/**
* Main parser for a tdf file. Parses initialization data, creates the header and
* source files, and passes parameter definitions to the appropriate function.
*
* @param fp file to parse
*
* @return 0 on success, -1 on error
*/
int parseTemplateDefinitionFile(File &fp)
{
TemplateDefinitionFile tdfFile;
int result = tdfFile.parse(fp);
if (result != 0)
return result;
if (tdfFile.getTemplateName() == ROOT_TEMPLATE_NAME)
{
fprintf(stderr, "Warning: will not generate C++ code for class %s, "
"invalid template name\n", tdfFile.getTemplateName().c_str());
return -1;
}
tdfFile.setWriteForCompiler(false);
result = writeTemplate(tdfFile, tdfFile.getPath());
if (result == 0)
{
tdfFile.setWriteForCompiler(true);
result = writeTemplate(tdfFile, tdfFile.getCompilerPath());
}
return result;
} // parseTemplateDefinitionFile
int
_evlWriteTemplate(const template_t *t, const char *path)
{
tfile_t *tf;
size_t fileSize;
int fd;
int nBytes;
tf = allocTfile(path, 1000);
writeTemplate(t, tf);
fileSize = tf->tf_next - tf->tf_buf;
fd = open(path, O_WRONLY|O_CREAT|O_TRUNC, 0644);
if (fd < 0) {
perror(path);
freeTfile(tf);
return -1;
}
nBytes = safeWrite(fd, tf->tf_buf, fileSize);
assert(nBytes == fileSize);
freeTfile(tf);
(void) close(fd);
return 0;
}
void KisTemplateTree::writeTemplateTree()
{
QString localDir = KoResourcePaths::saveLocation("data", m_templatesResourcePath);
Q_FOREACH (KisTemplateGroup *group, m_groups) {
//dbgUI <<"---------------------------------";
//dbgUI <<"group:" << group->name();
bool touched = false;
QList<KisTemplate*> templates = group->templates();
QList<KisTemplate*>::iterator it = templates.begin();
for (; it != templates.end() && !touched && !group->touched(); ++it)
touched = (*it)->touched();
if (group->touched() || touched) {
//dbgUI <<"touched";
if (!group->isHidden()) {
//dbgUI <<"not hidden";
QDir path;
path.mkpath(localDir + group->name()); // create the local group dir
} else {
//dbgUI <<"hidden";
if (group->dirs().count() == 1 && group->dirs().contains(localDir)) {
//dbgUI <<"local only";
QFile f(group->dirs().first());
f.remove();
//dbgUI <<"removing:" << group->dirs().first();
} else {
//dbgUI <<"global";
QDir path;
path.mkpath(localDir + group->name());
}
}
}
Q_FOREACH (KisTemplate *t, templates) {
if (t->touched()) {
//dbgUI <<"++template:" << t->name();
writeTemplate(t, group, localDir);
}
if (t->isHidden() && t->touched()) {
//dbgUI <<"+++ delete local template ##############";
writeTemplate(t, group, localDir);
QFile::remove(t->file());
QFile::remove(t->picture());
}
}
}
bool VCSharedProject::writeProject() const
{
LabelHandlerFnMap nodeHandlers;
nodeHandlers["GlobalProperties"] = &VCSharedProject::writeGlobalProperties;
nodeHandlers["ProjectItems"] = &VCSharedProject::writeProjectItems;
return writeTemplate(getPath(), nodeHandlers);
}
void KoTemplateTree::writeTemplateTree()
{
const QString localDir = KoResourcePaths::saveLocation("data", m_templatesResourcePath);
foreach (KoTemplateGroup *group, m_groups) {
//kDebug( 30003 ) <<"---------------------------------";
//kDebug( 30003 ) <<"group:" << group->name();
bool touched = false;
QList<KoTemplate*> templates = group->templates();
QList<KoTemplate*>::iterator it = templates.begin();
for (; it != templates.end() && !touched && !group->touched(); ++it)
touched = (*it)->touched();
if (group->touched() || touched) {
//kDebug( 30003 ) <<"touched";
if (!group->isHidden()) {
//kDebug( 30003 ) <<"not hidden";
QDir().mkpath(localDir + group->name()); // create the local group dir
} else {
//kDebug( 30003 ) <<"hidden";
if (group->dirs().count() == 1 && group->dirs().contains(localDir)) {
//kDebug( 30003 ) <<"local only";
KIO::NetAccess::del(QUrl::fromLocalFile(group->dirs().first()), 0);
//kDebug( 30003 ) <<"removing:" << group->dirs().first();
} else {
//kDebug( 30003 ) <<"global";
QDir().mkpath(localDir + group->name());
