void train(lbfgsfloatval_t* fx, lbfgsfloatval_t* parameter,
int featsize, int poslabel,
boost::shared_ptr<Eigen::SparseMatrix<double, Eigen::RowMajor> >& samples,
boost::shared_ptr<Eigen::VectorXi>& labels,
boost::shared_ptr<lbfgs_parameter_t>& opt_params,
boost::shared_ptr<derivative_parameter_t>& prog_params){
boost::shared_ptr<Eigen::VectorXi> instlabels( new Eigen::VectorXi(labels->rows()) );
instlabels->setOnes();
Eigen::Map<Eigen::VectorXd> map_param(parameter, featsize, 1);
for(int i=0; i < instlabels->rows(); ++i){
if( labels->coeff(i) != poslabel ){
instlabels->coeffRef(i) = -1;
}
}
map_param.setZero();
int ret=-1;
optimization_instance_t optInst(poslabel, samples, instlabels, prog_params);
do {
ret = lbfgs(featsize, parameter, fx,
evaluate, progress, (void*)&optInst,
opt_params.get());
if( 0 == ret || LBFGSERR_MAXIMUMLINESEARCH == ret){
std::cout << "L-BFGS optimize successful" << std::endl;
}
std::cout << "L-BFGS optimization terminated with status code = " << ret << std::endl;
std::cout << "objective function value = " << *fx << std::endl;
std::cout << "Training Accuracy " << test(parameter, featsize, samples, instlabels) * 100
<< std::endl;
} while( 0 );
}
/** @pred optimizer_run(-F,-Status)
Runs the optimization, _F is the best (minimal) function value and
Status (int) is the status code returned by libLBFGS. Anything except
0 indicates an error, see the documentation of libLBFGS for the
meaning.
*/
static int optimizer_run(void) {
int ret = 0;
YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2;
YAP_Int s1, s2;
lbfgsfloatval_t fx;
lbfgsfloatval_t * tmp_x=x;
if (optimizer_status == OPTIMIZER_STATUS_NONE) {
printf("ERROR: Memory for parameter vector not initialized, please call optimizer_initialize/1 first.\n");
return FALSE;
}
if (optimizer_status != OPTIMIZER_STATUS_INITIALIZED) {
printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n");
return FALSE;
}
// both arguments have to be variables
if (! YAP_IsVarTerm(t1) || ! YAP_IsVarTerm(t2)) {
return FALSE;
}
s1 = YAP_InitSlot(t1);
s2 = YAP_InitSlot(t2);
optimizer_status = OPTIMIZER_STATUS_RUNNING;
ret = lbfgs(n, x, &fx, evaluate, progress, NULL, ¶m);
x=tmp_x;
optimizer_status = OPTIMIZER_STATUS_INITIALIZED;
YAP_Unify(YAP_GetFromSlot(s1),YAP_MkFloatTerm(fx));
YAP_Unify(YAP_GetFromSlot(s2),YAP_MkIntTerm(ret));
return TRUE;
}
TEST(Services, do_bfgs_optimize__lbfgs) {
std::vector<double> cont_vector(2);
cont_vector[0] = -1; cont_vector[1] = 1;
std::vector<int> disc_vector;
static const std::string DATA("");
std::stringstream data_stream(DATA);
stan::io::dump dummy_context(data_stream);
Model model(dummy_context);
typedef stan::optimization::BFGSLineSearch<Model,stan::optimization::LBFGSUpdate<> > Optimizer_LBFGS;
Optimizer_LBFGS lbfgs(model, cont_vector, disc_vector, &std::cout);
double lp = 0;
bool save_iterations = true;
int refresh = 0;
int return_code;
unsigned int random_seed = 0;
rng_t base_rng(random_seed);
std::fstream* output_stream = 0;
mock_callback callback;
return_code = stan::services::optimization::do_bfgs_optimize(model, lbfgs, base_rng,
lp, cont_vector, disc_vector,
output_stream, &std::cout,
save_iterations, refresh,
callback);
EXPECT_FLOAT_EQ(return_code, 0);
EXPECT_EQ(35, callback.n);
}
float64_t CKLDualInferenceMethod::lbfgs_optimization()
{
lbfgs_parameter_t lbfgs_param;
lbfgs_param.m = m_m;
lbfgs_param.max_linesearch = m_max_linesearch;
lbfgs_param.linesearch = m_linesearch;
lbfgs_param.max_iterations = m_max_iterations;
lbfgs_param.delta = m_delta;
lbfgs_param.past = m_past;
lbfgs_param.epsilon = m_epsilon;
lbfgs_param.min_step = m_min_step;
lbfgs_param.max_step = m_max_step;
lbfgs_param.ftol = m_ftol;
lbfgs_param.wolfe = m_wolfe;
