<Type>& array0, size_t axis, Functor comparator)
    {
      Array1D<Type> result;
      switch(axis) {
      case 0:
        result.reinit(array0.columns());
        for(size_t column = 0; column < array0.columns(); ++column) {
          const Type* dataPtr = array0.data(0, column);
          Type columnMax = *dataPtr;
          size_t stride = array0.columns();
          for(size_t row = 0; row < array0.rows(); ++row) {
            if(comparator(*dataPtr, columnMax)) {
              columnMax = *dataPtr;
            }
            dataPtr += stride;
          }
          result(column) = columnMax;
        }
        break;
      case 1:
        result.reinit(array0.rows());
        for(size_t row = 0; row < array0.rows(); ++row) {
          const Type* dataPtr = array0.data(row, 0);
          Type rowMax = *dataPtr;
          for(size_t column = 0; column < array0.columns(); ++column) {
            if(comparator(*dataPtr, rowMax)) {
              rowMax = *dataPtr;
            }
            ++dataPtr;
          }
          result(row) = rowMax;
        }
        break;
      default:
        std::ostringstream message;
        message << "Axis " << axis << " is invalid for an Array2D.";
        DLR_THROW3(IndexException, "axisMinimum(const Array2D&, size_t, ...)",
                   message.str().c_str());
        break;
      }
      return result;  
    }


    // This function returns an Array1D in which each element has the
    // sum of one row or column of the input Array2D.
    template <class Type>
    inline Array1D<typename NumericTraits<Type>::SumType>
    axisSum(const Array2D<Type>& array0, size_t axis)
    {
      return axisSum(array0, axis,
                     type_tag<typename NumericTraits<Type>::SumType>());
    }

    // This function returns an Array1D in which each element has the
    // sum of one row or column of the input Array2D.
    template <class Type, class ResultType>
    inline Array1D<ResultType>
    axisSum(const Array2D<Type>& array0, size_t axis, type_tag<ResultType>)
    {
      return axisSum(array0, axis, type_tag<ResultType>(),
                     static_cast<ResultType>(0), std::plus<ResultType>());
    }

    // This function returns an Array1D in which each element has the
    // sum of one row or column of the input Array2D.
    template <class Type, class ResultType, class Functor>
    Array1D<ResultType>
    axisSum(const Array2D<Type>& array0, size_t axis, type_tag<ResultType>,
            const ResultType& initialValue, Functor adder)
    {
      Array1D<ResultType> result;
      switch(axis) {
      case 0:
        result.reinit(array0.columns());
        for(size_t column = 0; column < array0.columns(); ++column) {
          ResultType columnSum = initialValue;
          const Type* dataPtr = array0.data(0, column);
          size_t stride = array0.columns();
          for(size_t row = 0; row < array0.rows(); ++row) {
            columnSum = adder(columnSum, *dataPtr);
            dataPtr += stride;
          }
          result(column) = columnSum;
        }
        break;
      case 1:
        result.reinit(array0.rows());
        for(size_t row = 0; row < array0.rows(); ++row) {
          ResultType rowSum = initialValue;
          const Type* dataPtr = array0.data(row, 0);
          for(size_t column = 0; column < array0.columns(); ++column) {
            rowSum = adder(rowSum, *dataPtr);
            ++dataPtr;
          }
          result(row) = rowSum;
        }
        break;
      default:
        std::ostringstream message;
        message << "Axis " << axis << " is invalid for an Array2D.";
        DLR_THROW3(IndexException, "axisSum(const Array2D&, size_t, ...)",
                   message.str().c_str());
        break;
      }
      return result;  
    }

    // columnIndices(rows, columns): Returns an Array2D in which each
    // element contains the index of its column.
    template <class Type>
    Array2D<Type>
    columnIndices(size_t rows, size_t columns, type_tag<Type>)
    {
      Array2D<Type> returnValue(rows, columns);
      Array1D<Type> modelRow = range(static_cast<Type>(0),
                                     static_cast<Type>(columns),
                                     static_cast<Type>(1));
      for(size_t row=0; row < rows; ++row) {
        std::copy(modelRow.begin(), modelRow.end(), returnValue.data(row, 0));
      }
      return returnValue;
    }

    // This function selects those elements of an input Array1D which
    // correspond to "true" values of a mask Array1D, and returns an
    // Array1D containing only those elements.
    template <class Type0, class Type1>
    inline Array1D<Type1>
    compress(const Array1D<Type0>& condition,
             const Array1D<Type1>& input)
    {
      size_t numTrue = count(condition);
      return compress(condition, input, numTrue);
    }

