spline_linear.inl

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/** @file
 *  @author Bram de Greve (bram@cocamware.com)
 *  @author Tom De Muer (tom@cocamware.com)
 *
 *  *** BEGIN LICENSE INFORMATION ***
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 *  The contents of this file are subject to the Common Public Attribution License 
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 *  the License. You may obtain a copy of the License at 
 *  http://lass.sourceforge.net/cpal-license. The License is based on the 
 *  Mozilla Public License Version 1.1 but Sections 14 and 15 have been added to cover 
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 *  the Original Developer. In addition, Exhibit A has been modified to be consistent 
 *  with Exhibit B.
 *  
 *  Software distributed under the License is distributed on an "AS IS" basis, WITHOUT 
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 *  language governing rights and limitations under the License.
 *  
 *  The Original Code is LASS - Library of Assembled Shared Sources.
 *  
 *  The Initial Developer of the Original Code is Bram de Greve and Tom De Muer.
 *  The Original Developer is the Initial Developer.
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 *  Copyright (C) 2004-2011 the Initial Developer.
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 */
 
 
 
#ifndef LASS_GUARDIAN_OF_INCLUSION_NUM_SPLINE_LINEAR_INL
#define LASS_GUARDIAN_OF_INCLUSION_NUM_SPLINE_LINEAR_INL
 
#include "num_common.h"
#include "spline_linear.h"
#include "../stde/extended_iterator.h"
 
namespace lass
{
namespace num
{
 
// --- public --------------------------------------------------------------------------------------
 
template <typename S, typename D, typename T>
SplineLinear<S, D, T>::SplineLinear()
{
}
 
 
 
/** construct a spline from a range of control/data pairs.
 *
 *  Each node consists of a control value and a data value.  This contstructor accepts a single 
 *  range [iFirst, iLast) of control/data pairs.  The iterator type should have two fields
 *  @c first and @c second that contain respectively the control and data values.  This is
 *  choosen so that a std::pair can be used as a representatin of the control/data pair.
 *
 *  @pre
 *  @arg [iFirst, iLast) is a valid range.
 *  @arg @c PairInputIterator has a member @c first containing the control value
 *  @arg @c PairInputIterator has a member @c second containing the data value
 *
 *  @par complexity: 
 *      O(D * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg N = number of nodes
 */
template <typename S, typename D, typename T>
template <typename PairInputIterator>
SplineLinear<S, D, T>::SplineLinear(PairInputIterator iFirst, 
                                    PairInputIterator iLast)
{
    while (iFirst != iLast)
    {
        Node node;
        node.x = iFirst->first;
        node.y = iFirst->second;
        nodes_.push_back(node);
        ++iFirst;
    }
    init();
}
 
 
 
/** construct a spline from seperate ranges.
 *
 *  Each node consists of a control value and a data value.  This contstructor accepts seperate
 *  ranges for control and data values.  The control values are given by the range 
 *  [iFirstControl , iLastControl).  Of the range of the data values, only the iterator
 *  iFirstData to the the first element is given.
 *
 *  @pre
 *  @arg [iFirstControl, iLastControl) is a valid range.
 *  @arg [iFirstData, iFirstData + (iLastControl - iFirstControl)) is a valid range.
 *
 *  @par complexity: 
 *      O(D * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg N = number of nodes
 */
template <typename S, typename D, typename T>
template <typename ScalarInputIterator, typename DataInputIterator>
SplineLinear<S, D, T>::SplineLinear(ScalarInputIterator iFirstControl, 
                                    ScalarInputIterator iLastControl,
                                    DataInputIterator iFirstData)
{
    while (iFirstControl != iLastControl)
    {
        Node node;
        node.x = *iFirstControl++;
        node.y = *iFirstData++;
        nodes_.push_back(node);
    }
    init();
}
 
 
 
/** Get the linear interpolated data value that corresponds with constrol value @a iX.
 *
 *  @pre this->isEmpty() == false
 *
 *  @par complexity: 
 *      O(D * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg N = number of nodes
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TData
SplineLinear<S, D, T>::operator ()(TScalar iX) const
{
    LASS_ASSERT(!isEmpty());
 
    const typename TNodes::const_iterator n = findNode(iX);
    const TScalar dx = iX - n->x;
 
    TData result(n->y);
    TDataTraits::multiplyAccumulate(result, n->dy, dx);
    return result;
}
 
 
 
/** Get the first derivative of data value that corresponds with constrol value @a iX.
 *
 *  As long as @a iX is exact on a node, it equals to lim_{dx->0} (*this(iX + dx) - *this(iX)) / dx.
 *  With linear splines, in theory the first derivative does not exist on the nodes.  This
 *  function however will return the first derivative on the right of the node. *  
 *
 *  @pre this->isEmpty() == false
 *
 *  @par complexity: 
 *      O(D * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg N = number of nodes
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TData
SplineLinear<S, D, T>::derivative(TScalar iX) const
{
    LASS_ASSERT(!isEmpty());
 
    return findNode(iX)->dy;
}
 
 
 
/** Get the second derivative of data value that corresponds with constrol value @a iX.
 *
 *  For a linear spline, the second derivative is always zero, except on the nodes where it
 *  does not exist.  This function however will always return zero, even on the nodes.
 *
 *  @pre this->isEmpty() == false
 *
 *  @par complexity: 
 *      O(D * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg N = number of nodes
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TData
SplineLinear<S, D, T>::derivative2(TScalar /*iX*/) const
{
    LASS_ASSERT(!isEmpty());
 
    TData result;
    TDataTraits::zero(result, dataDimension_);
    return result;
}
 
 
 
