// ==========================================================================
//                 SeqAn - The Library for Sequence Analysis
// ==========================================================================
// Copyright (c) 2006-2016, Knut Reinert, FU Berlin
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
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//       notice, this list of conditions and the following disclaimer.
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//       notice, this list of conditions and the following disclaimer in the
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//     * Neither the name of Knut Reinert or the FU Berlin nor the names of
//       its contributors may be used to endorse or promote products derived
//       from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// ARE DISCLAIMED. IN NO EVENT SHALL KNUT REINERT OR THE FU BERLIN BE LIABLE
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//
// ==========================================================================
// Author: Manuel Holtgrewe <manuel.holtgrewe@fu-berlin.de>
// ==========================================================================
// Author: utro@math.unipa.it
// Author: srombo@deis.unical.it
// Author: Jonathan Göke
// ==========================================================================

//////////////////////////////////////////////////////////////////////////////

#ifndef SEQAN_STATISTICS_STATISTICS_MARKOV_MODEL_H_
#define SEQAN_STATISTICS_STATISTICS_MARKOV_MODEL_H_

#include <seqan/align.h>
#include <seqan/index.h>

namespace seqan {

/*!
 * @class MarkovModel
 * @headerfile <seqan/statistics.h>
 * @brief Gives a suitable representation of a Marcov Chain.
 *
 * @signature template <typename TAlphabet[, typename TFloat[, typename TSpec]]>
 *            class MarkovModel;
 *
 *
 * @tparam TAlphabet The type of the underlying alphabet.
 * @tparam TFloat    The type for storing counts, default is <tt>double</tt>.
 * @tparam TSpec     Tag for specialization.
 *
 * @section Examples
 *
 * @subsection Build a MarkovModel from Background
 *
 * @include demos/dox/statistics/build_markov_model.cpp
 *
 * The following example shows how to build a MarkovModel over a Dna alphabet from a set of background sequence.  After
 * build the model, we compute the zscore.
 *
 * @code{.console}
 * zscore: 11.8323
 * @endcode
 *
 * @subsection Load a MarkovModel from File
 *
 * We can also load the MarkovModel from a file (previously saved using @link MarkovModel::write @endlink).  Since we do
 * not have the background word set here but only the model, we compute the variance of a word using the function
 * calculateVariance from the alignment_free module.
 *
 * @include demos/dox/statistics/load_markov_model.cpp
 *
 * @code{.console}
 * variance: 0.267919
 * @endcode
 *
 * @var unsigned MarkovModel::order;
 * @brief The order of the MarkovModel.
 *
 * @var TMatrix MarkovModel::transition;
 * @brief The transition matirx.
 *
 * @var TVector MarkovModel::stationaryDistribution;
 * @brief The vector of characgter distribution (String of TFloat).
 *
 *
 * @fn MarkovModel::MarkovModel
 * @brief Constructor
 *
 * @signature MarkovModel::MarkovModel(order);
 *
 * @param[in] order The order of the model (<tt>unsigned</tt>).
 *
 *
 * @fn MarkovModel::build
 * @brief Compute the transition matrix from a training set.
 *
 * The character statitionary distribution and the auxiliary information that give raise to an instance of a
 * Markov Model are also computed.
 *
 * @signature void MarkovModel::build(stringSet);
 *
 * @param[in] stringSet The StringSet to build the model for.
 *
 *
 * @fn MarkovModel::set
 * @brief Set transition matrix.
 *
 * Given e transition matrix, sets it as transition matrix of the MarkovModel and computes (if it is not available) the
 * vector of character distributions and the auxiliary information.
 *
 * @signature void MarkovModel(transition[, stationaryDistribution]);
 *
 * @param[in,out] transition             The transition matrix.
 * @param[in]     stationaryDistribution The vector of character distributions.
 *
 *
 * @fn MarkovModel::emittedProbability
 * @brief Computes the probability that a string or a set of strings is emitted by the MarkovModel.
 *
 * @signature TFloat MarkovModel::emittedProbability(s);
 * @signature TFloat MarkovModel::emittedProbability(ss);
 *
 * @param[in] s  The String to compute the emission probability for.
 * @param[in] ss The StringSet to compute the emission probability for.
 *
 * @return TFloat The emission probability, TFloat is the TFloat from the MarkovModel.
 *
 *
 * @fn MarkovModel::write
 * @brief Stores an instance of a markovModel in a file.
 *
 * @signature void MarkovModel::write(file);
 *
 * @param[in,out] file The file to write the model to (type <tt>FILE *</tt>).
 *
 *
 * @fn MarkovModel::read
 * @brief Load an instance of MarkovModel from a file.
 *
 * @signature void MarkovModel::read(file);
 *
 * @param[in,out] file The file to read the model from (type <tt>FILE *</tt>).
 */

