blob: 8f280915edf69b6a624b4b2b768a0e59204799cb (
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
|
only testing
Num weight bits = 16
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = 0022.predict.tmp
using no cache
Reading datafile = train-sets/3parity
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.000000 0.000000 1 1.0 -1.0000 -1.0000 4
0.000000 0.000000 2 2.0 -1.0000 -1.0000 4
0.000000 0.000000 4 4.0 -1.0000 -1.0000 4
0.000000 0.000000 8 8.0 1.0000 1.0000 4
finished run
number of examples per pass = 8
passes used = 1
weighted example sum = 8
weighted label sum = 0
average loss = 0
best constant = -0.142857
total feature number = 32
|
