creating quadratic features for pairs: ui 
final_regressor = movielens.reg
Num weight bits = 16
learning rate = 0.025
initial_t = 1
power_t = 0
decay_learning_rate = 0.97
rank = 10
using l2 regularization = 0.001
creating cache_file = movielens.cache
Reading datafile = train-sets/ml100k_small_train
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
8.699942   8.699942            3         3.0   4.0000   0.2004       23
11.041393  13.382844           6         6.0   4.0000   0.3577       23
8.934545   6.406327           11        11.0   1.0000   0.6111       23
7.357914   5.781282           22        22.0   2.0000   1.2071       23
6.031830   4.705747           44        44.0   4.0000   1.7897       23
4.832065   3.604398           87        87.0   3.0000   2.9281       23
3.078799   1.325534          174       174.0   4.0000   3.4095       23
2.182642   1.286485          348       348.0   5.0000   3.7808       23

finished run
number of examples = 474
weighted example sum = 474
weighted label sum = 1666
average loss = 1.945
best constant = 3.52
total feature number = 10902