    // This function behaves in exactly the same way as compress(const
    // Array1D&, const Array1D&), above, but it permits the user to
    // specify the number of true elements in the condition array.
    template <class Type0, class Type1>
    Array1D<Type1>
    compress(const Array1D<Type0>& condition,
             const Array1D<Type1>& input,
             size_t numTrue)
    {
      if(condition.size() != input.size()) {
        std::ostringstream message;
        message << "Condition and input arguments must have the same "
                << "size, but condition has size = " << condition.size()
                << ", while input has size = " << input.size() << ".";
        DLR_THROW3(ValueException,
                   "compress(const Array1D&, const Array1D&, size_t)",
                   message.str().c_str());
      }
      Array1D<Type1> result(numTrue);
      // copy_mask(input.begin(), input.end(), condition.begin(),
      //           result.begin());
      //
      // Hmm... copy_mask was previously defined in dlrLib3 as follows:
      //
      // template <class In0, class In1, class Out>
      // Out copy_mask(In0 first, In0 last, In1 mask, Out res)
      // {
      //   while(first != last) {
      //     if(*mask) {
      //       *res++ = *first;
      //     }
      //     ++mask;
      //     ++first;
      //   }
      //   return res;
      // }

      typename Array1D<Type1>::const_iterator first = input.begin();
      typename Array1D<Type1>::const_iterator last = input.end();
      typename Array1D<Type0>::const_iterator mask = condition.begin();
      typename Array1D<Type1>::iterator target = result.begin();
      while(first != last) {
        if(*mask) {
          *(target++) = *first;
        }
        ++mask;
        ++first;
      }

      return result;
    }

    // This element counts the number of elements of the input array
    // which evaluate to true, and returns that number.
    template <class Type>
    inline size_t
    count(const Array1D<Type>& x)
    {
      return std::count_if(x.begin(), x.end(), StaticCastFunctor<Type, bool>());
    }

    // This function computes the cross product of two Vector3D
    // instances.
    inline Vector3D
    cross(const Vector3D& vector0, const Vector3D& vector1) {
      return Vector3D((vector0.y() * vector1.z()) - (vector0.z() * vector1.y()),
                      (vector0.z() * vector1.x()) - (vector0.x() * vector1.z()),
                      (vector0.x() * vector1.y()) - (vector0.y() * vector1.x()));
    }

    // This function computes the inner product of two input arrays.
    // The computation is done using the ProductType specified by
    // the appropriate NumericTraits class.
    template <class Type>
    inline typename NumericTraits<Type>::ProductType
    dot(const Array1D<Type>& x, const Array1D<Type>& y)
    {
      return dot(x, y, type_tag<typename NumericTraits<Type>::ProductType>());
    }

    // This function computes the inner product of two input arrays, and
    // allows the user to control which type is used to do the
    // calculation.
    template <class Type0, class Type1, class Type2>
    inline Type2
    dot(const Array1D<Type0>& x, const Array1D<Type1>& y, type_tag<Type2>)
    {
      if(x.size() != y.size()) {
        std::ostringstream message;
        message << "Input arguments must have matching sizes, but x.size() == "
                << x.size() << " and y.size() == " << y.size() << "."
                << std::endl;
        DLR_THROW3(ValueException, "dot()", message.str().c_str());
      }
      return std::inner_product(x.begin(), x.end(), y.begin(),
                                static_cast<Type2>(0));
    }

    // This function computes the inner product of two Vector2D instances.
    inline double
    dot(const Vector2D& vector0, const Vector2D& vector1)
    {
      return vector0.x() * vector1.x() + vector0.y() * vector1.y();
    }

    // This function computes the inner product of two Vector3D instances.
    inline double
    dot(const Vector3D& vector0, const Vector3D& vector1)
    {
      return (vector0.x() * vector1.x() + vector0.y() * vector1.y()
              + vector0.z() * vector1.z());
    }

    // This function computes the matrix X such that A * x = X * vec(A).
    template <class Type>
    Array2D<Type>
    equivalentMatrix(const Array1D<Type>& vector0, size_t rowsInMatrix)
    {
      Array2D<Type> XMatrix = zeros(rowsInMatrix, vector0.size() * rowsInMatrix,
                                    type_tag<Type>());
      for(size_t XRow = 0; XRow != rowsInMatrix; ++XRow) {
        for(size_t index0 = 0; index0 < vector0.length(); ++index0) {
          XMatrix(XRow, index0 * rowsInMatrix + XRow) = vector0(index0);
        }
      }
      return XMatrix;
    }
  
    // This function returns a 2D matrix with the specified shape in
    // which the elements on the diagonal are set to 1, and all other
    // elements are set to 0.
    template <class Type>
    Array2D<Type>
    identity(size_t rows, size_t columns, type_tag<Type>)
    {
      Array2D<Type> returnArray = zeros(rows, columns, type_tag<Type>());
      size_t rank = std::min(rows, columns);
      for(size_t index = 0; index < rank; ++index) {
        returnArray(index, index) = static_cast<Type>(1);
      } 
      return returnArray;
    }


