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/*
EGYPT Toolkit for Statistical Machine Translation
Written by Yaser Al-Onaizan, Jan Curin, Michael Jahr, Kevin Knight, John Lafferty, Dan Melamed, David Purdy, Franz Och, Noah Smith, and David Yarowsky.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307,
USA.
*/
/* Perplexity.h
* ============
* Mike Jahr, 7/15/99
* Machine Translation group, WS99
* Center for Language and Speech Processing
*
* Last Modified by: Yaser Al-Onaizan, August 17, 1999
*
* Simple class used to calculate cross entropy and perplexity
* of models.
*/
#ifndef _PERPLEXITY_H
#define _PERPLEXITY_H
#include <cmath>
#include <fstream>
#include "Vector.h"
#include "defs.h"
#include "Array2.h"
#include "Globals.h"
#include "syncObj.h"
#define CROSS_ENTROPY_BASE 2
class Perplexity {
private:
double sum;
double wc;
Array2<double, Vector<double> > *E_M_L;
Vector<string> modelid;
Vector<double > perp;
Vector<double > ce;
Vector<string> name ;
Mutex mutex;
public:
~Perplexity() { delete E_M_L;}
Perplexity() {
E_M_L = new Array2<double, Vector<double> >(MAX_SENTENCE_LENGTH,MAX_SENTENCE_LENGTH);
unsigned int l, m ;
Vector<double> fact(MAX_SENTENCE_LENGTH, 1.0);
for (m = 2 ; m < MAX_SENTENCE_LENGTH ; m++)
fact[m] = fact[m-1] * m ;
for (m = 1 ; m < MAX_SENTENCE_LENGTH ; m++)
for (l = 1 ; l < MAX_SENTENCE_LENGTH ; l++) {
(*E_M_L)(l, m) = log (pow((LAMBDA * l), double(m)) * exp(-LAMBDA * double(l)) /
(fact[m])) ;
}
sum = 0 ;
wc = 0;
perp.clear();
ce.clear();
name.clear();
}
inline void clear() {
mutex.lock();
sum = 0 ;
wc = 0 ;
mutex.unlock();
}
size_t size() const {return(min(perp.size(), ce.size()));}
inline void addFactor(const double p, const double count, const int l,
const int m,bool withPoisson) {
mutex.lock();
wc += count * m ; // number of french words
sum += count * ( (withPoisson?((*E_M_L)(l, m)):0.0) + p) ;
mutex.unlock();
}
inline double perplexity() const {
return exp( -1*sum / wc);
}
inline double cross_entropy() const {
return (-1.0*sum / (log(double(CROSS_ENTROPY_BASE)) * wc));
}
inline double word_count() const {
return wc;
}
inline double getSum() const {
return sum ;
}
void record(string model);
friend void generatePerplexityReport(const Perplexity&, const Perplexity&,
const Perplexity&, const Perplexity&,
ostream&, int, int, bool);
};
#endif
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