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/*
* Copyright © 2013-2014 marmuta <marmvta@gmail.com>
*
* This file is part of Onboard.
*
* Onboard 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 3 of the License, or
* (at your option) any later version.
*
* Onboard 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, see <http://www.gnu.org/licenses/>.
*/
#ifndef LM_UNIGRAM_H
#define LM_UNIGRAM_H
#include "lm_dynamic.h"
//------------------------------------------------------------------------
// UnigramModel - Memory efficient model for word frequencies.
//------------------------------------------------------------------------
class UnigramModel : public DynamicModelBase
{
public:
class ngrams_iter : public DynamicModelBase::ngrams_iter
{
public:
ngrams_iter(UnigramModel* lm) :
it(lm->m_counts.begin()), model(lm)
{}
virtual BaseNode* operator*() const // dereference operator
{
if (it == model->m_counts.end())
return NULL;
else
{
BaseNode* pnode = const_cast<BaseNode*>(&node);
pnode->count = *it;
return const_cast<BaseNode*>(&node);
}
}
virtual void operator++(int unused) // postfix operator
{ it++; }
virtual void get_ngram(std::vector<WordId>& ngram)
{
WordId wid = it - model->m_counts.begin();
ngram.resize(1);
ngram[0] = wid;
}
virtual int get_level()
{ return 1; }
virtual bool at_root()
{ return false; }
public:
std::vector<CountType>::iterator it;
UnigramModel* model;
BaseNode node; // dummy node to satisfy the NGramIter interface
};
virtual DynamicModelBase::ngrams_iter* ngrams_begin()
{return new ngrams_iter(this);}
public:
UnigramModel()
{
set_order(1);
}
virtual ~UnigramModel()
{
#ifndef NDEBUG
uint64_t v = dictionary.get_memory_size();
uint64_t n = ngrams.get_memory_size();
printf("memory: dictionary=%ld, ngrams=%ld, total=%ld\n", v, n, v+n);
#endif
}
virtual void clear()
{
std::vector<CountType>().swap(m_counts); // clear and really free the memory
DynamicModelBase::clear(); // clears dictionary
}
virtual int get_max_order()
{
return 1;
}
virtual BaseNode* count_ngram(const wchar_t* const* ngram, int n,
int increment=1, bool allow_new_words=true)
{
if (n == 1)
{
std::vector<WordId> wids(n);
if (dictionary.query_add_words(ngram, n, wids, allow_new_words))
return count_ngram(&wids[0], n, increment);
}
return NULL;
}
virtual BaseNode* count_ngram(const WordId* wids, int n, int increment)
{
if (n != 1)
return NULL;
WordId wid = wids[0];
if (m_counts.size() <= wid)
m_counts.push_back(0);
m_counts.at(wid) += increment;
node.word_id = wid;
node.count = m_counts[wid];
return &node;
}
virtual int get_ngram_count(const wchar_t* const* ngram, int n)
{
if (!n)
return 0;
WordId wid = dictionary.word_to_id(ngram[0]);
return wid >= 0 && wid < m_counts.size() ? m_counts.at(wid) : 0;
}
virtual void get_node_values(BaseNode* node, int level,
std::vector<int>& values)
{
values.push_back(node->count);
}
virtual bool is_model_valid()
{
int num_unigrams = get_num_ngrams(0);
return num_unigrams == dictionary.get_num_word_types();
}
virtual void get_memory_sizes(std::vector<long>& values)
{
values.push_back(dictionary.get_memory_size());
values.push_back(sizeof(CountType) * m_counts.capacity());
}
protected:
virtual void get_words_with_predictions(
const std::vector<WordId>& history,
std::vector<WordId>& wids)
{}
virtual void get_probs(const std::vector<WordId>& history,
const std::vector<WordId>& words,
std::vector<double>& probabilities);
virtual int get_num_ngrams(int level)
{
if (level == 0)
return m_counts.size();
else
return 0;
}
virtual void reserve_unigrams(int count)
{
m_counts.resize(count);
fill(m_counts.begin(), m_counts.end(), 0);
}
protected:
std::vector<CountType> m_counts;
BaseNode node; // dummy node to satisfy the count_ngram interface
};
#endif
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