liblinear.cpp
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#include <QTemporaryFile>
#include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp>
#include "openbr_internal.h"
#include "openbr/core/opencvutils.h"
#include <linear.h>
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
using namespace cv;
namespace br
{
class Linear : public Transform
{
Q_OBJECT
Q_ENUMS(Solver)
Q_PROPERTY(Solver solver READ get_solver WRITE set_solver RESET reset_solver STORED false)
Q_PROPERTY(float C READ get_C WRITE set_C RESET reset_C STORED false)
Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
Q_PROPERTY(bool returnDFVal READ get_returnDFVal WRITE set_returnDFVal RESET reset_returnDFVal STORED false)
Q_PROPERTY(bool overwriteMat READ get_overwriteMat WRITE set_overwriteMat RESET reset_overwriteMat STORED false)
public:
enum Solver { L2R_LR,
L2R_L2LOSS_SVC_DUAL,
L2R_L2LOSS_SVC,
L2R_L1LOSS_SVC_DUAL,
MCSVM_CS,
L1R_L2LOSS_SVC,
L1R_LR,
L2R_LR_DUAL,
L2R_L2LOSS_SVR,
L2R_L2LOSS_SVR_DUAL,
L2R_L1LOSS_SVR_DUAL };
private:
BR_PROPERTY(Solver, solver, L2R_L2LOSS_SVC_DUAL)
BR_PROPERTY(float, C, 1)
BR_PROPERTY(QString, inputVariable, "Label")
BR_PROPERTY(QString, outputVariable, "")
BR_PROPERTY(bool, returnDFVal, false)
BR_PROPERTY(bool, overwriteMat, true)
model *m;
void train(const TemplateList &data)
{
Mat samples = OpenCVUtils::toMat(data.data());
Mat labels = OpenCVUtils::toMat(File::get<float>(data, inputVariable));
problem prob;
prob.n = samples.cols;
prob.l = samples.rows;
prob.bias = -1;
prob.y = new double[prob.l];
for (int i=0; i<prob.l; i++)
prob.y[i] = labels.at<float>(i,0);
// Allocate enough memory for l feature_nodes pointers
prob.x = new feature_node*[prob.l];
feature_node *x_space = new feature_node[(prob.n+1)*prob.l];
int k = 0;
for (int i=0; i<prob.l; i++) {
prob.x[i] = &x_space[k];
for (int j=0; j<prob.n; j++) {
x_space[k].index = j+1;
x_space[k].value = samples.at<float>(i,j);
k++;
}
x_space[k++].index = -1;
}
parameter param;
// TODO: Support grid search
param.C = C;
param.eps = FLT_EPSILON;
param.solver_type = solver;
// TODO: Support weights
param.nr_weight = 0;
param.p = 1;
param.weight_label = NULL;
param.weight = NULL;
m = train_svm(&prob, ¶m);
delete[] prob.y;
delete[] prob.x;
delete[] x_space;
}
void project(const Template &src, Template &dst) const
{
dst = src;
Mat sample = src.m().reshape(1,1);
feature_node *x_space = new feature_node[sample.cols+1];
for (int j=0; j<sample.cols; j++) {
x_space[j].index = j+1;
x_space[j].value = sample.at<float>(0,j);
}
x_space[sample.cols].index = -1;
float prediction;
double prob_estimates[m->nr_class];
if (solver == L2R_L2LOSS_SVR ||
solver == L2R_L1LOSS_SVR_DUAL ||
solver == L2R_L2LOSS_SVR_DUAL ||
solver == L2R_L2LOSS_SVC_DUAL ||
solver == L2R_L2LOSS_SVC ||
solver == L2R_L1LOSS_SVC_DUAL ||
solver == MCSVM_CS ||
solver == L1R_L2LOSS_SVC)
{
prediction = predict_values(m,x_space,prob_estimates);
if (returnDFVal) prediction = prob_estimates[0];
} else if (solver == L2R_LR ||
solver == L2R_LR_DUAL ||
solver == L1R_LR)
{
prediction = predict_probability(m,x_space,prob_estimates);
if (returnDFVal) prediction = prob_estimates[0];
}
if (overwriteMat) {
dst.m() = Mat(1, 1, CV_32F);
dst.m().at<float>(0, 0) = prediction;
} else {
dst.file.set(outputVariable,prediction);
}
delete[] x_space;
}
void store(QDataStream &stream) const
{
QString filename = QString::number(qrand());
stream << filename;
save_model(filename.toStdString().c_str(),m);
}
void load(QDataStream &stream)
{
QString filename;
stream >> filename;
m = load_model(filename.toStdString().c_str());
}
};
BR_REGISTER(Transform, Linear)
} // namespace br
#include "liblinear.moc"