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final_regressor = models/active_cover.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/rcv1_mini.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0  -1.0000  -1.0000      128
0.000000 0.000000            2            2.0  -1.0000  -1.0000       44
0.198047 0.327943            4            5.0  -1.0000  -1.0000      190
0.291861 0.372267            7           10.9  unknown  -1.0000       56
0.139598 0.000000           17           22.9  unknown  -1.0000      193
0.124307 0.109523           35           46.5  unknown  -1.0000       48
0.191617 0.258261           67           93.5  -1.0000  -1.0000       82
0.226939 0.261907          138          188.0   1.0000  -1.0000      106
0.150954 0.075273          250          376.8  unknown  -1.0000      129

finished run
number of examples per pass = 250
passes used = 1
weighted example sum = 376.776421
weighted label sum = 12.994223
average loss = 0.150954
best constant = 0.116790
best constant's loss = 0.691445
total feature number = 19393
total queries = 96