/* Try to compute a trajectory in the given region.
* l Vector of length {params.N} that will receieve the left thruster
* values for each time step.
* r Vector of length {params.N} that will receive the right thruster
* values for each time step.
* params A structure containing the problem to solve.
*/
region_result_t compute_trajectory(double *l, double *r, region_params_t *params)
{
lbfgsfloatval_t fx;
lbfgs_parameter_t param;
int i;
int n = params->N;
lbfgsfloatval_t *x = lbfgs_malloc(n*2);
int ret = 0;
if (!x) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
return 1;
}
for (i = 0; i < n*2; i++) {
x[i] = 0;
}
/* Initialize the parameters for the L-BFGS optimization. */
lbfgs_parameter_init(¶m);
param.linesearch = LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE;
param.past = 100;
param.delta = 1e-4;
param.max_linesearch = 1000;
param.m = 5;
ret = lbfgs(n*2, x, &fx, evaluate, progress, params, ¶m);
memcpy(l, x, sizeof(l[0])*n);
memcpy(r, x+n, sizeof(r[0])*n);
printf("\nL-BFGS optimization terminated with status code = %d\n", ret);
printf("fx = %f\n", fx);
printf("\n");
printf("MATLAB variables:\n");
printf("l = [ ");
for (i = 0; i < n; i++) { printf("%f ", l[i]); }
printf(" ];\nr = [ ");
for (i = 0; i < n; i++) { printf("%f ", r[i]); }
printf(" ];\n");
printf("rx = [ ");
for (i = 0; i < params->polycount; i++) { printf("%f ", params->poly[i].x); }
printf(" ];\nry = [ ");
for (i = 0; i < params->polycount; i++) { printf("%f ", params->poly[i].y); }
printf(" ];\n");
printf("parm = [ %f %f %f %f %f %f %f %f %f ];\n",
params->p_0.x, params->p_0.y, params->v_0.x, params->v_0.y,
params->d_0.x, params->d_0.y, params->omega_0, params->mass,
params->radius);
printf("dt = %f\n", params->dt);
lbfgs_free(x);
if (ret == 0 || ret == 1 || ret == 2) {
return REGION_SOLVED;
} else {
return REGION_STUCK;
}
}
void model_new(model_t *model, size_t n_params, bool f_restrict)
{
model->params = lbfgs_malloc(n_params);
model->n_params = n_params;
if (f_restrict)
model->f_restrict = bitvector_alloc(n_params);
else
model->f_restrict = NULL;
}
int LbfgsWrap::run()
{
lbfgsfloatval_t fx;
if (m_x)
{
lbfgs_free(m_x);
}
m_x = lbfgs_malloc(m_unknow_x.rows() * m_unknow_x.cols());
if (!m_x)
{
EAGLEEYE_ERROR("failed to allocate a memory block for variables. \n");
return 1;
}
int rows = m_unknow_x.rows();
int cols = m_unknow_x.cols();
for (int i = 0; i < rows; ++i)
{
float* tr_data = m_unknow_x.row(i);
for (int j = 0; j < cols; ++j)
{
m_x[i * cols + j] = tr_data[j];
}
}
/*
Start the L-BFGS optimization; this will invoke the callback functions
evaluate() and progress() when necessary.
*/
int ret = lbfgs(rows * cols,m_x,&fx,_evaluate,_progress,this,&m_param);
for (int i = 0; i < rows; ++i)
{
float* tr_data = m_unknow_x.row(i);
for (int j = 0; j < cols; ++j)
{
tr_data[j] = float(m_x[i * cols + j]);
}
}
//report the result
EAGLEEYE_INFO("L-BFGS optimization terminated with status code = %d\n", ret);
EAGLEEYE_INFO("loss = %f",fx);
return ret;
}
int run(int N) {
lbfgsfloatval_t fx;
lbfgsfloatval_t *m_x = lbfgs_malloc(N);
if (m_x == NULL) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
return 1;
};
/* Initialize the variables. */
for (int i = 0;i < N; ++i) {
m_x[i] = -1.2;
};
/*
Start the L-BFGS optimization; this will invoke the callback functions
evaluate() and progress() when necessary.
