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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
rank = 10
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
3.457930 3.457930 1 1.0 2.0000 0.1404 23
5.743041 8.028152 2 2.0 3.0000 0.1666 23
9.391515 13.039988 4 4.0 4.0000 0.5033 23
9.041728 8.691941 8 8.0 2.0000 0.8482 23
6.344680 3.647632 16 16.0 3.0000 1.3200 23
4.815768 3.286857 32 32.0 2.0000 2.1091 23
3.394972 1.974175 64 64.0 5.0000 2.8885 23
2.366934 1.338896 128 128.0 4.0000 3.8108 23
1.826340 1.285746 256 256.0 3.0000 3.1742 23
finished run
number of examples per pass = 237
passes used = 2
weighted example sum = 474
weighted label sum = 1666
average loss = 1.51233
best constant = 3.51477
total feature number = 10902
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