    // This function returns an array in which each element is the
    // natural logarithm of the corresponding element of its input.
    template <class Type>
    Array1D<Type>
    ln(const Array1D<Type>& array0)
    {
      Array1D<Type> result(array0.size());
      // std::transform(array0.begin(), array0.end(), std::log);
      for(size_t index0 = 0; index0 < array0.size(); ++index0) {
        result[index0] = std::log(array0[index0]);      
      }
      return result;
    }


//   // Strange troubles compiling these next two functions.
//
//   // This function returns an array in which each element is the
//   // natural logarithm of the corresponding element of its input.
//   template <>
//   Array1D<float>
//   ln(const Array1D<float>& array0)
//   {
//     Array1D<float> result(array0.size());
//     std::transform(array0.begin(), array0.end(), std::logf);
//     return result;
//   }


//   // This function returns an array in which each element is the
//   // natural logarithm of the corresponding element of its input.
//   template <>
//   Array1D<long double>
//   ln(const Array1D<long double>& array0)
//   {
//     Array1D<long double> result(array0.size());
//     std::transform(array0.begin(), array0.end(), std::logl);
//     return result;
//   }


    // This function returns an array in which each element is the
    // natural logarithm of the corresponding element of its input.
    template <class Type>
    Array2D<Type>
    ln(const Array2D<Type>& array0)
    {
      Array2D<Type> result(array0.size());
      // std::transform(array0.begin(), array0.end(), std::log);
      for(size_t index0 = 0; index0 < array0.size(); ++index0) {
        result[index0] = std::log(array0[index0]);      
      }
      return result;
    }


    // This function returns an Array2D instance in which the value of
    // each element is the logical not of the corresponding element of
    // the input array.
    template <class Type>
    Array2D<Type>
    logicalNot(const Array2D<Type>& array0)
    {
      Array2D<Type> result(array0.rows(), array0.columns());
      std::transform(array0.begin(), array0.end(), result.begin(),
                     unaryComposeFunctor(StaticCastFunctor<bool, Type>(),
                                         std::logical_not<Type>()));
      return result;
    }

  
    // This function computes the magnitude of its input argument.
    template <class Type>
    inline Type
    magnitude(const Array1D<Type>& array0)
    {
      return static_cast<Type>(std::sqrt(magnitudeSquared(array0)));
    }

  
    // This function computes the magnitude of its input argument.
    inline double
    magnitude(const Vector2D& vector0) {
      return std::sqrt(magnitudeSquared(vector0));
    }

  
    // This function computes the magnitude of its input argument.
    inline double
    magnitude(const Vector3D& vector0) {
      return std::sqrt(magnitudeSquared(vector0));
    }


    // This function computes the square of the magnitude of its input
    // argument.
    template <class Type>
    inline typename NumericTraits<Type>::ProductType
    magnitudeSquared(const Array1D<Type>& array0)
    {
      typedef typename NumericTraits<Type>::ProductType ProductType;
      return std::inner_product(array0.begin(), array0.end(),
                                array0.begin(), static_cast<ProductType>(0));
    }


    // This function computes the square of the magnitude of its input
    // argument.
    inline double
    magnitudeSquared(const Vector2D& vector0) {
      return (vector0.x() * vector0.x() + vector0.y() * vector0.y());
    }

  
    // This function computes the square of the magnitude of its input
    // argument.
    inline double
    magnitudeSquared(const Vector3D& vector0) {
      return (vector0.x() * vector0.x() + vector0.y() * vector0.y()
              + vector0.z() * vector0.z());
    }

  
    // This function computes a vector * matrix product.  Assuming the
    // first argument, vector0, represents a column vector and the
    // second argument, matrix0, represents a 2D array, then this function
    // computes the product:
    //   vector0' * matrix0
    // Where the single quote indicates transpose.
    template <class Type>
    inline Array1D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array1D<Type>& vector0, const Array2D<Type>& matrix0)
    {
      return matrixMultiply(
        vector0, matrix0, type_tag<typename NumericTraits<Type>::ProductType>());
    }

    // This function computes a vector * matrix product just like
    // matrixMultiply(const Array1D<Type>&, const Array2D<Type>&), above.
    // This function differs in that the element type of the return
    // value is set explicitly using a third argument.
    template <class Type0, class Type1, class Type2>
    Array1D<Type2>
    matrixMultiply(const Array1D<Type0>& vector0, const Array2D<Type0>& matrix0,
                   type_tag<Type2>)
    {
      if(vector0.size() != matrix0.rows()) {
        std::ostringstream message;
        message << "Can't left-multiply a "
                << matrix0.rows() << " x " << matrix0.columns()
                << " matrix by a " << vector0.size() << " element vector\n";
        DLR_THROW(ValueException, "matrixMultiply()", message.str().c_str());
      }
      Array1D<Type2> result = zeros(matrix0.columns(), type_tag<Type2>());
      size_t index = 0;
      for(size_t row = 0; row < matrix0.rows(); ++row) {
        for(size_t column = 0; column < matrix0.columns(); ++column) {
          result[column] += (static_cast<Type2>(vector0[row])
                             * static_cast<Type2>(matrix0[index]));
          ++index;
        }
      }
      return result;
    }

