blob: 736e036d66ce42e987c4e5f90b9e64392ac9e7ea (
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
31
32
|
creating quadratic features for pairs: MF
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
final_regressor = topk.model
creating cache_file = topk-train.cache
Reading datafile = train-sets/topk.vw
num sources = 1
average since example example current current current
loss last counter weight label predict features
9.000000 9.000000 1 1.0 3.0000 0.0000 4
4.590362 0.180723 2 2.0 0.0000 0.4251 4
2.946029 1.301697 4 4.0 unknown 0.2876 1
2.641212 2.336395 8 8.0 unknown 0.4281 1
1.946607 1.252001 16 16.0 unknown 0.6361 1
1.419245 0.891883 32 32.0 unknown 0.7515 1
0.908998 0.398751 64 64.0 unknown 0.8212 1
0.555815 0.202631 128 128.0 unknown 0.8758 1
0.302649 0.049484 256 256.0 unknown 0.9141 1
0.153927 0.005205 512 512.0 unknown 0.9356 1
0.077005 0.000083 1024 1024.0 unknown 0.9394 1
finished run
number of examples per pass = 12
passes used = 100
weighted example sum = 1200
weighted label sum = 1500
average loss = 0.0657109
best constant = 1.66667
total feature number = 3900
|