}
}
}
foreach (KoTemplate *t, templates) {
if (t->touched()) {
//kDebug( 30003 ) <<"++template:" << t->name();
writeTemplate(t, group, localDir);
}
if (t->isHidden() && t->touched()) {
//kDebug( 30003 ) <<"+++ delete local template ##############";
writeTemplate(t, group, localDir);
QFile::remove(t->file());
QFile::remove(t->picture());
}
}
}
bool VCProject::writeFilters() const
{
std::string filtersFilePath = getPath() + ".filters";
LabelHandlerFnMap nodeHandlers;
nodeHandlers["FilterItemDescriptions"] = &VCProject::writeFilterItemDescriptions;
nodeHandlers["FilterDescriptions"] = &VCProject::writeFilterDescriptions;
return writeTemplate(filtersFilePath, nodeHandlers);
}
bool VCProject::writeProject() const
{
LabelHandlerFnMap nodeHandlers;
nodeHandlers["ProjectConfigSummary"] = &VCProject::writeProjectConfigSummary;
nodeHandlers["GlobalProperties"] = &VCProject::writeGlobalProperties;
nodeHandlers["ConfigurationProperties"] = &VCProject::writeConfigurationProperties;
nodeHandlers["BuildExtensionProperties"] = &VCProject::writeBuildExtensionProperties;
nodeHandlers["BuildExtensionTargets"] = &VCProject::writeBuildExtensionTargets;
nodeHandlers["SharedProjects"] = &VCProject::writeSharedProjects;
nodeHandlers["ConfigurationPropertySheets"] = &VCProject::writeConfigurationPropertySheets;
nodeHandlers["UserMacros"] = &VCProject::writeUserMacros;
nodeHandlers["ConfigurationItemDefinitions"] = &VCProject::writeConfigurationItemDefinitions;
nodeHandlers["ProjectItems"] = &VCProject::writeProjectItems;
nodeHandlers["ProjectReferences"] = &VCProject::writeProjectReferences;
return writeTemplate(getPath(), nodeHandlers);
}
void TextOutputStream::write(long double source) const
{
writeTemplate(source);
}
void TextOutputStream::write(float source) const
{
writeTemplate(source);
}
void TextOutputStream::write(uint64 source) const
{
writeTemplate(source);
}
void TextOutputStream::write(const void* source) const
{
writeTemplate(source);
}
void TextOutputStream::write(bool source) const
{
writeTemplate(source);
}
int ljocr::MQDFClassification::train(std::string character, std::vector< std::vector<float> >& features, float ratio, std::string path) {
Mat samples(Size(features.front().size(), features.size()), CV_32F, cv::Scalar(0));
for (int j = 0; j < features.size(); ++j) {
vector<float> feature = features[j];
for (int i = 0; i < feature.size(); ++i) {
samples.at<float>(j, i) = feature[i];
}
}
#if _DEBUG
//Mat_<double> samples =
//(Mat_<double>(3, 2) <<
//2.0, 2.0, 2.0, 5.0, 6.0, 5.0);
#endif
Mat cov, mean;
calcCovarMatrix(samples, cov, mean, CV_COVAR_NORMAL | CV_COVAR_ROWS);
MQDFParam mqdf_param;
//mqdf_param.mean = mean;
mean.convertTo(mean, CV_32F);
mqdf_param.mean = mean.clone();
cov /= (samples.rows - 1); //cov/(N-1)
SVD svd;
Mat w, u, vt;
svd.compute(cov, w, u, vt, SVD::FULL_UV);
#if _DEBUG
//Mat_<double> cov1 =
//(Mat_<double>(3, 3) <<
//2.0, 1.0, 0.0, 1.0, 4.0, 5.0, 0, 5, 3);
//svd.compute(cov1, w, u, vt, SVD::FULL_UV);
#endif
double sum_w = 0;
for (int j = 0; j < w.rows; ++j) {
sum_w += w.at<double>(j, 0);
}
mqdf_param.principle_dim = 0;
double log_lamda = 0;
if (ratio < 1.0) {
int j;
double temp_sum = 0;
for (j = 0; j < w.rows; ++j) {
double lamda_temp = w.at<double>(j, 0);
temp_sum += w.at<double>(j, 0);
if (temp_sum / sum_w > ratio) {
break;
}
log_lamda += log(lamda_temp);
}
mqdf_param.principle_dim = j;
} else {
mqdf_param.principle_dim = ratio;
for (int j = 0; j < mqdf_param.principle_dim; ++j) {
double lamda_temp = w.at<double>(j, 0);
log_lamda += log(lamda_temp);
}
}
float principle_eigenvalue_sum = 0;
for (int j = 0; j < mqdf_param.principle_dim; ++j) {
principle_eigenvalue_sum += w.at<double>(j, 0);
}
//mqdf_param.delta = w.at<double>(mqdf_param.principle_dim, 0);
//mqdf_param.delta = (sum_w - principle_eigenvalue_sum) / (w.rows - mqdf_param.principle_dim); //ML estimation value, the performance of generation is not good
