@Override
    public double dist(Vec a, Vec b)
    {
        return kf.eval(a, a) - 2*kf.eval(a, b) + kf.eval(b, b);
    }
@ Override public double dist ( Vec a , Vec b ) { return kf . eval ( a , a ) - 2 * kf . eval ( a , b ) + kf . eval ( b , b ) ; }
Returns the square of the distance function expanded as kernel methods.
<br>
d<sup>2</sup>(x,y) = K(x,x) - 2*K(x,y) + K(y,y)

<br><br>
Special Notes:<br>
The use of {@link RBFKernel} or {@link PolynomialKernel} of degree 1
in the {@link NearestNeighbour} classifier will degenerate into the
normal nearest neighbor algorithm.

@param a the first vector
@param b the second vector
@return the distance metric based on a kernel function