using l2 regularization = 1
predictions = ksvm_train.poly.predict
Lambda = 1
Kernel = poly
degree = 2
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/rcv1_smaller.dat
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0   1.0000   0.0000       50
1.236589 1.473179            2            2.0  -1.0000   0.4732      103
1.072391 0.908193            4            4.0  -1.0000  -0.1956      134
0.977210 0.882029            8            8.0  -1.0000  -0.3065      145
0.941543 0.905877           16           16.0   1.0000  -0.3392       23
0.938600 0.935656           32           32.0  -1.0000  -0.2156       31
0.912083 0.885567           64           64.0  -1.0000  -0.9240       60
0.900832 0.889580          128          128.0   1.0000   0.2666      105

finished run
number of examples = 250
weighted example sum = 250.000000
weighted label sum = -22.000000
average loss = 0.816899
best constant = -0.088000
best constant's loss = 0.992256
total feature number = 19870
Num support = 248
Number of kernel evaluations = 169707 Number of cache queries = 124678
Total loss = 204.224655
Done freeing model
Done freeing kernel params
Done with finish 