lbfgs_param.gtol = m_gtol;
lbfgs_param.xtol = m_xtol;
lbfgs_param.orthantwise_c = m_orthantwise_c;
lbfgs_param.orthantwise_start = m_orthantwise_start;
lbfgs_param.orthantwise_end = m_orthantwise_end;
float64_t nlml_opt=0;
void * obj_prt = static_cast<void *>(this);
Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);
lbfgs(m_W.vlen, m_W.vector, &nlml_opt,
CKLDualInferenceMethod::evaluate,
NULL, obj_prt, &lbfgs_param, CKLDualInferenceMethod::adjust_step);
return nlml_opt;
}
/* 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;
}
}
float64_t CKLDualInferenceMethodMinimizer::minimize()
{
lbfgs_parameter_t lbfgs_param;
lbfgs_param.m = m_m;
lbfgs_param.max_linesearch = m_max_linesearch;
lbfgs_param.linesearch = LBFGSLineSearchHelper::get_lbfgs_linear_search(m_linesearch_id);
lbfgs_param.max_iterations = m_max_iterations;
lbfgs_param.delta = m_delta;
lbfgs_param.past = m_past;
lbfgs_param.epsilon = m_epsilon;
lbfgs_param.min_step = m_min_step;
lbfgs_param.max_step = m_max_step;
lbfgs_param.ftol = m_ftol;
lbfgs_param.wolfe = m_wolfe;
lbfgs_param.gtol = m_gtol;
lbfgs_param.xtol = m_xtol;
lbfgs_param.orthantwise_c = m_orthantwise_c;
lbfgs_param.orthantwise_start = m_orthantwise_start;
lbfgs_param.orthantwise_end = m_orthantwise_end;
init_minimization();
float64_t cost=0.0;
int error_code=lbfgs(m_target_variable.vlen, m_target_variable.vector,
&cost, CKLDualInferenceMethodMinimizer::evaluate,
NULL, this, &lbfgs_param, CKLDualInferenceMethodMinimizer::adjust_step);
if(error_code!=0 && error_code!=LBFGS_ALREADY_MINIMIZED)
{
SG_SWARNING("Error(s) happened during L-BFGS optimization (error code:%d)\n",
error_code);
}
return cost;
}
int run(Array &x)
{
double fx;
int ret = lbfgs(x.size(), x.data(), &fx, _evaluate, _progress, this, &_params);
_error_code = ret;
return ret;
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
/* Variable Declarations */
double *s, *y, *g, *H, *d, *ro, *alpha, *beta, *q, *r;
int nVars,nSteps,lhs_dims[2];
double temp;
int i,j;
/* Get Input Pointers */
g = mxGetPr(prhs[0]);
s = mxGetPr(prhs[1]);
y = mxGetPr(prhs[2]);
H = mxGetPr(prhs[3]);
/* Compute number of variables (p), rank of update (d) */
nVars = mxGetDimensions(prhs[1])[0];
nSteps = mxGetDimensions(prhs[1])[1];
/* Set-up Output Vector */
lhs_dims[0] = nVars;
lhs_dims[1] = 1;
plhs[0] = mxCreateNumericArray(2,lhs_dims,mxDOUBLE_CLASS,mxREAL);
d = mxGetPr(plhs[0]);
lbfgs(g, s, y, H, nVars, nSteps, d);
}
int learn_regression(Regression * regression){
PR * pr = (PR*)regression;
if (pr->p.method == 2){
lbfgs(pr, pr_eval, pr_grad, pr_repo, 5, pr->c, pr->p.niters, pr->x);
}
else if (pr->p.method == 1){
owlqn(pr, pr_eval, pr_grad, pr_repo, 5, pr->c, pr->p.niters, pr->p.lambda, pr->x);
}
return 0;
}
double Inference::get_bright_logprob(const int spot_index)
{
active_spot_index = spot_index;
// set E = 1 before optimize this spot
frame.E[active_spot_index] = 1;
int N = 3;
double logp;
double * x = new double[N];
if(x==NULL)
{
std::cout<<"Allocating storage FAILED!"<< "\n";
return -1;
}
for (int i=0; i<N; i++)
{
x[i] = 1.0;
}
lbfgs_parameter_t param;
lbfgs_parameter_init(¶m);
param.m = 10;
//param.epsilon = 1e-5;
param.max_iterations = 20000;
param.linesearch = LBFGS_LINESEARCH_BACKTRACKING_WOLFE;
int status = lbfgs(N,x,&logp,inner_evaluate,inner_progress,this,¶m);
if (status == 0)
{
printf("Inner L-BFGS optimization terminated with status code = %d, logp=%f\n",status, logp);
//getchar();
}
else
{
printf("Inner L-BFGS optimization terminated with status code = %d, logp=%f\n",status, logp);
getchar();
}
// make sure to set E = 0 before exit to ensure that
// we don't the bright spot set. However, it will change
// the parameters A, B, and phi.
