protected void doOnePassBinomial(
double[][] x,
double[] theta,
double[] y,
final double tl2,
double[] w,
double[] pTrain,
double[] rTrain) {
for (int j = 0; j < x.length; j++) {
if (Math.abs(theta[j]) <= MathUtils.EPSILON) {
continue;
}
double[] v = x[j];
double eta = VectorUtils.dotProduct(rTrain, v);
double wNew = (w[j] * theta[j] + eta) / (theta[j] + tl2);
double delta = wNew - w[j];
w[j] = wNew;
// Update predictions
for (int i = 0; i < pTrain.length; i++) {
pTrain[i] += delta * v[i];
rTrain[i] = OptimUtils.getPseudoResidual(pTrain[i], y[i]);
}
}
}
protected void doOnePassGaussian(
double[][] x, double[] sq, final double tl2, double[] w, double[] rTrain) {
for (int j = 0; j < x.length; j++) {
double[] v = x[j];
// Calculate weight updates using naive updates
double eta = VectorUtils.dotProduct(rTrain, v);
double wNew = (w[j] * sq[j] + eta) / (sq[j] + tl2);
double delta = wNew - w[j];
w[j] = wNew;
// Update residuals
for (int i = 0; i < rTrain.length; i++) {
rTrain[i] -= delta * v[i];
}
}
}
