FAIL: test_prob (test_maxent.TestMaxentLm)

[drtonyr]

It's a long long time since I used srilm, thanks for the python bindings.

All worked swimmingly, except the MaxentLm test failed. I don't need this, so I'm good, just thought I'd report it as others may well have the same. I'm Ubuntu 22.04.1 LTS, srilm-1.7.3, everything done according to your instructions except I removed 'iconv' from setup.py as I didn't have libiconv and didn't seem to need it.

think0 tonyr: make
python3 -m unittest discover -v tests/
test_estimate (test_discount.TestNgramDiscount) ... ok
test_init (test_discount.TestNgramDiscount) ... ok
test_read_write (test_discount.TestNgramDiscount) ... ok
test_order (test_maxent.TestMaxentLm) ... ok
test_prob (test_maxent.TestMaxentLm) ... Starting fitting...
Starting OWL-BFGS with c1=0.000456204, sigma2=6576, max_iters=1000
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.68213
  regularized dual is 4.68213
  norm of gradient =0.424182
  norm of regularized gradient =0.424182
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.26599
  regularized dual is 4.26607
  norm of gradient =0.405679
  norm of regularized gradient =0.405527
Iteration 1
  No of NaNs in logZs: 0, No infs: 0
  dual is 12.6053
  regularized dual is 12.6493
  norm of gradient =0.702017
  norm of regularized gradient =0.704752
  No of NaNs in logZs: 0, No infs: 0
  dual is 6.60097
  regularized dual is 6.61289
  norm of gradient =0.700588
  norm of regularized gradient =0.702018
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.77496
  regularized dual is 3.77842
  norm of gradient =0.540149
  norm of regularized gradient =0.540885
Iteration 2
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.20445
  regularized dual is 3.20612
  norm of gradient =0.142862
  norm of regularized gradient =0.142163
Iteration 3
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.10224
  regularized dual is 3.10467
  norm of gradient =0.0930172
  norm of regularized gradient =0.0926131
Iteration 4
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.00898
  regularized dual is 3.01256
  norm of gradient =0.099428
  norm of regularized gradient =0.0990038
Iteration 5
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.94199
  regularized dual is 2.94677
  norm of gradient =0.0660072
  norm of regularized gradient =0.0657694
Iteration 6
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.88191
  regularized dual is 2.88783
  norm of gradient =0.0590299
  norm of regularized gradient =0.0590307
Iteration 7
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.81161
  regularized dual is 2.81963
  norm of gradient =0.0470536
  norm of regularized gradient =0.0467976
Iteration 8
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.74549
  regularized dual is 2.75595
  norm of gradient =0.0400534
  norm of regularized gradient =0.0395721
Iteration 9
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.64902
  regularized dual is 2.66465
  norm of gradient =0.0348613
  norm of regularized gradient =0.0342515
Iteration 10
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.50365
  regularized dual is 2.53153
  norm of gradient =0.0706963
  norm of regularized gradient =0.0704513
Iteration 11
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.36064
  regularized dual is 2.4049
  norm of gradient =0.0920386
  norm of regularized gradient =0.0911455
Iteration 12
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.27304
  regularized dual is 2.32685
  norm of gradient =0.0343558
  norm of regularized gradient =0.0323156
Iteration 13
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.16259
  regularized dual is 2.23523
  norm of gradient =0.0279268
  norm of regularized gradient =0.0254337
Iteration 14
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.09053
  regularized dual is 2.17633
  norm of gradient =0.0421421
  norm of regularized gradient =0.0403006
Iteration 15
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.03505
  regularized dual is 2.13228
  norm of gradient =0.0271689
  norm of regularized gradient =0.024379
Iteration 16
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.01308
  regularized dual is 2.11385
  norm of gradient =0.021937
  norm of regularized gradient =0.0180038
Iteration 17
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.9581
  regularized dual is 2.0703
