Weight vector

VW's weight vector has (2^b) weights (where (b) is specified by the -b option) and each example's features are hash to an index in ([0,2^b-1]). The weight vector is also used to store other vectors needed by more sophisticated learning algorithms, such as the conjugate gradient method (--conjugate_gradient), or adaptive gradient descent (--adaptive and/or --exact_adaptive_norm).

When more than one vector is stored in the same global (2^b) space, every hash-value slot will store two (or more) "weights" so the slot hash value is first integer divided (hash_value / N) to store N values per slot. You may want to consider increasing the -b option value to avoid hash-collisions in these cases.

VW uses -b 18 by default. 2^18 is 262144 meaning if you have much less than 262144 distinct features in your training set you should be relatively safe from hash-collisions. If you use reductions like multiclass, you may want to increase the default by requesting a bigger -b value.