blob: 911bb69e0289ead94124454c920a47951d521c1c (
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
|
final_regressor = models/bs.vote.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
predictions = bs.vote.predict
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
1.000000 1.000000 1 1.0 1.0000 0.0000 51
0.500000 0.000000 2 2.0 0.0000 0.0000 104
0.250000 0.000000 4 4.0 0.0000 0.0000 135
0.250000 0.250000 8 8.0 0.0000 0.0000 146
0.312500 0.375000 16 16.0 1.0000 0.0000 24
0.343750 0.375000 32 32.0 0.0000 0.0000 32
0.343750 0.343750 64 64.0 0.0000 0.0000 61
0.359375 0.375000 128 128.0 1.0000 1.0000 106
finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 0
average loss = 0.31
best constant = 0
total feature number = 15482
|