  norm of gradient =0.024164
  norm of regularized gradient =0.020173
Iteration 18
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.91613
  regularized dual is 2.03855
  norm of gradient =0.0230848
  norm of regularized gradient =0.0186832
Iteration 19
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87709
  regularized dual is 2.01744
  norm of gradient =0.101709
  norm of regularized gradient =0.101195
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.88923
  regularized dual is 2.01995
  norm of gradient =0.0485505
  norm of regularized gradient =0.0469903
Iteration 20
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87557
  regularized dual is 2.00922
  norm of gradient =0.0200276
  norm of regularized gradient =0.0148839
Iteration 21
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87054
  regularized dual is 2.00577
  norm of gradient =0.019616
  norm of regularized gradient =0.0141275
Iteration 22
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86262
  regularized dual is 2.0008
  norm of gradient =0.0195721
  norm of regularized gradient =0.0139688
Iteration 23
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86826
  regularized dual is 2.00589
  norm of gradient =0.0233995
  norm of regularized gradient =0.0190818
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86482
  regularized dual is 2.00263
  norm of gradient =0.0202747
  norm of regularized gradient =0.0149986
Iteration 24
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86117
  regularized dual is 2.00053
  norm of gradient =0.0204487
  norm of regularized gradient =0.0150996
Iteration 25
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86038
  regularized dual is 2.00014
  norm of gradient =0.0197198
  norm of regularized gradient =0.0141236
Iteration 26
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85947
  regularized dual is 1.99986
  norm of gradient =0.0197912
  norm of regularized gradient =0.014258
Iteration 27
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85859
  regularized dual is 1.99951
  norm of gradient =0.0198709
  norm of regularized gradient =0.0142521
Iteration 28
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85683
  regularized dual is 1.99866
  norm of gradient =0.0207602
  norm of regularized gradient =0.0157125
Iteration 29
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85675
  regularized dual is 1.99861
  norm of gradient =0.019597
  norm of regularized gradient =0.0139656
Iteration 30
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85668
  regularized dual is 1.9986
  norm of gradient =0.0197068
  norm of regularized gradient =0.0140497
Iteration 31
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85674
  regularized dual is 1.9987
  norm of gradient =0.0196683
  norm of regularized gradient =0.0139978
Iteration 32
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85709
  regularized dual is 1.99924
  norm of gradient =0.0198983
  norm of regularized gradient =0.014353
Iteration 33
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85676
  regularized dual is 1.99913
  norm of gradient =0.0196602
  norm of regularized gradient =0.0140289
Iteration 34
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85656
  regularized dual is 1.99906
  norm of gradient =0.0196105
  norm of regularized gradient =0.0139592
Iteration 35
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85622
  regularized dual is 1.99905
  norm of gradient =0.019561
  norm of regularized gradient =0.0139033
Iteration 36
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85632
  regularized dual is 1.99923
  norm of gradient =0.0195582
  norm of regularized gradient =0.0139196
Iteration 37
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85641
  regularized dual is 1.99941
  norm of gradient =0.0207849
  norm of regularized gradient =0.0154313
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85634
  regularized dual is 1.99929
  norm of gradient =0.0198557
  norm of regularized gradient =0.0142442
Iteration 38
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85643
  regularized dual is 1.9994
  norm of gradient =0.0196162
  norm of regularized gradient =0.0139561
Iteration 39
OWL-BFGS terminated with the stopping criterion
Duration: 0 seconds
FAIL
test_read_write (test_maxent.TestMaxentLm) ... Starting fitting...