//mqdf_param.remainder = log_lamda + (w.rows - mqdf_param.principle_dim)*log(mqdf_param.delta);
//lj: all classes share the same delta, so the delta in remainder is ignored
#ifdef CROSS_VALIDATION
mqdf_param.delta = sum_w / w.rows; //prepare for cross validation
#else
//mqdf_param.delta = 27.6436; //the value is estimated by cross_validation for 300 samples
//mqdf_param.delta = 26.2426969; //the value is estimated by cross_validation for 1000 samples
//mqdf_param.delta = 32.6721689; //the value is estimated by cross_validation for 3755 samples
//mqdf_param.delta = 64.919496*0.7; //the value is estimated by cross_validation for 3755 samples
//mqdf_param.delta = 1.896899*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF
//mqdf_param.delta = 1.361794*0.7; //the value is estimated by cross_validation for 500 samples. best for MPF
//mqdf_param.delta = 23.766482*0.7; //the value is estimated by cross_validation for 3755 samples
//mqdf_param.delta = 0.960328*0.7; //the value is estimated by cross_validation for 500 samples. best for MPF class 0
//mqdf_param.delta = 1.114572*0.7; //the value is estimated by cross_validation for 500 samples. best for MPF class 1
//mqdf_param.delta = 0.997763*0.7; //the value is estimated by cross_validation for 500 samples. best for MPF class 1
//mqdf_param.delta = 1.604470*0.7; //the value is estimated by cross_validation for 1000 samples. best for MPF
//mqdf_param.delta = 1.339773*0.7; //the value is estimated by cross_validation for 1000 samples. best for MPF class 0
//mqdf_param.delta = 1.228499*0.7; //the value is estimated by cross_validation for 1000 samples. best for MPF class 1
//mqdf_param.delta = 1.677936*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 0
//mqdf_param.delta = 1.675047*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 1
//mqdf_param.delta = 1.541195*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 2, 3 class
//mqdf_param.delta = 1.571957*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 1, 3 class
//mqdf_param.delta = 1.52195*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 0, 3 class
//mqdf_param.delta = 1.385178*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 4, 5 class
//mqdf_param.delta = 1.430945*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 3, 5 class
//mqdf_param.delta = 1.249550*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 2, 5 class
//mqdf_param.delta = 1.322204*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 1, 5 class
//mqdf_param.delta = 1.358019*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 0, 5 class
//mqdf_param.delta = 1.496428*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 0, 5 class pca95
//mqdf_param.delta = 1.435780*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 1, 5 class pca95
//mqdf_param.delta = 1.333554*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 2, 5 class pca95
//mqdf_param.delta = 1.287792*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 3, 5 class pca95
mqdf_param.delta = 1.309553*0.7; //the value is estimated by cross_validation for 3755 samples. best for MPF class 4, 5 class pca95
#endif
mqdf_param.remainder = log_lamda;
//lj end
Rect rect(0, 0, 1, mqdf_param.principle_dim);
cv::Mat principle_eigenvalue = w(rect).clone();
principle_eigenvalue.convertTo(principle_eigenvalue, CV_32F);
mqdf_param.principle_eigenvalue = principle_eigenvalue;
rect.x = 0;
rect.y = 0;
rect.height = u.rows;
rect.width = mqdf_param.principle_dim;
cv::Mat principle_eigenvector = u(rect).clone();
principle_eigenvector.convertTo(principle_eigenvector, CV_32F);
mqdf_param.principle_eigenvector = principle_eigenvector;
#ifdef CROSS_VALIDATION
character_mqdf_param_.insert(std::make_pair(character, mqdf_param));
#else
writeTemplate(character, mqdf_param, path);
#endif
return 0;
}
