creating quadratic features for pairs: ui 
using l2 regularization = 2e-06
final_regressor = models/movielens.reg
Num weight bits = 16
learning rate = 0.05
initial_t = 1
power_t = 0
decay_learning_rate = 0.97
creating cache_file = train-sets/ml100k_small_train.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
3.295105 3.295105            1            1.0   2.0000   0.1848       23
5.751018 8.206931            2            2.0   3.0000   0.1352       23
9.362045 12.973073            4            4.0   4.0000   0.5185       23
9.071618 8.781190            8            8.0   2.0000   0.7967       23
6.322147 3.572676           16           16.0   3.0000   1.2553       23
4.816805 3.311464           32           32.0   2.0000   2.0616       23
3.399764 1.982723           64           64.0   5.0000   2.8991       23
2.357936 1.316107          128          128.0   4.0000   3.8250       23
1.815673 1.273411          256          256.0   3.0000   3.1820       23

finished run
number of examples per pass = 237
passes used = 2
weighted example sum = 474.000000
weighted label sum = 1666.000000
average loss = 1.498131
best constant = 3.514768
total feature number = 10902