    // This function computes a matrix * vector product.  Assuming the
    // first argument, matrix0, represents a matrix, and the and the
    // second argument, vector0, represents a column vector, then this
    // function computes the product:
    //  matrix0 * vector0
    template <class Type>
    inline Array1D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array2D<Type>& matrix0, const Array1D<Type>& vector0)
    {
      return matrixMultiply(
        matrix0, vector0, type_tag<typename NumericTraits<Type>::ProductType>());
    }


    // This function computes a matrix * vector product just like
    // matrixMultiply(const Array2D<Type>&, const Array1D<Type>&), above.
    // This function differs in that the element type of the return
    // value is set explicitly using a third argument.
    template <class Type0, class Type1, class Type2>
    Array1D<Type2>
    matrixMultiply(const Array2D<Type0>& matrix0, const Array1D<Type1>& vector0,
                   type_tag<Type2>)
    {
      if(vector0.size() != matrix0.columns()) {
        std::ostringstream message;
        message << "matrixMultiply() -- can't right-multiply a "
                << matrix0.rows() << " x " << matrix0.columns()
                << " matrix by a " << vector0.size() << " element vector\n";
        DLR_THROW(ValueException, "matrixMultiply()", message.str().c_str());
      }
      Array1D<Type2> result(matrix0.rows());
      for(size_t row = 0; row < matrix0.rows(); ++row) {
        result[row] = dot(vector0, matrix0.row(row), type_tag<Type2>());
      }
      return result;
    }

    // This function computes a matrix * matrix product.  That is,
    // the elements of the resulting array are the dot products of the
    // rows of the first argument with the columns of the second argument.
    template <class Type>
    inline Array2D<typename NumericTraits<Type>::ProductType>
    matrixMultiply(const Array2D<Type>& matrix0, const Array2D<Type>& matrix1)
    {
      return matrixMultiply(
        matrix0, matrix1, type_tag<typename NumericTraits<Type>::ProductType>());
    }
    
    // This function computes a matrix * matrix product, just like
    // matrixMultiply(const Array2D<Type>&, const Array2D<Type>&), above.
    // This function differs in that the element type of the return
    // value is set explicitly using a third argument.
    template <class Type0, class Type1, class Type2>
    Array2D<Type2>
    matrixMultiply(const Array2D<Type0>& matrix0, const Array2D<Type1>& matrix1,
                   type_tag<Type2>)
    {
      if(matrix1.rows() != matrix0.columns()) {
        std::ostringstream message;
        message << "matrixMultiply() -- can't left-multiply a "
                << matrix1.rows() << " x " << matrix1.columns()
                << " matrix by a "
                << matrix0.rows() << " x " << matrix0.columns()
                << " matrix\n";
        DLR_THROW(ValueException, "matrixMultiply()", message.str().c_str());
      }
      Array2D<Type2> result = zeros(matrix0.rows(), matrix1.columns(),
                                    type_tag<Type2>());
      for(size_t resultRow = 0; resultRow < result.rows(); ++resultRow) {
        for(size_t resultColumn = 0; resultColumn < result.columns();
            ++resultColumn) {
          for(size_t index = 0; index < matrix0.columns(); ++index) {
            result(resultRow, resultColumn) +=
              (static_cast<Type2>(matrix0(resultRow, index))
               * static_cast<Type2>(matrix1(index, resultColumn)));
          }
        }
      }
      return result;
    }

    // This function returns a copy of the largest element in the input
    // Array1D instance.
    template <class Type>
    inline Type
    maximum(const Array1D<Type>& array0)
    {
      return maximum(array0, std::less<Type>());
    }

    // This function returns a copy of the largest element in the input
    // Array1D instance, where largeness is defined by the return value
    // of the second argument.
    template <class Type, class Functor>
    Type
    maximum(const Array1D<Type>& array0, Functor comparator)
    {
      if(array0.size() == 0) {
        DLR_THROW3(ValueException, "maximum()",
                   "Can't find the maximum element of an empty array.");
      }
      return *std::max_element(array0.begin(), array0.end(), comparator);
    }


    // This function computes the average value, or geometric mean, of
    // the elements of input sequence.
    template <class Iterator, class Type>
    Type
    mean(const Iterator& beginIter, const Iterator& endIter)
    {
      Type meanValue;
      size_t count = 0;
      while(beginIter != endIter) {
        meanValue += *beginIter;
        ++count;
        ++beginIter;
      }
      return meanValue / count;
    }