*/
int ret = lbfgs(N, m_x, &fx, _evaluate, _progress, this, NULL);
/* Report the result. */
printf("L-BFGS optimization terminated with status code = %d\n", ret);
printf(" fx = %f, x[0] = %f, x[1] = %f\n", fx, m_x[0], m_x[1]);
return ret;
};
Matrix *runLbfgsOptim(Matrix *y, doubleVector *target, int L, int jobs, double epsilon) {
int M = y->nrow;
Matrix *w = createZeroMatrix(M, L);
lbfgsfloatval_t fx;
lbfgsfloatval_t *x = lbfgs_malloc(w->nrow*w->ncol);
lbfgs_parameter_t param;
if (x == NULL) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
exit(1);
}
/* Initialize the variables. */
for (size_t i = 0; i < w->nrow; i++) {
for (size_t j = 0; j < w->ncol;j++) {
x[j*w->nrow + i] = getMatrixElement(w, i, j);
}
}
lbfgs_parameter_init(¶m);
param.epsilon = epsilon;
param.m = 20;
LbfgsInput inp;
inp.target = target;
inp.y = y;
inp.jobs = jobs;
inp.L = w->ncol;
int ret = lbfgs(w->nrow*w->ncol, x, &fx, evaluate, progress, (void*)&inp, ¶m);
printf("L-BFGS optimization terminated with status code = %d\n", ret);
printf(" fx = %f\n", fx);
Matrix *w_opt = formMatrixFromFloatVal(x, w->nrow, w->ncol);
lbfgs_free(x);
return(w_opt);
}
int main(int argc, char *argv[])
{
int i, ret = 0;
int n = 2;
lbfgsfloatval_t fx;
lbfgsfloatval_t *x = lbfgs_malloc(n);
lbfgs_parameter_t param;
if (!x) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
return 1;
}
for (i = 0; i < n; i++) {
x[i] = 10;
}
/* Initialize the parameters for the L-BFGS optimization. */
lbfgs_parameter_init(¶m);
//param.linesearch = LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE;
ret = lbfgs(n, x, &fx, evaluate, progress, NULL, ¶m);
printf("L-BFGS optimization terminated with status code = %d\n", ret);
printf(" fx = %f; ", fx);
for (i = 0; i < n; i ++){
printf("x[%d] = %f; ", i, x[i]);
}
printf("\n");
/* Answer according to Wolfram Alpha:
* 1.00607 x^2 - 0.363643 x + 0.554
*/
lbfgs_free(x);
return 0;
}
void RAE::training()
{
x = lbfgs_malloc(getRAEWeightSize());
Map<MatrixLBFGS>(x, getRAEWeightSize(), 1).setRandom();
lbfgs_parameter_t param;
iterTimes = atoi(para->getPara("IterationTime").c_str());
loadTrainingData();
lbfgs_parameter_init(¶m);
param.max_iterations = iterTimes;
lbfgsfloatval_t fx = 0;
int ret;
ret = lbfgs(getRAEWeightSize(), x, &fx, RAELBFGS::evaluate, RAELBFGS::progress, this, ¶m);
cout << "L-BFGS optimization terminated with status code = " << ret << endl;
cout << " fx = " << fx << endl;
updateWeights(x);
logWeights(para);
trainingData.clear();
lbfgs_free(x);
}
Eigen::VectorXf minimizeLBFGS( EnergyFunction & efun, int restart, bool verbose ) {
Eigen::VectorXf x0 = efun.initialValue();
const int n = x0.rows();
lbfgsfloatval_t *x = lbfgs_malloc(n);
if (x == NULL) {
printf("ERROR: Failed to allocate a memory block for variables.\n");
return x0;
}
std::copy( x0.data(), x0.data()+n, x );
lbfgs_parameter_t param;
lbfgs_parameter_init(¶m);
// You might want to adjust the parameters to your problem
param.epsilon = 1e-6;
param.max_iterations = 50;
double last_f = 1e100;
int ret;
for( int i=0; i<=restart; i++ ) {
lbfgsfloatval_t fx;
ret = lbfgs(n, x, &fx, evaluate, verbose?progress:NULL, &efun, ¶m);
if( last_f > fx )
last_f = fx;
else
break;
}
if ( verbose ) {
printf("L-BFGS optimization terminated with status code = %d\n", ret);
}
std::copy( x, x+n, x0.data() );
lbfgs_free(x);
return x0;
}
lbfgsfloatval_t RAE::_evaluate(const lbfgsfloatval_t* x, lbfgsfloatval_t* g, const int n, const lbfgsfloatval_t step)
{
lbfgsfloatval_t fx = 0;
int RAEThreadNum = atoi(para->getPara("RAEThreadNum").c_str());
RAEThreadPara* threadpara = new RAEThreadPara[RAEThreadNum];
int batchsize = trainingData.size() / RAEThreadNum;
updateWeights(x);
for(int i = 0; i < RAEThreadNum; i++)
{
threadpara[i].cRAE = this->copy();
threadpara[i].g = lbfgs_malloc(getRAEWeightSize());
for(int j = 0; j < getRAEWeightSize(); j++)
{
threadpara[i].g[j] = 0;
}
if(i == RAEThreadNum-1)
{
map<string, int>::iterator s;
s = trainingData.begin();
for(int d = 0; d < i*batchsize; d++)
{
s++;
}
threadpara[i].cRAE->trainingData.insert(s, trainingData.end());
threadpara[i].instance_num = trainingData.size()%batchsize;
