add HingeLossLayer for one-vs-all hinge loss

This layer implements a "one-vs-all" hinge loss, (1/n) sum_ij max(0, 1 -
y_ij x_ij), with bottom blob x_ij (i ranging over examples and j over
classes), and y_ij = +1/-1 indicating the label. No regularization is
included, since regularization is done via weight decay or using the
parameters of another layer. The gradient is taken to be zero at the
hinge point. This commit only provides the CPU implementation.
make gradient checker's kink use feature absolute value

In theory, layer functions could be nonsmooth anywhere; in all cases in
use so far, they are nonsmooth at either zero or +1 and -1. In the
future, it might be necessary to generalize the kink mechanism beyond
this stopgap measure.
test HingeLossLayer

Based on SoftmaxWithLossLayerTest.
Merge pull request #303 from longjon/hinge-loss-layer

HingeLossLayer