using l2 regularization = 1
enabling BFGS based optimization **without** curvature calculation
Num weight bits = 20
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
m = 7
Allocated 72M for weights and mem
## avg. loss 	der. mag. 	d. m. cond.	 wolfe1    	wolfe2    	mix fraction	curvature 	dir. magnitude	step size
creating cache_file = train-sets/zero.dat.cache
Reading datafile = train-sets/zero.dat
num sources = 1
 1 0.00000   	0.00000   	0.00000   	          	          	          	0.00000   	0.00000   	0.00000

finished run
number of examples per pass = 5
passes used = 2
weighted example sum = 10.000000
weighted label sum = 0.000000
average loss = 0.000000
total feature number = 6002