}
else
{
map<string, int>::iterator s, e;
s = trainingData.begin();
e = trainingData.begin();
for(int d = 0; d < i*batchsize; d++)
{
s++;
}
for(int d = 0; d < (i+1)*batchsize; d++)
{
e++;
}
threadpara[i].cRAE->trainingData.insert(s, e);
threadpara[i].instance_num = batchsize;
}
}
pthread_t* pt = new pthread_t[RAEThreadNum];
for (int a = 0; a < RAEThreadNum; a++) pthread_create(&pt[a], NULL, RAELBFGS::deepThread, (void *)(threadpara + a));
for (int a = 0; a < RAEThreadNum; a++) pthread_join(pt[a], NULL);
for(int i = 0; i < RAEThreadNum; i++)
{
fx += threadpara[i].lossVal;
for(int elem = 0; elem < getRAEWeightSize(); elem++)
{
g[elem] += threadpara[i].g[elem];
}
}
/*
int internal_node_num = 0;
for(map<string, int>::iterator it = trainingData.begin(); it != trainingData.end(); it++)
{
internal_node_num += getInternalNode(it->first);
}*/
fx /= trainingData.size();
fx += ZETA * decay();
for(int elem = 0; elem < getRAEWeightSize(); elem++)
{
g[elem] /= trainingData.size();
}
delWeight1 += ZETA * weights1;
delWeight1_b.setZero();
delWeight2 += ZETA * weights2;
delWeight2_b.setZero();
update(g);
delete pt;
pt = NULL;
for(int i = 0; i < RAEThreadNum; i++)
{
lbfgs_free(threadpara[i].g);
delete threadpara[i].cRAE;
}
delete threadpara;
threadpara = NULL;
return fx;
}
/**
* @brief Use the open source implement of OWL-QN to optimize the feature weights.
*
* @param weights feature weights
*/
void MStep_Trainer::OptimizeFeatureWeights_jp(vector<double>& weights)
{
//if expect_count==0 L1 norm LBFGS error!!
for(int i=0;i<this->m_scfg.phrase_rules_count;i++)
{
if(this->m_scfg.phrase_rules[i]->expect_count==0)
{
this->m_scfg.phrase_rules[i]->expect_count=1e-20;
this->m_scfg.N_cnt[3]+=1e-20;
}
}
cout<<"begin optimize the lambda!"<<endl;
int N = FeatureManager::GetSingleton().GetFeatureCount();
cout<<"the number of features is: "<<N<<endl;
int ret = 0;
lbfgsfloatval_t fx=0;
lbfgsfloatval_t *x = lbfgs_malloc(N);
lbfgs_parameter_t param;
if (x == NULL) {
cout<<"ERROR: Failed to allocate a memory block for variables"<<endl;
}
/* Initialize the variables. */
if(weights.size()>0)
{
for(int i=0;i<N;i++)
{
x[i]= weights[i];
}
}
/* Initialize the parameters for the L-BFGS optimization. */
lbfgs_parameter_init(¶m);
/*param.linesearch = LBFGS_LINESEARCH_BACKTRACKING;*/
/*
Start the L-BFGS optimization; this will invoke the callback functions
evaluate() and progress() when necessary.
*/
//we pre compute some value that used in gradient function
LBFGS_Param* lbfgs_param=new LBFGS_Param();
lbfgs_param->scfg=&this->m_scfg;
//int thread_count = this->config->configfile->read<int>("thread_count");
double L2_coefficient=this->m_pconfig->configfile->read<double>("L2_coefficient");
lbfgs_param->L2_coefficient= L2_coefficient;
PreComputeLBFGS_Param(lbfgs_param,N);
int max_iteration = this->m_pconfig->configfile->read<int>("max_iteration");
int past=this->m_pconfig->configfile->read<int>("past");
double delta=this->m_pconfig->configfile->read<double>("delta");
int linesearch =this->m_pconfig->configfile->read<int>("linesearch");
double gtol =this->m_pconfig->configfile->read<double>("gtol");
double epsilon=this->m_pconfig->configfile->read<double>("epsilon");
double L1_coefficient = this->m_pconfig->configfile->read<double>("L1_coefficient");
int orthantwise_start = this->m_pconfig->configfile->read<int>("orthantwise_start");
param.orthantwise_start = orthantwise_start;
param.orthantwise_end = N-1;
param.orthantwise_c=L1_coefficient;
param.linesearch = linesearch;
param.max_linesearch = 20;
param.max_iterations =max_iteration;
param.past=past;
param.delta=delta;
param.epsilon=epsilon;
param.gtol=gtol;
ret = lbfgs(N, x, &fx, evaluate_main, progress, static_cast<void*>(lbfgs_param), ¶m);
Loger::LogTime();
Loger::mylogfile<<"L-BFGS optimization terminated with status code = "<<ret<<endl;
Loger::mylogfile<<"L-BFGS optimization log value fx = "<<fx<<endl;
cout<<"L-BFGS optimization terminated with status code = "<<ret<<endl;
cout<<"L-BFGS optimization log value fx = "<<fx<<endl;
weights.clear();
weights.resize(N,0);
for(int i=0;i<N;i++)
{
weights[i]=x[i];
}
lbfgs_free(x);
}