only testing
Num weight bits = 16
learning rate = 10
initial_t = 1
power_t = 0.5
rank = 10
using no cache
Reading datafile = test-sets/ml100k_small_test
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
2.640166   2.640166            3         3.0   5.0000   3.3590       23
1.539937   0.439708            6         6.0   4.0000   3.3826       23
1.467355   1.380256           11        11.0   1.0000   3.3434       23
1.178384   0.889414           22        22.0   3.0000   3.3272       23

finished run
number of examples = 30
weighted example sum = 30
weighted label sum = 110
average loss = 1.399
best constant = 3.759
total feature number = 690
