blob: 2a56f527a43066b28346fd9bebc46a2c2fc876a4 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
creating cubic features for triples: abc
final_regressor = models/xxor.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/xxor.dat.cache
Reading datafile = train-sets/xxor.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.422559 0.422559 3 3.0 0.0000 0.3239 5
0.527830 0.633101 6 6.0 1.0000 0.1236 5
0.406989 0.261980 11 11.0 0.0000 0.4429 5
0.287058 0.167128 22 22.0 1.0000 0.6241 5
0.171319 0.055579 44 44.0 1.0000 0.8228 5
0.091564 0.009955 87 87.0 1.0000 0.9574 5
0.046018 0.000472 174 174.0 1.0000 0.9966 5
0.023010 0.000002 348 348.0 1.0000 1.0000 5
0.011505 0.000000 696 696.0 0.0000 0.0000 5
finished run
number of examples = 800
weighted example sum = 800
weighted label sum = 400
average loss = 0.01001
best constant = 0.5
best constant's loss = 0.25
total feature number = 4000
|