/*
with matrix calss matrices
*/
template <typename TAlphabet, typename TFloat = double, typename TSpec = Default>
class MarkovModel
{

public:

    //Definition of matrix types, could be set to two dimensional as soon as implemented
    typedef Matrix<TFloat, 2> TMatrix;
    //typedef String<TFloat> TMatrix;
    typedef String<TFloat> TVector;

    unsigned int order;
    TMatrix transition;
    TVector stationaryDistribution;
    //The following matrices are only for internal use of the class
    TMatrix _q;            //QPP^(morder-1)
    TMatrix _qppp;        //QPP^(morder-1)
    TMatrix _qppqpp;    //QPPQP^(morder-1)


    MarkovModel(unsigned int order_):
    order(order_)
    {

        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        //unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);
        unsigned int column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);

        //Special case of order 0 marko model:
        //A Bernoulli scheme is a special case of a Markov chain where the transition probability matrix has identical rows, which means that the next state is even independent of the current state (in addition to being independent of the past states).
        if(order==0)
        {
            column_size=alphabet_size;

        }
        //resize the matrix
        setLength(transition, 0, column_size);
        setLength(transition, 1, column_size);

        resize(transition,(TFloat) 0);

        clear(stationaryDistribution);
        resize(stationaryDistribution, column_size);
    }


    ///////////////////////////////////////////////////////////////
    ///// BUILD THE MODEL
    ///////////////////////////////////////////////////////////////
    typedef String<Dna> TText;
    typedef Size<TText>::Type TSize;
    typedef StringSet<TText > TStringSet;

    //template <typename TAlphabet>
    void build(StringSet<String<TAlphabet> > const &strings)
    {

        typedef String<TAlphabet> TText;
        //typedef Size<TText>::Type TSize;
        typedef StringSet<TText > TStringSet;

        typedef Index<TStringSet, IndexQGram<SimpleShape> > TIndex;
        typedef typename Fibre<TIndex, QGramDir>::Type TDir;
        typedef typename Iterator<TDir, Standard>::Type TIter;
        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        //unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);
        unsigned int column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);

        //Special case of order 0 marko model:
        //A Bernoulli scheme is a special case of a Markov chain where the transition probability matrix has identical rows, which means that the next state is even independent of the current state (in addition to being independent of the past states).
        if(order==0)
        {
            column_size=alphabet_size;

        }

        TIndex ind(strings);
        resize(indexShape(ind), order + 1);
        indexRequire(ind, QGramSADir());

        TIter itBegin = begin(indexDir(ind), Standard());
        TIter itEnd = end(indexDir(ind), Standard()) - 1;

        //Frequency of all q-grams for a markov model of order q-1 to calculate transition probabilitiesof (q-1) gram to next (q-1)gram
        String<TAlphabet> qgram;
        Shape<TAlphabet, SimpleShape> orderShape;
        resize(orderShape, order);
        if(order==0)
        {
            resize(orderShape, order+1);
        }

        //count transition of sequences according to model order
        for(TIter itA = itBegin; itA != itEnd; ++itA)
        {
            unhash(qgram, itA - itBegin, weight(indexShape(ind)));
            //std::cout<<"\n"<<qgram<<"\n"<<(*(itA+1) - *itA)<<"\n"<<hash(orderShape, begin(qgram));
            //old for the array: value(transition, hash(orderShape, begin(qgram)) * column_size + hash(orderShape, begin(qgram) + 1)) = *(itA+1) - *itA;

            if(order==0)
            {
                for(unsigned int row=0;row<column_size;++row)
                {
                    value(transition, row,hash(orderShape, begin(qgram))) = static_cast<TFloat>(*(itA+1) - *itA);
                }
            }
            else
            {
                //new for the matrix
                value(transition, hash(orderShape, begin(qgram)),hash(orderShape, begin(qgram) + 1)) = static_cast<TFloat>(*(itA+1) - *itA);
            }
        }
        //std::cout<<"\n"<<transition<<"\n\n";