/** Get the integrated data value between control points @a iA and @a iB.
 *
 *  @pre this->isEmpty() == false
 *
 *  @par complexity: 
 *      O(D * M * log(N)) with 
 *      @arg D = a figure that indicates the complexity of operations on data values.
 *               Is most probably linear with the dimension of the data value
 *      @arg M = number of nodes between @a iA and @a iB.
 *      @arg N = total number of nodes in spline
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TData
SplineLinear<S, D, T>::integral(TScalar iBegin, TScalar iEnd) const
{
    LASS_ASSERT(!isEmpty());
 
    TNodeConstIterator first = findNode(iBegin);
    TNodeConstIterator last = findNode(iEnd);
    if (first == last)
    {
        TData result(first->y);
        TDataTraits::scale(result, iEnd - iBegin);
        TDataTraits::multiplyAccumulate(result, first->dy, 
            (num::sqr(iEnd - first->x) - num::sqr(iBegin - first->x)) / 2);
        return result;
    }
    else
    {
        TScalar multiplier = 1;
        if (iEnd < iBegin)
        {
            std::swap(iBegin, iEnd);
            std::swap(first, last);
            multiplier = -1;
        }
 
        TNodeConstIterator next = stde::next(first);
 
        TData result(first->y);
        TDataTraits::scale(result, next->x - iBegin);
        TDataTraits::multiplyAccumulate(result, first->dy, 
            (num::sqr(next->x - first->x) - num::sqr(iBegin - first->x)) / 2);
        first = next++;
 
        while (first != last)
        {
            const TScalar dx = next->x - first->x;
            TDataTraits::multiplyAccumulate(result, first->y, dx);
            TDataTraits::multiplyAccumulate(result, first->dy, num::sqr(dx) / 2);
            first = next++;
        }
 
        const TScalar dx = iEnd - first->x;
        TDataTraits::multiplyAccumulate(result, first->y, dx);
        TDataTraits::multiplyAccumulate(result, first->dy, num::sqr(dx) / 2);
 
        TDataTraits::scale(result, multiplier);
        return result;
    }
}
 
 
 
/** return true if the spline contains any nodes at all.
 *  @par complexity: 
 *      O(1)
 */
template <typename S, typename D, typename T>
bool SplineLinear<S, D, T>::isEmpty() const
{
    return nodes_.empty();
}
 
 
 
/** return the range of control values for which the spline can interpolate.
 *  @par complexity: 
 *      O(1)
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TControlRange
SplineLinear<S, D, T>::controlRange() const
{
    typedef typename SplineLinear<S, D, T>::TControlRange TRange;
    return TRange(nodes_.front().x, nodes_.back().x);
}
 
 
 
// --- private -------------------------------------------------------------------------------------
 
template <typename S, typename D, typename T>
void SplineLinear<S, D, T>::init()
{
    // are there any nodes at all?  we need at least two!
    //
    const typename TNodes::size_type size = nodes_.size();
    if (size < 2)
    {
        LASS_THROW("A linear interpolator needs at least two nodes!  This one only has '"
            << static_cast<unsigned>(nodes_.size()) << "'.");
    }
 
    typename TNodes::iterator prev = nodes_.begin();
    dataDimension_ = TDataTraits::dimension(prev->y);
    const typename TNodes::iterator end = nodes_.end();
    for (typename TNodes::iterator i = stde::next(nodes_.begin()); i != end; prev = i++)
    {
        // check for increasing control components
        //
        if (prev->x >= i->x)
        {
            LASS_THROW("Nodes in linear interpolator must have absolutely increasing control "
                "values.");
        }
 
        // check dimension of data
        //
        if (TDataTraits::dimension(i->y) != dataDimension_)
        {
            LASS_THROW("All data in linear interpolator must be of same dimension.");
        }
 
        // calculate derivative of previous node
        //
        prev->dy = i->y;
        TDataTraits::multiplyAccumulate(prev->dy, prev->y, -1);
        TDataTraits::scale(prev->dy, num::inv(i->x - prev->x));
    }
 
    // extend last node with same derivative as last edge.
    stde::prev(end)->dy = stde::prev(end, 2)->dy;
}
 
 
 
/** binary search to find node that belongs to iX
 *
 *  @return
 *  @arg the index @a i if @a iX is in the interval [@c nodes_[i].x, @c nodes_[i+1].x)
 *  @arg 0 if @a iX is smaller than @c nodes_[0].x</tt>
 *  @arg @c nodes_.size()-2 if @a iX is greater than @c nodes_[nodes_.size()-1].x
 *
 *  complexity: O(ln N)
 */
template <typename S, typename D, typename T>
const typename SplineLinear<S, D, T>::TNodeConstIterator
SplineLinear<S, D, T>::findNode(TScalar iX) const
{
    LASS_ASSERT(nodes_.size() >= 2);
 
    // early outs
    //
    if (iX < nodes_.front().x)
    {
        return nodes_.begin();
    }
    if (iX >= nodes_.back().x)
    {
        return stde::prev(nodes_.end());
    }
 
    // binary search
    //
    TNodeConstIterator first = nodes_.begin();
    TNodeConstIterator last = nodes_.end();
    while (stde::next(first) != last)
    {
        TNodeConstIterator middle = stde::next(first, std::distance(first, last) / 2);
        LASS_ASSERT(middle != first && middle != last);
 
        if (middle->x <= iX)
        {
            first = middle;
        }
        else
        {
            last = middle;
        }
        LASS_ASSERT(first != last);
    }
 
    LASS_ASSERT(first->x <= iX && last->x > iX);
    return first;
}
 
 
 
}
 
}
 
#endif
 
// EOF