frame.E[active_spot_index] = 0;
// double logp_piece;
// double logp = 0.0;
// for(std::vector<Evidence>::iterator iter = evidence_list.begin();
// iter != evidence_list.end(); iter++)
// {
// logp_piece =
// iter->s[active_spot_index] * log(frame.mu[active_spot_index])
// - frame.mu[active_spot_index];
// logp = logp + logp_piece;
// }
return logp;
}
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;
};
int main(int argc, char* argv[]) {
int i, ret = 0;
double fx;
double* x = vecalloc(N);
lbfgs_parameter_t param;
/* Initialize the variables. */
for (i = 0; i < N; i += 2) {
x[i] = -1.2;
x[i + 1] = 1.0;
}
lbfgs_default_parameter(¶m);
param.orthantwise_c = 1.0;
param.linesearch = LBFGS_LINESEARCH_BACKTRACKING_WOLFE;
ret = lbfgs(N, x, &fx, evaluate, 0, 0, ¶m);
vecfree(x);
printf("L-BFGS optimization terminated with status code = %d\n", ret);
return ret;
}
int
main(int argc, char* argv[])
{
int i, n, status;
nlp_float *x;
function_object func_obj;
linesearch_parameter ls_parameter;
lbfgs_parameter parameter;
n = 10;
x = (nlp_float *)malloc_vec(n * sizeof(nlp_float));
for (i = 0; i < n; ++i)
x[i] = 1.;
func_obj.func = func;
func_obj.grad = grad;
default_lbfgs_parameter(¶meter);
default_linesearch_parameter(&ls_parameter);
status = lbfgs(
x,
n,
&func_obj,
&ls_parameter,
¶meter
);
if (LBFGS_SATISFIED == status)
{
printf("\n=== Optimal x =========================================\n");
for (i = 0; i < n; ++i)
printf(" x_%-8d = %12.6e\n", i + 1 , x[i]);
printf("=======================================================\n");
}
free_vec(x);
return 0;
}
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);
}
void testSoftICADriver()
{
const int numPatches = 200000; // 200000 10000
const int patchWidth = 8;
MNISTSamplePatchesDataFunction mnistdf(numPatches, patchWidth);
Config config;
config.setValue("addBiasTerm", false);
config.setValue("meanStddNormalize", false);
config.setValue("configurePolicyTesting", false);
config.setValue("trainingMeanAndStdd", false);
updateMNISTConfig(config);
const int numFeatures = 50;
const double lambda = 0.0005f;
const double epsilon = 1e-2;
SoftICACostFunction sfc(numFeatures, lambda, epsilon);
LIBLBFGSOptimizer lbfgs(1000);
Driver drv(&config, &mnistdf, &sfc, &lbfgs);
drv.drive();
}
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;
}
void testStlDriver()
{
const int numPatches = 200000; // 200000
const int patchWidth = 9;
const int numFeatures = 50;
const double lambda = 0.0005f;
const double epsilon = 1e-2;
Config config;
config.setValue("addBiasTerm", false);
config.setValue("meanStddNormalize", false);
config.setValue("configurePolicyTesting", false);
config.setValue("trainingMeanAndStdd", false);
updateMNISTConfig(config);
if (false)
{
MNISTSamplePatchesUnlabeledDataFunction mnistUnlabeled(numPatches, patchWidth);
SoftICACostFunction sfc(numFeatures, lambda, epsilon);
LIBLBFGSOptimizer lbfgs(200); // 1000
Driver drv1(&config, &mnistUnlabeled, &sfc, &lbfgs);
const Vector_t optThetaRica = drv1.drive();
Matrix_t Wrica(
Eigen::Map<const Matrix_t>(optThetaRica.data(), numFeatures, pow(patchWidth, 2)));
std::ofstream ofs_wrica("../W2.txt");
ofs_wrica << Wrica << std::endl;
}
Matrix_t Wrica;
// debug: read off the values
std::ifstream in("/home/sam/School/online/stanford_dl_ex/W2.txt");
if (in.is_open())
{
std::string str;
int nbRows = 0;
while (std::getline(in, str))
{
if (str.size() == 0)
continue;
std::istringstream iss(str);