        //normalization, rows have to sum up to 1
        for(unsigned int row = 0; row < column_size;++row)
        {
            TFloat sum = 0;
            for(unsigned int col = 0; col < column_size;++col)
            {
                sum += value(transition, row,col);    //sum of the rows
            }
            if (sum != 0)
            {
                for(unsigned int col = 0; col < column_size;++col)
                {
                    value(transition, row, col) /= sum;    //normalize by dividing by sum of rows
                }
            }
        }
//std::cout<<transition<<"\n\n";
        //Special case of order 0 marko model:
        //A Bernoulli scheme is a special case of a Markov chain where the transition probability matrix has identical rows, which means that the next state is even independent of the current state (in addition to being independent of the past states).
        if(order==0)
        {
            order=1;
        }
        //----Calculation of stationary Distribution-----
        TMatrix temp = transition;
        //initialise a variable t representing a good threshold to estimate the vector
        //after multiplying t times the transition matrix with itself
        unsigned int t=6;
        for (unsigned int i=0; i<t; i++)
        {
            temp=temp*temp;
        }

        for (unsigned int i=0; i<column_size; i++){
            value(stationaryDistribution,i)=value(temp, 0,i);

        }
        //std::cout<<temp<<"\n\n";

        //for(unsigned int i=0;i<length(stationaryDistribution);++i){std::cout<<stationaryDistribution[i]<<"\t";}
        //!is commented since I dont use it and it makes everything very slow for k>3 or 4
        //!call ensureAuxMatrices(markovModel);
        //_computeAuxiliaryMatrices();


    }


    ///////////////////////////////////////////////////////////////
    ///// EMITTEDPROBABILITY
    ///////////////////////////////////////////////////////////////


    template <typename TString, typename TSetSpec>
    TFloat emittedProbability(StringSet<TString, TSetSpec > const &stringSet)
    {
        TFloat p = 0;

        for(unsigned int i=0; i<length(stringSet); i++)
        {
            p+= emittedProbability(stringSet[i]);
        }

        return p;
    }

    template <typename TString>
    TFloat emittedProbability(TString const &string)
    {
        Shape<TAlphabet, SimpleShape> orderShape;
        resize(orderShape, order);

        int row = hash(orderShape,begin(string));
        TFloat p = value(stationaryDistribution,row);

        for(unsigned int i=1; i<(length(string)-order+1); i++)
        {
            int column=hash(orderShape,begin(string)+i);
            p*=value(transition,row,column);

            row = column;
        }

        return p;
    }


    ///////////////////////////////////////////////////////////////
    ///// SET THE MODEL
    ///////////////////////////////////////////////////////////////


    void set(Matrix<TFloat,2> &iTransition)
    {
        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);

        transition = iTransition;

        Matrix<TFloat,2> temp = transition;
        //initialise a variable t representing a good threshold to estimate the vector
        //after multiplying e times the transition matrix with itself
        unsigned int t=6;
        for (unsigned int i=0; i<t; i++){
            temp=temp*temp;
        }

        for (unsigned int i=0; i<column_size; i++){
            value(stationaryDistribution,i)=value(temp, 0,i);    //Set stationary distribution to row of transition^6
        }

        //_computeAuxiliaryMatrices();
    }



    void set(Matrix<TFloat,2> &iTransition, String<TFloat> &iStationaryDistribution)
    {
        transition = iTransition;

        stationaryDistribution = iStationaryDistribution;

        //_computeAuxiliaryMatrices();
    }


    ///////////////////////////////////////////////////////////////
    ///// WRITE
    ///////////////////////////////////////////////////////////////


    void write(FILE *file)
    {
        ensureAuxMatrices(*this);
        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);