    
    // This function computes the average value, or geometric mean, of
    // the elements of its argument.
    template <class Type>
    inline double
    mean(const Array1D<Type>& array0)
    {
      if(array0.size() == 0) {
        DLR_THROW3(ValueException, "mean()",
                   "Can't find the mean of an empty array.");
      }
      NumericTypeConversionFunctor<typename NumericTraits<Type>::SumType,
        Type> functor;
      return functor(
        mean(array0, type_tag<typename NumericTraits<Type>::SumType>()));
    }
  

    // This function computes the average value, or geometric mean, of
    // the elements of its argument, and allows the user to specify the
    // precision with which the computation is carried out.
    template <class Type0, class Type1>
    inline Type1
    mean(const Array1D<Type0>& array0, type_tag<Type1>)
    {
      return sum(array0, type_tag<Type1>()) / static_cast<Type1>(array0.size());
    }
  

    // This function estimates the mean and covariance of a set of
    // vectors, which are represented by the rows (or columns) of the
    // input 2D array.
    template <class Type>
    void
    getMeanAndCovariance(const Array2D<Type>& sampleArray,
                         Array1D<double>& meanArray,
                         Array2D<double>& covarianceArray,
                         size_t majorAxis)
    {
      // Check input.
      if(sampleArray.size() == 0) {
        meanArray.reinit(0);
        covarianceArray.reinit(0, 0);
        return;
      }

      // Compute number of samples and sample dimensionality.
      size_t dimensionality;
      size_t numberOfSamples;
      if(majorAxis == 0) {
        dimensionality = sampleArray.columns();
        numberOfSamples = sampleArray.rows();
      } else {
        dimensionality = sampleArray.rows();
        numberOfSamples = sampleArray.columns();
      }
      
      // Now compute the mean value of the sample points.
      // For efficiency, we simultaneously compute covariance.
      // We make use of the identity:
      // 
      //   covariance = E[(x - u)*(x - u)^T] = E[x * x^T] - u * u^T
      // 
      // where E[.] denotes expected value and u is the mean.
      //
      // Note that the sample mean is an unbiased estimator for u, but
      // substituting the sample mean for u in the covariance equation
      // gives a biased estimator of covariance.  We resolve this by
      // using the unbiased estimator:
      //
      //   covariance = (1/(N-1))sum(x * x^T) - (N/(N-1)) * uHat * uHat^T
      //
      // where uHat is the sample mean.

      meanArray.reinit(dimensionality);
      meanArray = 0.0;
      covarianceArray.reinit(dimensionality, dimensionality);
      covarianceArray = 0.0;
      typename Array2D<Type>::const_iterator sampleIterator =
        sampleArray.begin();

      if(majorAxis == 0) {
        for(size_t rowIndex = 0; rowIndex < sampleArray.rows(); ++rowIndex) {
          for(size_t columnIndex = 0; columnIndex < sampleArray.columns();
              ++columnIndex) {
            // Update accumulator for mean.
            meanArray[columnIndex] += *sampleIterator;

            // Update accumulator for covariance.  We only compute the top
            // half of the covariance matrix for now, since it's symmetric.
            typename Array2D<Type>::const_iterator sampleIterator2 =
              sampleIterator;
            for(size_t covarianceIndex = columnIndex;
                covarianceIndex < dimensionality;
                ++covarianceIndex) {
              // Note(xxx): Should fix this to run faster.
              covarianceArray(columnIndex, covarianceIndex) +=
                *sampleIterator * *sampleIterator2;
              ++sampleIterator2;
            }
            ++sampleIterator;
          }
        }
      } else {
        for(size_t rowIndex = 0; rowIndex < sampleArray.rows(); ++rowIndex) {
          for(size_t columnIndex = 0; columnIndex < sampleArray.columns();
              ++columnIndex) {
            // Update accumulator for mean.
            meanArray[rowIndex] += *sampleIterator;

            // Update accumulator for covariance.  We only compute the top
            // half of the covariance matrix for now, since it's symmetric.
            typename Array2D<Type>::const_iterator sampleIterator2 =
              sampleIterator;
            for(size_t covarianceIndex = rowIndex;
                covarianceIndex < dimensionality;
                ++covarianceIndex) {
              // Note(xxx): Should fix this to run faster.
              covarianceArray(rowIndex, covarianceIndex) +=
                *sampleIterator * *sampleIterator2;
              sampleIterator2 += numberOfSamples;
            }
            ++sampleIterator;
          }
        }
      }
    
      // Finish up the computation of the mean.
      meanArray /= static_cast<double>(numberOfSamples);

      // Finish up the computation of the covariance matrix.
      for(size_t index0 = 0; index0 < dimensionality; ++index0) {
        for(size_t index1 = index0; index1 < dimensionality; ++index1) {
          // Add correction to the top half of the covariance matrix.
          covarianceArray(index0, index1) -=
            numberOfSamples * meanArray[index0] * meanArray[index1];
          covarianceArray(index0, index1) /= (numberOfSamples - 1);

          // And copy to the bottom half.
          if(index0 != index1) {
            covarianceArray(index1, index0) = covarianceArray(index0, index1);
          }
        }
      }

