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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/rcv1_small.dat.cache
Reading datafile = train-sets/rcv1_small.dat
num sources = 1
 1 0.69315   	0.00266   	0.87764   	          	          	          	2.24708   	776.93237 	0.39057   
 3 0.51357   	0.00493   	0.23981   	 0.523903  	0.088793  	          	          	40.87817  	1.00000   
 4 0.48774   	0.00232   	0.08215   	 0.274548  	-0.410690 	          	          	2.64529   	1.00000   
 5 0.47879   	0.00006   	0.00617   	 0.595892  	0.183063  	          	          	0.47184   	1.00000   
 6 0.47750   	0.00000   	0.00221   	 0.703360  	0.403715  	          	          	0.68626   	1.00000   
 7 0.47680   	0.00000   	0.00038   	 0.588395  	0.175459  	          	          	0.08911   	1.00000   
 8 0.47671   	0.00000   	0.00002   	 0.568445  	0.136827  	          	          	0.00444   	1.00000   

finished run
number of examples = 8000
weighted example sum = 8000
weighted label sum = -656
average loss = 0.458842
best constant = -0.164369
best constant's loss = 0.689781
total feature number = 629912