        //write the transition matrix
        for(unsigned int row=0; row<column_size; row++){
            for(unsigned int col=0; col<column_size; col++){
              fprintf(file,"%f ",value(transition, row,col));
            }
            fprintf(file,"\n");
        }
        //write the stationary distribution vector
        for(unsigned int row=0; row<column_size; row++){
              fprintf(file,"%f ",value(stationaryDistribution, row));
            }
        fprintf(file,"\n");

        if(length(_q)){
            //write the auxiliary matrix
            for(unsigned int row=0; row<column_size; row++){
                for(unsigned int col=0; col<column_size; col++){
                    fprintf(file,"%f ",value(_q, row,col));
                }
                fprintf(file,"\n");
            }

            for(unsigned int row=0; row<column_size; row++){
                for(unsigned int col=0; col<column_size; col++){
                  fprintf(file,"%f ",value(_qppp,row,col));
                }
                fprintf(file,"\n");
            }

            for(unsigned int row=0; row<column_size; row++){
                for(unsigned int col=0; col<column_size; col++){
                fprintf(file,"%f ",value(_qppqpp, row,col));
                }
                fprintf(file,"\n");
            }
        }
    }


    ///////////////////////////////////////////////////////////////
    ///// READ
    ///////////////////////////////////////////////////////////////

    void read(FILE *file)
    {
        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);

        //read the transition matrix
        for(unsigned int row=0; row<column_size; row++)
        {
            for(unsigned int col=0; col<column_size; col++)
            {
              int unused = fscanf(file,"%lf ", & value(transition, row,col));
              (void)unused;
            }
            int unused = fscanf(file,"\n");
            (void)unused;
        }
        //read the stationary distribution vector
        for(unsigned int row=0; row<column_size; row++)
        {
            int unused = fscanf(file,"%lf ",&value(stationaryDistribution, row));
        (void)unused;
        }
        int unused = fscanf(file,"\n");
        (void)unused;

        if (!feof(file))
        {
            setLength(_q, 0, column_size);
            setLength(_q, 1, column_size);
            resize(_q);

            //read the auxiliary matrix
            for(unsigned int row=0; row<column_size; row++)
            {
                for(unsigned int col=0; col<column_size; col++)
                {
                    int unused = fscanf(file,"%lf ",&value(_q, row,col));
                    (void)unused;
                }
                int unused = fscanf(file,"\n");
                (void)unused;
            }
            setLength(_qppp, 0, column_size);
            setLength(_qppp, 1, column_size);
            resize(_qppp);
            for(unsigned int row=0; row<column_size; row++){
                for(unsigned int col=0; col<column_size; col++){
                  int unused = fscanf(file,"%lf ",&value(_qppp, row,col));
                  (void)unused;
                }
                int unused = fscanf(file,"\n");
                (void)unused;
            }
            setLength(_qppqpp, 0, column_size);
            setLength(_qppqpp, 1, column_size);
            resize(_qppqpp);
            for(unsigned int row=0; row<column_size; row++){
                for(unsigned int col=0; col<column_size; col++){
                    int unused = fscanf(file,"%lf ",&value(_qppqpp, row,col));
                    (void)unused;
                }
                int unused = fscanf(file,"\n");
                (void)unused;
            }
        }
    }


    /////////////////////////////////////////////////////////////////////////////
    ///// COMPUTE THE AUXILIARY MATRICES FOR THE VARIANCE And Z-SCORE COMPUTATION
    /////////////////////////////////////////////////////////////////////////////



    void _computeAuxiliaryMatrices()
    {
    //std::cout<<"auxMat\n";
        //clear(_q);
        //clear(_qppp);
        //clear(_qppqpp);

        unsigned int const alphabet_size = ValueSize<TAlphabet>::VALUE;
        unsigned int const column_size = (unsigned int) std::pow((double) alphabet_size, (int) order);
        TMatrix I;
        TMatrix Ip;

        //resize the matrices
        setLength(I, 0, column_size);
        setLength(I, 1, column_size);
        resize(I, 0.0);

        setLength(Ip, 0, column_size);
        setLength(Ip, 1, column_size);
        resize(Ip, 0.0);

        for(unsigned int i=0; i<column_size; i++){
            value(I,i,i)=1.0;
             for (unsigned int j=0; j<column_size; j++)
                value(Ip,i,j)=value(stationaryDistribution,j);
        }


