@Override
protected float getAlpha(PredictionResult margin) {
float m = margin.getScore();
float var = margin.getVariance();
float squared_phi = phi * phi;
float n = var + c / 2.f;
float v_phi_phi = var * squared_phi;
float v_phi_phi_m = v_phi_phi * m;
float term = v_phi_phi_m * m * var + 4.f * n * var * (n + v_phi_phi);
float gamma = phi * (float) Math.sqrt(term);
float alpha_numer = -(2.f * m * n + v_phi_phi_m) + gamma;
if (alpha_numer <= 0.f) {
return 0.f;
}
float alpha_denom = 2.f * (n * n + n * v_phi_phi);
if (alpha_denom == 0.f) {
return 0.f;
}
float alpha = alpha_numer / alpha_denom;
return Math.max(0.f, alpha);
}
@Override
protected float getAlpha(PredictionResult margin) {
float m = margin.getScore();
float var = margin.getVariance();
float alpha_numer =
-m * psi
+ (float)
Math.sqrt((m * m * squared_phi * squared_phi / 4.f) + (var * squared_phi * zeta));
float alpha_denom = var * zeta;
if (alpha_denom == 0.f) {
return 0.f;
}
float alpha = alpha_numer / alpha_denom;
if (alpha <= 0.f) {
return 0.f;
}
return Math.max(c, alpha);
}
@Override
protected float getBeta(PredictionResult margin, float alpha) {
if (alpha == 0.f) {
return 0.f;
}
float var = margin.getVariance();
float beta_numer = alpha * phi;
float var_alpha_phi = var * beta_numer;
float u = -var_alpha_phi + (float) Math.sqrt(var_alpha_phi * var_alpha_phi + 4.f * var);
float beta_den = u / 2.f + var_alpha_phi;
if (beta_den == 0.f) {
return 0.f;
}
float beta = beta_numer / beta_den;
return beta;
}
protected float loss(PredictionResult margin, float y) {
float var = margin.getVariance();
float mean = margin.getScore();
float loss = phi * (float) Math.sqrt(var) - (y * mean);
return Math.max(loss, 0.f);
}
