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#ifndef NEURALLM_H
#define NEURALLM_H
#include <vector>
#include <cctype>
#include <cstdlib>
#include <boost/shared_ptr.hpp>
#include <../3rdparty/Eigen/Dense>
#include "util.h"
#include "vocabulary.h"
#include "neuralNetwork.h"
/*
To do:
- move digit mapping into vocabulary.h
*/
namespace nplm
{
class neuralLM : public neuralNetwork
{
char map_digits;
boost::shared_ptr<vocabulary> vocab;
int start, null;
public:
neuralLM()
: neuralNetwork(),
vocab(new vocabulary()),
map_digits(0)
{
}
void set_map_digits(char value) { map_digits = value; }
void set_vocabulary(const vocabulary &vocab)
{
*(this->vocab) = vocab;
start = vocab.lookup_word("<s>");
null = vocab.lookup_word("<null>");
}
const vocabulary &get_vocabulary() const { return *(this->vocab); }
int lookup_word(const std::string &word) const
{
if (map_digits)
for (int i=0; i<word.length(); i++)
if (isdigit(word[i]))
{
std::string mapped_word(word);
for (; i<word.length(); i++)
if (isdigit(word[i]))
mapped_word[i] = map_digits;
return vocab->lookup_word(mapped_word);
}
return vocab->lookup_word(word);
}
double lookup_ngram(const int *ngram_a, int n)
{
Eigen::Matrix<int,Eigen::Dynamic,1> ngram(m->ngram_size);
for (int i=0; i<m->ngram_size; i++)
{
if (i-m->ngram_size+n < 0)
{
if (ngram_a[0] == start)
ngram(i) = start;
else
ngram(i) = null;
}
else
{
ngram(i) = ngram_a[i-m->ngram_size+n];
}
}
return neuralNetwork::lookup_ngram(ngram);
}
double lookup_ngram(const std::vector<int> &ngram_v)
{
return lookup_ngram(ngram_v.data(), ngram_v.size());
}
template <typename Derived>
double lookup_ngram(const Eigen::MatrixBase<Derived> &ngram)
{
return neuralNetwork::lookup_ngram(ngram);
}
template <typename DerivedA, typename DerivedB>
void lookup_ngram(const Eigen::MatrixBase<DerivedA> &ngram, const Eigen::MatrixBase<DerivedB> &log_probs_const)
{
return neuralNetwork::lookup_ngram(ngram, log_probs_const);
}
void read(const std::string &filename)
{
std::vector<std::string> words;
m->read(filename, words);
set_vocabulary(vocabulary(words));
resize();
// this is faster but takes more memory
//m->premultiply();
}
};
template <typename T>
void addStartStop(std::vector<T> &input, std::vector<T> &output, int ngram_size, const T &start, const T &stop)
{
output.clear();
output.resize(input.size()+ngram_size);
for (int i=0; i<ngram_size-1; i++)
output[i] = start;
std::copy(input.begin(), input.end(), output.begin()+ngram_size-1);
output[output.size()-1] = stop;
}
template <typename T>
void makeNgrams(const std::vector<T> &input, std::vector<std::vector<T> > &output, int ngram_size)
{
output.clear();
for (int j=ngram_size-1; j<input.size(); j++)
{
std::vector<T> ngram(input.begin() + (j-ngram_size+1), input.begin() + j+1);
output.push_back(ngram);
}
}
inline void preprocessWords(const std::vector<std::string> &words,
std::vector< std::vector<int> > &ngrams,
int ngram_size,
const vocabulary &vocab,
bool numberize,
bool add_start_stop,
bool ngramize) {
int start = vocab.lookup_word("<s>");
int stop = vocab.lookup_word("</s>");
// convert words to ints
std::vector<int> nums;
if (numberize) {
for (int j=0; j<words.size(); j++) {
nums.push_back(vocab.lookup_word(words[j]));
}
}
else {
for (int j=0; j<words.size(); j++) {
nums.push_back(boost::lexical_cast<int>(words[j]));
}
}
// convert sequence to n-grams
ngrams.clear();
if (ngramize) {
std::vector<int> snums;
if (add_start_stop) {
addStartStop<int>(nums, snums, ngram_size, start, stop);
} else {
snums = nums;
}
makeNgrams(snums, ngrams, ngram_size);
}
else {
if (nums.size() != ngram_size)
{
std::cerr << "error: wrong number of fields in line" << std::endl;
std::exit(1);
}
ngrams.push_back(nums);
}
}
} // namespace nplm
#endif
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