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using l2 regularization = 1
enabling BFGS based optimization **without** curvature calculation
Num weight bits = 20
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
m = 7
Allocated 72M for weights and mem
## avg. loss der. mag. d. m. cond. wolfe1 wolfe2 mix fraction curvature dir. magnitude step size
creating cache_file = train-sets/rcv1_small.dat.cache
Reading datafile = train-sets/rcv1_small.dat
num sources = 1
1 0.69315 0.00186 0.34333 0.29016 135.67795 1.18327
3 0.46631 0.00773 0.16072 0.558359 0.205344 176.93877 1.00000
4 0.36719 0.00415 0.06788 0.435725 -0.010674 28.54624 1.00000
5 0.33604 0.00047 0.01318 0.715574 0.454858 32.82015 1.00000
6 0.32041 0.00001 0.00476 0.679156 0.380024 22.63070 1.00000
7 0.31063 0.00005 0.00350 0.755260 0.517819 55.27250 1.00000
8 0.29882 0.00002 0.00095 0.666044 0.330579 27.55031 1.00000
9 0.29526 0.00000 0.00011 0.562531 0.101449 2.45273 1.00000
10 0.29486 0.00000 0.00003 0.559816 0.115043 0.80446 1.00000
11 0.29475 0.00000 0.00001 0.504319 0.006255 0.09031 1.00000
finished run
number of examples = 200000
weighted example sum = 200000
weighted label sum = -12720
average loss = 0.199705
best constant = -0.0636
total feature number = 15587880
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