    }
  
                    
    // This function returns a copy of the smallest element in the input
    // Array1D instance.
    template <class Type>
    inline Type
    minimum(const Array1D<Type>& array0)
    {
      if(array0.size() == 0) {
        DLR_THROW3(ValueException, "minimum()",
                   "Can't find the minimum element of an empty array.");
      }
      return minimum(array0, std::less<Type>());
    }

    // This function returns a copy of the smallest element in the input
    // Array1D instance, where largeness is defined by the value of the
    // second argument.
    template <class Type, class Functor>
    Type
    minimum(const Array1D<Type>& array0, Functor comparator)
    {
      return *std::min_element(array0.begin(), array0.end(), comparator);
    }


    // This function uses the iterative method of Newton and Raphson to
    // search for a zero crossing of the supplied functor.
    template <class FUNCTOR>
    double newtonRaphson(double startPoint, FUNCTOR objectiveFunction,
                         double epsilon, size_t maxIterations)
    {
      double argument = startPoint;
      for(size_t count = 0; count < maxIterations; ++count) {
        double result = objectiveFunction(argument);
        if(std::fabs(result) < epsilon) {
          return argument;
        }
        double resultPrime = objectiveFunction.derivative(argument);
        if(resultPrime == 0.0) {
          DLR_THROW(ValueException, "newtonRaphson()", "Zero derivative.");
        }
        argument = argument - (result / resultPrime);
      }
      DLR_THROW(ValueException, "newtonRaphson()",
                "Root finding failed to converge.");
      return 0.0;  // keep compiler happy
    }

    // This function computes the normalized correlation of two Array1D
    // arguments.
    template <class Type>
    inline double
    normalizedCorrelation(const Array1D<Type>& signal0,
                          const Array1D<Type>& signal1)
    {
      return normalizedCorrelation(signal0, signal1, type_tag<double>());
    }

    // This function is equivalent to normalizedCorrelation(const
    // Array1D&, const Array1D&), except that the computation is carried
    // out using the type specified by the third argument.
    template <class Type, class Type2>
    Type2
    normalizedCorrelation(const Array1D<Type>& signal0,
                          const Array1D<Type>& signal1,
                          type_tag<Type2>)
    {
      if(signal0.size() != signal1.size()) {
        DLR_THROW(ValueException, "normalizedCorrelation()",
                  "Input arrays must have the same size.");
      }
      Type2 oneOverN =
        static_cast<Type2>(1) / static_cast<Type2>(signal0.size());
      Type2 sum0 = static_cast<Type2>(0);
      Type2 sum1 = static_cast<Type2>(0);
      Type2 sum00 = static_cast<Type2>(0);
      Type2 sum01 = static_cast<Type2>(0);
      Type2 sum11 = static_cast<Type2>(0);
      typedef typename Array1D<Type>::const_iterator SignalIter;
      SignalIter iter0 = signal0.begin();
      SignalIter iter1 = signal1.begin();
      SignalIter end0 = signal0.end();
      while(iter0 != end0) {
        sum0 += *iter0;
        sum1 += *iter1;
        sum00 += (*iter0) * (*iter0);
        sum01 += (*iter0) * (*iter1);
        sum11 += (*iter1) * (*iter1);
        ++iter0;
        ++iter1;
      }
      Type2 numerator = sum01 - oneOverN * sum0 * sum1;
      Type2 denominator = std::sqrt((sum00 - oneOverN * sum0 * sum0)
                                    * (sum11 - oneOverN * sum1 * sum1));
      return numerator / denominator;
    }

    // This function returns an Array1D of the specified size and type
    // in which the value of every element is initialized to 1.
    template <class Type>
    inline Array1D<Type>
    ones(int size, type_tag<Type>)
    {
      Array1D<Type> result(size);
      result = 1;
      return result;
    }

    // This function returns an Array2D of the specified size and type
    // in which the value of every element is initialized to one.
    template <class Type>
    inline Array2D<Type>
    ones(int rows, int columns, type_tag<Type>)
    {
      Array2D<Type> result(rows, columns);
      result = 1;
      return result;
    }

    // This function computes the outer product of two input Array1D
    // instances. 
    template <class Type>
    inline Array2D<typename NumericTraits<Type>::ProductType>
    outerProduct(const Array1D<Type>& x, const Array1D<Type>& y)
    {
      return outerProduct(
        x, y, type_tag<typename NumericTraits<Type>::ProductType>());
    }

