using l2 regularization = 1
predictions = ksvm_train.rbf.predict
Lambda = 1
Kernel = rbf
bandwidth = 1
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
1.074842 1.149683            2            2.0  -1.0000   0.1497      103
1.004367 0.933892            4            4.0  -1.0000  -0.1400      134
0.946322 0.888277            8            8.0  -1.0000  -0.2731      145
0.928589 0.910857           16           16.0   1.0000  -0.3958       23
0.919013 0.909436           32           32.0  -1.0000  -0.3809       31
0.904098 0.889183           64           64.0  -1.0000  -0.5457       60
0.929634 0.955169          128          128.0   1.0000  -0.2001      105

finished run
number of examples = 250
weighted example sum = 250.000000
weighted label sum = -22.000000
average loss = 0.893919
best constant = -0.088000
best constant's loss = 0.992256
total feature number = 19870
Num support = 250
Number of kernel evaluations = 182687 Number of cache queries = 103819
Total loss = 223.479767
Done freeing model
Done freeing kernel params
Done with finish 