std::vector<double> tokens //
{ std::istream_iterator<double> { iss }, std::istream_iterator<double> { } };
Wrica.conservativeResize(nbRows + 1, tokens.size());
for (size_t i = 0; i < tokens.size(); ++i)
Wrica(nbRows, i) = tokens[i];
++nbRows;
}
}
else
{
std::cerr << "file W.txt failed" << std::endl;
exit(EXIT_FAILURE);
}
const int imageDim = 28;
Eigen::Vector2i imageConfig;
imageConfig << imageDim, imageDim;
const int numFilters = numFeatures;
const int poolDim = 5;
const int filterDim = patchWidth;
const int convDim = (imageDim - filterDim + 1);
assert(convDim % poolDim == 0);
const int outputDim = (convDim / poolDim);
StlFilterFunction stlFilterFunction(filterDim, Wrica);
SigmoidFunction sigmoidFunction;
ConvolutionFunction convolutionFunction(&stlFilterFunction, &sigmoidFunction);
MeanPoolFunction meanPoolFunction(numFilters, outputDim);
MNISTSamplePatchesLabeledDataFunction mnistLabeled(&convolutionFunction, &meanPoolFunction,
imageConfig, numFilters, poolDim, outputDim);
SoftmaxCostFunction mnistcf(0.01f);
LIBLBFGSOptimizer lbfgs2(300);
config.setValue("configurePolicyTesting", false);
config.setValue("trainingMeanAndStdd", true);
config.setValue("meanStddNormalize", true);
config.setValue("addBiasTerm", true);
config.setValue("numGrd", true);
config.setValue("training_accuracy", true);
config.setValue("testing_accuracy", true);
//config.setValue("addBiasTerm", false);
Driver drv2(&config, &mnistLabeled, &mnistcf, &lbfgs2);
drv2.drive();
}
void EstimatePairModelMAP(numeric_t *x, numeric_t *lambdas, alignment_t *ali,
options_t *options) {
/* Computes Maximum a posteriori (MAP) estimates for the parameters of
and undirected graphical model by L-BFGS */
/* Start timer */
gettimeofday(&ali->start, NULL);
/* Initialize L-BFGS */
lbfgs_parameter_t param;
lbfgs_parameter_init(¶m);
param.epsilon = 1E-3;
param.max_iterations = options->maxIter; /* 0 is unbounded */
/* Array of void pointers provides relevant data structures */
void *d[3] = {(void *)ali, (void *)options, (void *)lambdas};
/* Estimate parameters by optimization */
static lbfgs_evaluate_t algo;
switch(options->estimatorMAP) {
case INFER_MAP_PLM:
algo = PLMNegLogPosterior;
break;
case INFER_MAP_PLM_GAPREDUCE:
algo = PLMNegLogPosteriorGapReduce;
break;
case INFER_MAP_PLM_BLOCK:
algo = PLMNegLogPosteriorBlock;
break;
case INFER_MAP_PLM_DROPOUT:
algo = PLMNegLogPosteriorDO;
break;
default:
algo = PLMNegLogPosterior;
}
if (options->zeroAPC == 1) fprintf(stderr,
"Estimating coupling hyperparameters le = 1/2 inverse variance\n");
int ret = 0;
lbfgsfloatval_t fx;
ret = lbfgs(ali->nParams, x, &fx, algo, ReportProgresslBFGS,
(void*)d, ¶m);
fprintf(stderr, "Gradient optimization: %s\n", LBFGSErrorString(ret));
/* Optionally re-estimate parameters with adjusted hyperparameters */
if (options->zeroAPC == 1) {
/* Form new priors on the variances */
ZeroAPCPriors(ali, options, lambdas, x);
/* Reinitialize coupling parameters */
for (int i = 0; i < ali->nSites - 1; i++)
for (int j = i + 1; j < ali->nSites; j++)
for (int ai = 0; ai < ali->nCodes; ai++)
for (int aj = 0; aj < ali->nCodes; aj++)
xEij(i, j, ai, aj) = 0.0;
/* Iterate estimation with new hyperparameter estimates */
options->zeroAPC = 2;
ret = lbfgs(ali->nParams, x, &fx, algo,
ReportProgresslBFGS, (void*)d, ¶m);
fprintf(stderr, "Gradient optimization: %s\n", LBFGSErrorString(ret));
}
}
/**
* @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);
}