AMRules and RandomAMRules

-AMRules now uses weights for examples in all its extent.
-New: RandomAMRules  ensemble method. Uses bagging to perturb training set (using AMRules support for weights), and at each rule expansion a different subset of the attributes is used.
-Fixed minor RandomRules bugs.

Main difference between RandomRules and RandomAMRules: each model of the former sees a different subset of the attributes while the latter uses different subset of the attributes for expanding each rule.