Method update

JSAT/src/jsat/classifiers/linear/kernelized/OSKL.java:320–353

(DataPoint dataPoint, int targetClass)

    }

    @Override
    public void update(DataPoint dataPoint, int targetClass)
    {
        final Vec x_t = dataPoint.getNumericalValues();
        final List<Double> qi = k.getQueryInfo(x_t);
        final double score = scoreSaveEval(x_t, qi);
        final double y_t = targetClass*2-1;
        //4: Compute the derivative ℓ′(yt, ft(xt))
        final double lossD = lossC.getDeriv(score, y_t);
        t++;
        // Step 5: Sample a binary random variable Zt with
        if(rand.nextDouble() > Math.abs(lossD)/G)
            return;//"failed", no update
        final double alpha_t = -eta*Math.signum(lossD)*G;
        //Update the squared norm 
        curSqrdNorm += alpha_t*alpha_t*inputKEvals.getD(0);
        for(int i = 0; i < alphas.size(); i++)
            curSqrdNorm += 2*alpha_t*alphas.getD(i)*inputKEvals.getD(i+1);
        //add values
        alphas.add(alpha_t);
        vecs.add(x_t);
        if(accelCache != null)
            accelCache.addAll(qi);
        //update online alpha averages for current & old SVs
        alphaAveraged.add(0.0);//implicit zero for time we didn't have new SVs
        updateAverage();
        //project alphas to maintain norm if needed
        if(curSqrdNorm > R*R)
        {
            double coeff = R/Math.sqrt(curSqrdNorm);
            alphas.getVecView().mutableMultiply(coeff);
            curSqrdNorm *= coeff*coeff;
        }
    };

    private double score(Vec x, List<Double> qi)
    {