only testing
Generating 3-grams for all namespaces.
Generating 1-skips for all namespaces.
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = 001.predict.tmp
using no cache
Reading datafile = train-sets/0001.dat
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
0.000000   0.000000            3         3.0   0.0000   0.0000      326
0.000000   0.000000            6         6.0   1.0000   1.0000      170
0.000000   0.000000           11        11.0   0.0000   0.0000      212
0.000000   0.000000           22        22.0   1.0000   1.0000      512
0.000000   0.000000           44        44.0   0.0000   0.0000      380
0.000000   0.000000           87        87.0   1.0000   1.0000      350
0.000000   0.000000          174       174.0   1.0000   1.0000      140

finished run
number of examples = 200
weighted example sum = 200
weighted label sum = 91
average loss = 0
best constant = 0.4523
best constant's loss = 0.2477
total feature number = 89692