    // This function computes the outer product of two input Array1D
    // instances and allows the user to control which type is used to do the
    // computation.
    template <class Type0, class Type1, class Type2>
    Array2D<Type2>
    outerProduct(const Array1D<Type0>& x, const Array1D<Type1>& y,
                 type_tag<Type2>)
    {
      Array2D<Type2> result(x.size(), y.size());
      typename Array2D<Type2>::iterator runningIterator = result.begin();
      typename Array1D<Type1>::const_iterator yBegin = y.begin();
      typename Array1D<Type1>::const_iterator yEnd = y.end();
      for(size_t index = 0; index < x.size(); ++index) {
        std::transform(yBegin, yEnd, runningIterator,
                       std::bind2nd(std::multiplies<Type2>(), x[index]));
        runningIterator += y.size();
      }
      return result;
    }

    // This function returns an Array1D in which the first element has
    // value equal to argument "start," and each subsequent element has
    // value equal to the previous element plus argument "stride."
    template <class Type>
    Array1D<Type>
    range(Type start, Type stop, Type stride)
    {
      if(stride == 0) {
        DLR_THROW3(ValueException, "range(Type, Type, Type)",
                   "Argument \"stride\" must not be equal to 0.");
      }
      int length = static_cast<int>((stop - start) / stride);
      if(stride > 0) {
        if((length * stride) < (stop - start)) {      
          ++length;      
        }
      } else {
        if((length * stride) > (stop - start)) {
          ++length;      
        }
      }
      Array1D<Type> result(length);
      for(int index = 0; index < length; ++index) {
        result(index) = start;
        start += stride;
      }
      return result;
    }
  

    // This function computes the RMS (Root Mean Square) value of the
    // elements of its argument.
    template <class Type>
    inline Type
    rms(const Array1D<Type>& array0)
    {
      NumericTypeConversionFunctor<
        typename NumericTraits<Type>::SumType, Type> functor;
      return functor(rms(array0,
                         type_tag<typename NumericTraits<Type>::ProductType>()));
    }

    // This function computes the RMS (Root Mean Square) value of the
    // elements of its argument, and allows the user to specify the
    // precision with which the computation is carried out.
    template <class Type0, class Type1>
    Type1
    rms(const Array1D<Type0>& array0, type_tag<Type1>)
    {
      Type1 accumulator = 0;
      for(int index = 0; index < array0.size(); ++index) {
        Type1 element = static_cast<Type1>(array0[index]);
        accumulator += element * element;
      }
      return ::sqrt(accumulator / static_cast<Type1>(array0.size()));
    }

    // rowIndices(rows, columns): Returns an Array2D in which each
    // element contains the index of its row.
    template <class Type>
    Array2D<Type>
    rowIndices(size_t rows, size_t columns, type_tag<Type>)
    {
      Array2D<Type> returnValue(rows, columns);
      for(size_t row=0; row < rows; ++row) {
        Type* rowPtr = returnValue.data(row, 0);
        std::fill(rowPtr, rowPtr + columns, static_cast<Type>(row));
      }
      return returnValue;
    }

    // This function returns true if the two arrays have the same shape,
    // false otherwise.
    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array1D<Type0>& array0, const Array1D<Type1>& array1)
    {
      return array0.size() == array1.size();
    }

    // This function returns true if the two arrays have the same shape,
    // false otherwise.
    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array2D<Type0>& array0, const Array2D<Type1>& array1)
    {
      return ((array0.rows() == array1.rows())
              && (array0.columns() == array1.columns()));
    }

    // This function returns true if the two arrays have the same shape,
    // false otherwise.
    template <class Type0, class Type1>
    inline bool
    shapeMatch(const Array3D<Type0>& array0, const Array3D<Type1>& array1)
    {
      return ((array0.shape0() == array1.shape0())
              && (array0.shape1() == array1.shape1())
              && (array0.shape2() == array1.shape2()));            
    }

    // skewSymmetric(x): Returns a skew symmetric matrix X such
    // that matrixMultiply(X, y) = cross(x, y).
    template <class Type>
    inline Array2D<Type>
    skewSymmetric(const Array1D<Type>& vector0)
    {
      if(vector0.size() != 3) {
        std::ostringstream message;
        message << "Argument must have exactly 3 elements, but instead has "
                << vector0.size() << "elements.";
        DLR_THROW(ValueException, "skewSymmetric()", message.str().c_str());
      }
      Array2D<Type> returnVal(3, 3);
      returnVal(0, 0) = 0;
      returnVal(0, 1) = -vector0(2);
      returnVal(0, 2) = vector0(1);
  
      returnVal(1, 0) = vector0(2);
      returnVal(1, 1) = 0;
      returnVal(1, 2) = -vector0(0);

      returnVal(2, 0) = -vector0(1);
      returnVal(2, 1) = vector0(0);
      returnVal(2, 2) = 0;
      return returnVal;
    }


