def expected_squared_hinge(target, prediction):
target = 2 * target - 1
pred_mean, pred_var = unpack_mean_var(prediction)
mean_unsqrd, var_unsqrd = transfer.rectifier(
margin - target * pred_mean, pred_var)
# See Murphy, "Machine Learning: A Probabilist Perspective", Eq (2.26)
# for the following step.
mean = var_unsqrd + mean_unsqrd ** 2
return mean
def expected_squared_hinge(target, prediction):
target = 2 * target - 1
pred_mean, pred_var = unpack_mean_var(prediction)
mean_unsqrd, var_unsqrd = transfer.rectifier(
margin - target * pred_mean, pred_var)
# See Murphy, "Machine Learning: A Probabilist Perspective", Eq (2.26)
# for the following step.
mean = var_unsqrd + mean_unsqrd ** 2
return mean
def expected_hinge(target, prediction):
target = 2 * target - 1
pred_mean, pred_var = unpack_mean_var(prediction)
mean, _ = transfer.rectifier(margin - target * pred_mean, pred_var)
return mean
def expected_hinge(target, prediction):
target = 2 * target - 1
pred_mean, pred_var = unpack_mean_var(prediction)
mean, _ = transfer.rectifier(margin - target * pred_mean, pred_var)
return mean
