void passive_aggressive_2::train(const common::sfv_t& sfv,
const string& label) {
check_touchable(label);
labels_.get_model()->increment(label);
string incorrect_label;
float margin = calc_margin(sfv, label, incorrect_label);
float loss = 1.f + margin;
if (loss < 0.f) {
storage_->register_label(label);
return;
}
float sfv_norm = squared_norm(sfv);
if (sfv_norm == 0.f) {
storage_->register_label(label);
return;
}
update_weight(
sfv,
loss / (2 * sfv_norm + 1 / (2 * config_.regularization_weight)),
label,
incorrect_label);
touch(label);
}
void NXVectorTest::squared_normTest(void)
{
double pData[4] = { 5.0, 6.0, 7.0, 8.0 };
NXVectorRef4d p(pData);
double const pSquaredNorm = squared_norm(p);
double const answer = (pData[0] * pData[0] +
pData[1] * pData[1] +
pData[2] * pData[2] +
pData[3] * pData[3]);
CPPUNIT_ASSERT(pSquaredNorm == answer);
}
void PA::train(const sfv_t& sfv, const string& label){
string incorrect_label;
float margin = calc_margin(sfv, label, incorrect_label);
float loss = 1.f + margin;
if (loss < 0.f){
return;
}
float sfv_norm = squared_norm(sfv);
if (sfv_norm == 0.f) {
return;
}
update_weight(sfv, loss / sfv_norm, label, incorrect_label);
}
void passive_aggressive_2::train(const common::sfv_t& sfv,
const string& label) {
string incorrect_label;
float margin = calc_margin(sfv, label, incorrect_label);
float loss = 1.f + margin;
if (loss < 0.f) {
return;
}
float sfv_norm = squared_norm(sfv);
if (sfv_norm == 0.f) {
return;
}
update_weight(
sfv, loss / (2 * sfv_norm + 1 / (2 * config_.C)), label, incorrect_label);
}
void quaternion_t::rectify()
{
quaternion_t temp_q = *this;
if( fabs( squared_norm() - 1 ) > PRX_ZERO_CHECK )
normalize();
if(is_approximate_equal(temp_q))
*this = temp_q;
// double theta = 2.0 * acos( q[3] );
// double d = sin(theta*.5);
// if( d == 0 )
// {
// q[0] = q[1] = q[2] = 0;
// q[3] = 1;
// }
// else
// {
// double ax = q[0]/d;
// double ay = q[1]/d;
// double az = q[2]/d;
// double norm = sqrt( ax*ax + ay*ay + az*az );
// if( fabs( norm - 1 ) < PRX_ZERO_CHECK )
// {
// ax /= norm;
// ay /= norm;
// az /= norm;
// q[0] = sin( theta / 2.0 ) * ax;
// q[1] = sin( theta / 2.0 ) * ay;
// q[2] = sin( theta / 2.0 ) * az;
// }
// else
// {
// q[0] = 0.0;
// q[1] = 0.0;
// q[2] = 0.0;
// q[3] = 1.0;
// }
// }
}
void passive_aggressive_1::train(const common::sfv_t& sfv,
const string& label) {
string incorrect_label;
float margin = calc_margin(sfv, label, incorrect_label);
float loss = 1.f + margin;
if (loss < 0.f) {
storage_->register_label(label);
return;
}
float sfv_norm = squared_norm(sfv);
if (sfv_norm == 0.f) {
storage_->register_label(label);
return;
}
update_weight(
sfv, min(config_.C, loss / (2 * sfv_norm)), label, incorrect_label);
touch(label);
}
void passive_aggressive::train(const common::sfv_t& fv, float value) {
sum_ += value;
sq_sum_ += value * value;
count_ += 1;
float avg = sum_ / count_;
float std_dev = std::sqrt(sq_sum_ / count_ - avg * avg);
float predict = estimate(fv);
float error = value - predict;
float sign_error = error > 0.f ? 1.0f : -1.0f;
float loss = sign_error * error - config_.sensitivity * std_dev;
if (loss > 0.f) {
float coeff = sign_error * loss / squared_norm(fv);
if (!std::isinf(coeff)) {
update(fv, coeff);
}
}
}
double vector_t::norm() const
{
return sqrt( squared_norm() );
}
double quaternion_t::norm() const
{
return sqrt( squared_norm() );
}
void inverse()
{
conjugate();
*this /= squared_norm();
}
T norm() const
{
return cmath<T>::sqrt( squared_norm());
}