        _q=transition-I;
        _q=_q+Ip;
        _q=_computeInverseMatrix(_q);//works for simple non singular matrices, others not checked
        //original code: _q=_computeInverseMatrix(_matricialSum(_matricialDifference(transition, I), Ip));

        _qppp=_q*transition;
        _qppqpp = _qppp*transition;
        _qppqpp = _qppqpp*_q;
        for(unsigned int i=1; i<order; i++)
        {
            _qppp=_qppp*transition;
            _qppqpp=_qppqpp*transition;
        }
    }


};

//////////////////////////////////////////////////////////////////////////////
// _computeInverseMatrix
//////////////////////////////////////////////////////////////////////////////

/*
 * @fn _computeInverseMatrix
 * @brief Computes the inverse matrix of a given matrix.
 *
 * @signature TMatrix _computeInverseMatrix(matrix);
 *
 * @param[in] matrix The matrix in the input (2-dimensional Matrix).
 *
 * @return TMatrix The inverse matrix, the same type as matrix.
 */

template <typename TValue>
Matrix<TValue,2> _computeInverseMatrix(Matrix<TValue,2> &matrix)
{
    typedef Matrix<TValue,2> TMatrix;
    unsigned int n = length(matrix,0);
    TMatrix result;
    //resize the matrix
    setLength(result, 0, n);
    setLength(result, 1, n);
    resize(result, 0.0);

    //copy the matrix in result, since the procedure is in-place
    TMatrix tmp = matrix;

    //lu decomposition of a in-place
    String<TValue> indx=_ludcmp(tmp);

    String<TValue> col;

    unsigned int i;

    // inverse by columns
    for (unsigned int j=0; j<n; j++)
    {
        resize(col,n,0);
        if(j>0)
        {
            for(i=0; i<n; i++)
            {
                value(col,i)=0;
            }
        }
        value(col,j) = 1;

        _lubksb(tmp,indx,col);

        for (i=0; i<n; i++)
        {
            value(result, i,j)= value(col,i);
        }

    }

    return result;
}

/*
 *
 * LU decomposition
 *
 */

template <typename TValue>
String<TValue> _ludcmp(Matrix<TValue,2> &result)
{
    double const TINY = 1.0e-20;
    int n = length(result,0);
    int i, imax, j, k,d;
    imax = std::numeric_limits<int>::min();
    double big,dum,sum,temp;
    String<TValue> vv;
    resize(vv, n, 1.0);


    d = 1;
    for (i=1; i<=n; i++)
    {
        big = 0.0;
        for (j=1; j<=n; j++)
        {
            if ((temp=fabs(value(result, i-1,j-1)))>big)
            {
                big = temp;
            }
        }
        if (big==0.0)
        {
            std::cout<<"Singular matrix in routine ludcmp" << std::endl;
            exit(1);
        }

        value(vv,i-1) = 1.0/big;
    }
    String<TValue> indx;
    resize(indx,n);

    for (j=1; j<=n; j++)
    {
        for (i=1; i<j; i++)
        {
            sum = value(result,i-1,j-1);
            for (k=1; k<i; k++)
            {
                sum -= value(result, i-1,k-1)*value(result, k-1,j-1);
            }
            value(result, i-1,j-1) = sum;
        }
        big = 0.0;
        for (i=j; i<=n; i++)
        {
            sum = value(result,i-1,j-1);
            for (k=1; k<j; k++)
            {
                sum -= value(result, i-1,k-1)*value(result, k-1,j-1);
            }
            value(result, i-1,j-1) = sum;
            if ((dum = value(vv, i-1)*fabs(sum))>=big)
            {
                big = dum;
                imax = i;
            }
        }
        if (j != imax)
        {
            for (k=1; k<=n; k++)
            {
                dum = value(result,imax-1,k-1);
                value(result, imax-1,k-1) = value(result, j-1,k-1);
                value(result,j-1,k-1) = dum;
            }
            d = -(d);
            value(vv, imax-1)=value(vv,j-1);
        }

        value(indx, j-1) = imax;

        if (value(result, j-1,j-1) == 0.0)
        {
            value(result, j-1,j-1) = TINY;
        }
        if (j!=n)
        {
            dum = 1.0/(value(result,j-1,j-1));
            for (i=j+1; i<=n; i++)
            {
                value(result, i-1,j-1) *= dum;
            }
        }
  }

 return indx;