    // This function computes the real roots of the quadratic polynomial
    // c0*x^2 + c1*x + c0 = 0.
    template <class Type>
    void
    solveQuadratic(Type c0, Type c1, Type c2,
                   Type& root0, Type& root1, bool& valid,
                   bool checkValidity)
    {
      Type ss = c1 * c1;
      ss -= (4.0 * c0 * c2);
      if(checkValidity) {
        valid = (ss >= 0.0);
        // s = numpy.choose(valid, (1.0, s))
        if(!valid) {
          ss = 1.0;
        }
      } else {
        valid = true;
      }
      Type rt = std::sqrt(ss);
      // sgn = numpy.greater_equal(b, 0.0)
      // rt = numpy.choose(sgn, (-rt, rt))
      if(c1 < 0.0) {
        rt *= -1;
      }
      Type qq = -0.5 * (rt + c1);
      root0 = qq / c0;
      root1 = c2 / qq;
    }

  
    // This function computes the standard deviation of a group of
    // scalar samples.
    template <class Type>
    inline double
    standardDeviation(const Array1D<Type>& array0)
    {
      return standardDeviation(array0, type_tag<double>());
    }

    // This function computes the standard deviation, of the elements of
    // its argument, and allows the user to specify the precision with
    // which the computation is carried out.
    template <class Type0, class Type1>
    inline Type1
    standardDeviation(const Array1D<Type0>& array0, type_tag<Type1>)
    {
      return ::sqrt(variance(array0, type_tag<Type1>()));
    }

    // This function computes the sum of all elements in the input
    // array.  The summation is done using the SumType specified by the
    // appropriate NumericTraits class.
    template <class Type>
    inline typename NumericTraits<Type>::SumType
    sum(const Array1D<Type>& array0)
    {
      return sum(array0, type_tag<typename NumericTraits<Type>::SumType>());
    }

    // This function computes the sum of all elements in the input
    // array, and allows the user to control which type is used to do the
    // summation.
    template <class Type, class Type2>
    Type2
    sum(const Array1D<Type>& array0, type_tag<Type2>)
    {
      return std::accumulate(array0.begin(), array0.end(),
                             static_cast<Type2>(0));
    }


    // This function computes the sum of those elements of its
    // argument which lie within a rectangular region of interest.
    template <class Type>
    inline typename NumericTraits<Type>::SumType
    sum(const Array2D<Type>& array0,
        const Index2D& upperLeftCorner,
        const Index2D& lowerRightCorner)
    {
      return sum(array0, upperLeftCorner, lowerRightCorner,
                 type_tag<typename NumericTraits<Type>::SumType>());
    }
      
    
    // This function computes the sum of those elements of its
    // argument which lie within a rectangular region of interest.
    template <class Type, class Type2>
    Type2
    sum(const Array2D<Type>& array0,
        const Index2D& upperLeftCorner,
        const Index2D& lowerRightCorner,
        type_tag<Type2> typeTag)
    {
      Type2 result = static_cast<Type2>(0);
      for(int row = upperLeftCorner.getRow();
          row < lowerRightCorner.getRow();
          ++row) {
        typename Array2D<Type>::const_iterator rowBegin = array0.rowBegin(row);
        result += std::accumulate(
          rowBegin + upperLeftCorner.getColumn(),
          rowBegin + lowerRightCorner.getColumn(),
          static_cast<Type2>(0));
      }
      return result;
    }

    
    // This function computes the standard deviation of a group of
    // scalar samples.
    template <class Type>
    inline double
    variance(const Array1D<Type>& array0)
    {
      return variance(array0, type_tag<double>());
    }

    // This function computes the standard deviation, of the elements of
    // its argument, and allows the user to specify the precision with
    // which the computation is carried out.
    template <class Type0, class Type1>
    inline Type1
    variance(const Array1D<Type0>& array0, type_tag<Type1>)
    {
      Type1 meanValue = mean(array0, type_tag<Type1>());
      Type1 accumulator = 0;
      for(size_t index0 = 0; index0 < array0.size(); ++index0) {
        Type1 offset = static_cast<Type1>(array0[index0]) - meanValue;
        accumulator += offset * offset;
      }
      return accumulator / static_cast<Type1>(array0.size());
    }

    // This function returns an Array1D of the specified size and type
    // in which the value of every element is zero.
    template <class Type>
    inline Array1D<Type>
    zeros(size_t size, type_tag<Type>)
    {
      Array1D<Type> result(size);
      result = 0;
      return result;
    }

    // This function returns an Array2D of the specified size and type
    // in which the value of every element is zero.
    template <class Type>
    inline Array2D<Type>
    zeros(size_t rows, size_t columns, type_tag<Type>)
    {
      Array2D<Type> result(rows, columns);
      result = 0;
      return result;
    }


    // This function returns an Array3D of the specified size and type
    // in which the value of every element is zero.
    template <class Type>
    inline Array3D<Type>
    zeros(size_t shape0, size_t shape1, size_t shape2, type_tag<Type>)
    {
      Array3D<Type> result(shape0, shape1, shape2);
      result = 0;
      return result;
    }
  
  } // namespace numeric

} // namespace dlr

#endif /* #ifndef _DLR_NUMERIC_UTILITIES_H_ */

---