using l2 regularization = 1
predictions = ksvm_train.linear.predict
Lambda = 1
Kernel = linear
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
1.266108 1.532215            2            2.0  -1.0000   0.5322      103
1.034805 0.803502            4            4.0  -1.0000  -0.4093      134
0.946691 0.858578            8            8.0  -1.0000  -0.3831      145
0.927406 0.908121           16           16.0   1.0000  -0.6578       23
0.916010 0.904614           32           32.0  -1.0000  -0.4899       31
0.920972 0.925934           64           64.0  -1.0000  -0.8973       60
0.915840 0.910709          128          128.0   1.0000   0.0358      105

finished run
number of examples = 250
weighted example sum = 250.000000
weighted label sum = -22.000000
average loss = 0.809643
best constant = -0.088000
best constant's loss = 0.992256
total feature number = 19870
Num support = 246
Number of kernel evaluations = 174784 Number of cache queries = 119191
Total loss = 202.410736
Done freeing model
Done freeing kernel params
Done with finish 
