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gmm.cc
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gmm.cc
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/* -*- C++ -*-
*
* Copyright (c) 2014
* Spoken Language Systems Group
* MIT Computer Science and Artificial Intelligence Laboratory
* Massachusetts Institute of Technology
*
* All Rights Reserved
./gmm.cc
* FILE: cluster.cc *
* *
* *
* Chia-ying (Jackie) Lee <chiaying@csail.mit.edu> *
* Feb 2014 *
*********************************************************************/
#include "gmm.h"
GMM::GMM(Config* config) {
_config = config;
_mix_num = _config -> mix_num();
_weight.resize(_mix_num, 0);
for (int i = 0; i < _mix_num; ++i) {
Mixture mixture(_config);
_mixtures.push_back(mixture);
}
}
GMM::GMM(Config* config, int mix_num) {
_config = config;
_mix_num = mix_num;
_weight.resize(_mix_num, 0);
for (int i = 0; i < _mix_num; ++i) {
Mixture mixture(_config);
_mixtures.push_back(mixture);
}
}
GMM::GMM(const GMM& rhs) {
_config = rhs.config();
_mix_num = rhs.mix_num();
_mixtures = rhs.mixtures();
_weight = rhs.weight();
}
GMM& GMM::operator= (const GMM& rhs) {
_config = rhs.config();
_mix_num = rhs.mix_num();
_mixtures = rhs.mixtures();
_weight = rhs.weight();
return *this;
}
void GMM::set_mixture(Mixture& mixture, int index) {
_mixtures[index] = mixture;
}
void GMM::set_mixtures(vector<Mixture>& mixtures) {
_mixtures = mixtures;
}
void GMM::set_weight(vector<float> weight) {
_weight = weight;
}
void GMM::Minus(float* data, int index) {
--_weight[index];
_mixtures[index].Minus(data);
}
void GMM::Plus(float* data, int index) {
++_weight[index];
_mixtures[index].Plus(data);
}
vector<float> GMM::ComponentLikelihood(float* data) {
vector<float> likelihood;
for (int i = 0; i < _mix_num; ++i) {
likelihood.push_back(_weight[i] + _mixtures[i].likelihood(data));
}
return likelihood;
}
vector<float> GMM::ComponentLikelihood(int index) {
vector<float> likelihood;
for (int i = 0; i < _mix_num; ++i) {
likelihood.push_back(_weight[i] + _mixtures[i].likelihood(index));
}
return likelihood;
}
float GMM::ComputeLikehood(float* data) {
vector<float> likelihood;
for (int i = 0; i < _mix_num; ++i) {
likelihood.push_back(_weight[i] + _mixtures[i].likelihood(data));
}
return _toolkit.SumLogs(likelihood);
}
float GMM::ComputeLikehood(int index) {
vector<float> likelihood;
for (int i = 0; i < _mix_num; ++i) {
likelihood.push_back(_weight[i] + _mixtures[i].likelihood(index));
}
return _toolkit.SumLogs(likelihood);
}
void GMM::ComputeLikehood(vector<float*> data, float* likelihood) {
for (int i = 0; i < (int) data.size(); ++i) {
likelihood[i] = ComputeLikehood(data[i]);
}
}
void GMM::ComputeLikehood(int start_frame, int end_frame, float* likelihood) {
for (int i = start_frame; i <= end_frame; ++i) {
likelihood[i - start_frame] = ComputeLikehood(i);
}
}
GMM& GMM::operator+= (GMM& rhs) {
vector<float> rhs_weight = rhs.weight();
for (int i = 0; i < _mix_num; ++i) {
_weight[i] += rhs_weight[i];
_mixtures[i] += rhs.mixture(i);
}
return *this;
}
void GMM::PreCompute(float** data, int frame_num) {
for (int i = 0; i < _mix_num; ++i) {
_mixtures[i].PreCompute(data, frame_num);
}
}
void GMM::Save(ofstream& fout) {
fout.write(reinterpret_cast<char*> (&_mix_num), sizeof(int));
fout.write(reinterpret_cast<char*> (&_weight[0]), sizeof(float) * _mix_num);
for (int m = 0; m < _mix_num; ++m) {
float det = mixture(m).det();
vector<float> mean = mixture(m).mean();
vector<float> pre = mixture(m).pre();
fout.write(reinterpret_cast<char*> (&det), sizeof(float));
fout.write(reinterpret_cast<char*> (&mean[0]), sizeof(float) * mean.size());
fout.write(reinterpret_cast<char*> (&pre[0]), sizeof(float) * pre.size());
}
}
void GMM::Load(ifstream& fin) {
fin.read(reinterpret_cast<char*> (&_mix_num), sizeof(int));
fin.read(reinterpret_cast<char*> (&_weight[0]), sizeof(float) * \
_mix_num);
for (int m = 0; m < _mix_num; ++m) {
float det;
vector<float> mean(_config -> dim(), 0);
vector<float> pre(_config -> dim(), 0);
fin.read(reinterpret_cast<char*> (&det), sizeof(float));
fin.read(reinterpret_cast<char*> (&mean[0]), sizeof(float) * \
_config -> dim());
fin.read(reinterpret_cast<char*> (&pre[0]), sizeof(float) * \
_config -> dim());
_mixtures[m].set_det(det);
_mixtures[m].set_mean(mean);
_mixtures[m].set_pre(pre);
}
}
GMM::~GMM() {
}