function regularize( weights, lambda, eta ) {
	var scalingFactor;
	if ( lambda > 0.0 ) {
		scalingFactor = 1.0 - ( eta * lambda );
		weights.scaleTo( max( scalingFactor, MIN_SCALING_FACTOR ) );
	}
}
L2 regularization of feature weights.

@private
@param {WeightVector} weights - current model coefficients
@param {NonNegativeNumber} lambda - regularization parameter
@param {PositiveNumber} eta - current learning rate