}


template <typename TValue>
void _lubksb(Matrix<TValue,2> &a, String<TValue> &indx, String<TValue>  &b)
{
    int n =length(a,0);    //Number of columns in matrix a
    int i, ii=0,ip,j;
    double sum;

    for (i=1; i<=n; i++)
    {
        ip = static_cast<int>(value(indx,i-1));
        sum = value(b,ip-1);
        value(b,ip-1) = value(b,i-1);
        if (ii)
        {
            for (j=ii;j<=i-1;j++)
            {
                sum -=value(a,i-1,j-1)*value(b,j-1);
            }
        }
        else
        if (sum)
        {
            ii=i;
        }
        value(b,i-1) = sum;
    }
    for (i=n; i>=1; i--)
    {
        sum = value(b,i-1);
        for (j=i+1; j<=n; j++)
        {
            sum -= value(a,i-1,j-1)*value(b,j-1);
        }
        value(b,i-1) = sum/value(a,i-1,i-1);
    }
}


//////////////////////////////////////////////////////////////////////////////
// Interface
//////////////////////////////////////////////////////////////////////////////


///////////////////////////////////////////////////////////////

template <typename TAlphabet, typename TFloat, typename TSpec>
void buildMarkovModel(MarkovModel<TAlphabet, TFloat, TSpec> &mm,
        StringSet<String<TAlphabet > > &stringSet)
{

    mm.build(stringSet);

}

template <typename TAlphabet, typename TFloat, typename TSpec>
void buildMarkovModel(MarkovModel<TAlphabet, TFloat, TSpec> &mm,
        StringSet<String<TAlphabet > > const &stringSet)
{

    mm.build(stringSet);

}


///////////////////////////////////////////////////////////////

template <typename TAlphabet, typename TFloat, typename TSpec>
void setMarkovModel(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
                    Matrix<TFloat,2> &iTransition)
{
    mm.set(iTransition);
}



template <typename TAlphabet, typename TFloat, typename TSpec>
void setMarkovModel(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
                    Matrix<TFloat,2> &iTransition,
                    String<TFloat> &iStationaryDistribution)
{
    mm.set(iTransition, iStationaryDistribution);
}

///////////////////////////////////////////////////////////////

template <typename TAlphabet, typename TFloat, typename TSpec>
TFloat emittedProbability(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
        StringSet<String<TAlphabet > > & stringSet)
{
    return mm.emittedProbability(stringSet);
}

template <typename TAlphabet, typename TFloat, typename TSpec>
TFloat emittedProbability(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
                          String<TAlphabet> &string)
{
    return mm.emittedProbability(string);
}

//const
template <typename TAlphabet, typename TFloat, typename TSpec, typename TString, typename TSetSpec>
TFloat emittedProbability(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
        StringSet<TString, TSetSpec> const &stringSet)
{
    return mm.emittedProbability(stringSet);
}

template <typename TAlphabet, typename TFloat, typename TSpec, typename TString>
TFloat emittedProbability(MarkovModel<TAlphabet, TFloat, TSpec> & mm,
                          TString const &string)
{
    return mm.emittedProbability(string);
}

///////////////////////////////////////////////////////////////

template <typename TAlphabet, typename TFloat, typename TSpec>
void write(FILE *file,
           MarkovModel<TAlphabet, TFloat, TSpec> & mm )
{
//IOREV only defined for FILE* not for File(), uses custom io with fprintf
    mm.write(file);
}

///////////////////////////////////////////////////////////////

template <typename TAlphabet, typename TFloat, typename TSpec>
void read(FILE *file,
          MarkovModel<TAlphabet, TFloat, TSpec> & mm )
{
//IOREV only defined for FILE* not for File(), uses custom io with fscanf
    mm.read(file);
}
//////////////////////////////////////////////////////////////////////////////
template <typename TAlphabet, typename TFloat, typename TSpec>
void ensureAuxMatrices(MarkovModel<TAlphabet, TFloat, TSpec> & mm )
{

    if(empty(mm._q)){
        mm._computeAuxiliaryMatrices();
    }
}


}

#endif  // #ifndef SEQAN_STATISTICS_STATISTICS_MARKOV_MODEL_H_
