Generating 2-grams for all namespaces.
creating quadratic features for pairs: :: 
WARNING: duplicate namespace interactions were found. Removed: 4278.
You can use --leave_duplicate_interactions to disable this behaviour.
only testing
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
0.077284 0.077284            1            1.0  -1.0000  -0.7220    32385
0.041674 0.006065            2            2.0  -1.0000  -0.9221     3741
0.030923 0.020171            4            4.0  -1.0000  -0.8484    71631
0.027502 0.024082            8            8.0   1.0000   0.9214     2211
0.035515 0.043529           16           16.0   1.0000   0.9767     3570
0.024695 0.013874           32           32.0  -1.0000  -0.9606     4278
0.031099 0.037503           64           64.0   1.0000   1.0000     5671
0.022095 0.013092          128          128.0  -1.0000  -1.0000     8778

finished run
number of examples = 250
weighted example sum = 250.000000
weighted label sum = -30.000000
average loss = 0.014494
best constant = -0.120000
best constant's loss = 0.985600
total feature number = 4176509
