lbfgs floating point to fixed point printout conversion

1 files changed, 18 insertions, 18 deletions

diff --git a/test/train-sets/ref/rcv1_small.stderr b/test/train-sets/ref/rcv1_small.stderr
index d4dac43e..3cb0cca0 100644
--- a/test/train-sets/ref/rcv1_small.stderr
+++ b/test/train-sets/ref/rcv1_small.stderr
@@ -11,23 +11,23 @@ Allocated 72M for weights and mem
 creating cache_file = train-sets/rcv1_small.dat.cache
 Reading from train-sets/rcv1_small.dat
 num sources = 1
- 1 6.931472e-01	1.247013e-02	1.093747e+02	          	          	          	1.652544e+02	1.072661e+06	6.618564e-01	0.289
- 3 2.496379e+01	9.448176e-02	5.849782e+01	 -0.335275 	-0.667864 	          	          	(revise x 0.5)	3.309282e-01	0.321
- 4 6.688902e+00	7.916274e-02	2.138882e+01	 -0.165651 	-0.341257 	          	          	(revise x 0.5)	1.654641e-01	0.362
- 5 2.025952e+00	5.333706e-02	9.612557e+00	 -0.073645 	-0.172136 	          	          	(revise x 0.5)	8.273205e-02	0.403
- 6 8.860910e-01	2.270515e-02	4.214893e+00	 -0.021323 	-0.077380 	          	          	(revise x 0.5)	4.136603e-02	0.443
- 7 6.578700e-01	5.668358e-03	2.422930e+00	 0.007797  	-0.022375 	          	          	(revise x 0.5)	2.068301e-02	0.485
- 8 6.409029e-01	1.090401e-03	2.538482e+00	 0.023094  	0.007617  	          	          	5.593078e+00	1.000000e+00	0.595     
- 9 5.915731e-01	8.417667e-04	1.984715e+00	 0.939814  	0.880475  	          	          	4.497635e+02	1.000000e+00	0.733     
-10 3.619765e-01	1.536667e-04	2.219506e-01	 0.590336  	0.255459  	          	          	1.230659e+02	1.000000e+00	0.896     
-11 3.481746e-01	5.063765e-03	6.800128e-01	 0.218977  	-0.427065 	          	          	2.743107e+01	1.000000e+00	1.078     
-12 3.232802e-01	2.902376e-04	8.337535e-02	 0.597309  	0.217820  	          	          	3.598937e+00	1.000000e+00	1.283     
-13 3.198322e-01	1.480065e-05	4.911858e-02	 0.738430  	0.480967  	          	          	8.913005e+00	1.000000e+00	1.514     
-14 3.159695e-01	5.730583e-05	4.996126e-02	 0.762546  	0.528461  	          	          	2.429691e+01	1.000000e+00	1.773     
-15 3.108274e-01	1.807561e-04	5.458046e-02	 0.726800  	0.455732  	          	          	1.030238e+02	1.000000e+00	2.041     
-16 2.980001e-01	3.906264e-05	1.068944e-02	 0.692397  	0.384367  	          	          	5.095499e+01	1.000000e+00	2.294     
-17 2.948572e-01	6.621593e-08	1.792561e-04	 0.516694  	0.031775  	          	          	3.948198e-01	1.000000e+00	2.545     
-18 2.947937e-01	1.091626e-07	8.738563e-05	 0.595950  	0.192321  	          	          	2.461794e-01	1.000000e+00	2.796     
+ 1 0.69315   	0.01247   	109.37472 	          	          	          	165.25443 	1072660.62500	0.66186   	0.766
+ 3 24.96379  	0.09448   	58.49782  	 -0.335275 	-0.667864 	          	          	(revise x 0.5)	0.33093   	0.852
+ 4 6.68890   	0.07916   	21.38882  	 -0.165651 	-0.341257 	          	          	(revise x 0.5)	0.16546   	0.966
+ 5 2.02595   	0.05334   	9.61256   	 -0.073645 	-0.172136 	          	          	(revise x 0.5)	0.08273   	1.093
+ 6 0.88609   	0.02271   	4.21489   	 -0.021323 	-0.077380 	          	          	(revise x 0.5)	0.04137   	1.227
+ 7 0.65787   	0.00567   	2.42293   	 0.007797  	-0.022375 	          	          	(revise x 0.5)	0.02068   	1.372
+ 8 0.64090   	0.00109   	2.53848   	 0.023094  	0.007617  	          	          	5.59308   	1.00000   	1.636     
+ 9 0.59157   	0.00084   	1.98471   	 0.939814  	0.880475  	          	          	449.76352 	1.00000   	1.932     
+10 0.36198   	0.00015   	0.22195   	 0.590336  	0.255459  	          	          	123.06593 	1.00000   	2.188     
+11 0.34817   	0.00506   	0.68001   	 0.218977  	-0.427065 	          	          	27.43107  	1.00000   	2.386     
+12 0.32328   	0.00029   	0.08338   	 0.597309  	0.217820  	          	          	3.59894   	1.00000   	2.627     
+13 0.31983   	0.00001   	0.04912   	 0.738430  	0.480967  	          	          	8.91300   	1.00000   	2.902     
+14 0.31597   	0.00006   	0.04996   	 0.762546  	0.528461  	          	          	24.29691  	1.00000   	3.194     
+15 0.31083   	0.00018   	0.05458   	 0.726800  	0.455732  	          	          	103.02377 	1.00000   	3.502     
+16 0.29800   	0.00004   	0.01069   	 0.692397  	0.384367  	          	          	50.95499  	1.00000   	3.810     
+17 0.29486   	0.00000   	0.00018   	 0.516694  	0.031775  	          	          	0.39482   	1.00000   	4.115     
+18 0.29479   	0.00000   	0.00009   	 0.595950  	0.192321  	          	          	0.24618   	1.00000   	4.427     
 
 finished run
 number of examples = 200000
@@ -36,5 +36,5 @@ weighted label sum = -1.272e+04
 average loss = 0.4485
 best constant = -0.06361
 total feature number = 15587880
-19 2.947603e-01	1.980417e-08	3.041856e-05	 0.619564  	0.238334  	          	          	1.564171e-01	1.000000e+00	3.075     
+19 0.29476   	0.00000   	0.00003   	 0.619564  	0.238334  	          	          	0.15642   	1.00000   	4.763