parsed 4 class weights
recall_tree: node_only = 0 bern_hyper = 1 max_depth = 4 routing = deterministic
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/multiclass
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0        1        0        2
1.000000 1.000000            2           11.0        2        1        2

finished run
number of examples = 10
weighted example sum = 19.100000
weighted label sum = 0.000000
average loss = 1.000000
total feature number = 20