Starting OWL-BFGS with c1=0.000456204, sigma2=6576, max_iters=1000
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.68213
  regularized dual is 4.68213
  norm of gradient =0.424182
  norm of regularized gradient =0.424182
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.26599
  regularized dual is 4.26607
  norm of gradient =0.405679
  norm of regularized gradient =0.405527
Iteration 1
  No of NaNs in logZs: 0, No infs: 0
  dual is 12.6053
  regularized dual is 12.6493
  norm of gradient =0.702017
  norm of regularized gradient =0.704752
  No of NaNs in logZs: 0, No infs: 0
  dual is 6.60097
  regularized dual is 6.61289
  norm of gradient =0.700588
  norm of regularized gradient =0.702018
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.77496
  regularized dual is 3.77842
  norm of gradient =0.540149
  norm of regularized gradient =0.540885
Iteration 2
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.20445
  regularized dual is 3.20612
  norm of gradient =0.142862
  norm of regularized gradient =0.142163
Iteration 3
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.10224
  regularized dual is 3.10467
  norm of gradient =0.0930172
  norm of regularized gradient =0.0926131
Iteration 4
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.00898
  regularized dual is 3.01256
  norm of gradient =0.099428
  norm of regularized gradient =0.0990038
Iteration 5
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.94199
  regularized dual is 2.94677
  norm of gradient =0.0660072
  norm of regularized gradient =0.0657694
Iteration 6
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.88191
  regularized dual is 2.88783
  norm of gradient =0.0590299
  norm of regularized gradient =0.0590307
Iteration 7
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.81161
  regularized dual is 2.81963
  norm of gradient =0.0470536
  norm of regularized gradient =0.0467976
Iteration 8
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.74549
  regularized dual is 2.75595
  norm of gradient =0.0400534
  norm of regularized gradient =0.0395721
Iteration 9
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.64902
  regularized dual is 2.66465
  norm of gradient =0.0348613
  norm of regularized gradient =0.0342515
Iteration 10
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.50365
  regularized dual is 2.53153
  norm of gradient =0.0706963
  norm of regularized gradient =0.0704513
Iteration 11
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.36064
  regularized dual is 2.4049
  norm of gradient =0.0920386
  norm of regularized gradient =0.0911455
Iteration 12
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.27304
  regularized dual is 2.32685
  norm of gradient =0.0343558
  norm of regularized gradient =0.0323156
Iteration 13
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.16259
  regularized dual is 2.23523
  norm of gradient =0.0279268
  norm of regularized gradient =0.0254337
Iteration 14
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.09053
  regularized dual is 2.17633
  norm of gradient =0.0421421
  norm of regularized gradient =0.0403006
Iteration 15
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.03505
  regularized dual is 2.13228
  norm of gradient =0.0271689
  norm of regularized gradient =0.024379
Iteration 16
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.01308
  regularized dual is 2.11385
  norm of gradient =0.021937
  norm of regularized gradient =0.0180038
Iteration 17
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.9581
  regularized dual is 2.0703
  norm of gradient =0.024164
  norm of regularized gradient =0.020173
Iteration 18
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.91613
  regularized dual is 2.03855
  norm of gradient =0.0230848
  norm of regularized gradient =0.0186832
Iteration 19
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87709
  regularized dual is 2.01744
  norm of gradient =0.101709
  norm of regularized gradient =0.101195
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.88923
  regularized dual is 2.01995
  norm of gradient =0.0485505
  norm of regularized gradient =0.0469903
Iteration 20
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87557
  regularized dual is 2.00922
  norm of gradient =0.0200276
  norm of regularized gradient =0.0148839
Iteration 21
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87054
  regularized dual is 2.00577
  norm of gradient =0.019616
  norm of regularized gradient =0.0141275
Iteration 22
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86262
  regularized dual is 2.0008
  norm of gradient =0.0195721
  norm of regularized gradient =0.0139688
Iteration 23
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86826
  regularized dual is 2.00589
  norm of gradient =0.0233995
  norm of regularized gradient =0.0190818
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86482
  regularized dual is 2.00263
  norm of gradient =0.0202747
  norm of regularized gradient =0.0149986
Iteration 24
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86117
  regularized dual is 2.00053
  norm of gradient =0.0204487
  norm of regularized gradient =0.0150996
Iteration 25
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86038
  regularized dual is 2.00014
  norm of gradient =0.0197198
  norm of regularized gradient =0.0141236
Iteration 26
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85947
  regularized dual is 1.99986
  norm of gradient =0.0197912
  norm of regularized gradient =0.014258
Iteration 27
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85859
  regularized dual is 1.99951
  norm of gradient =0.0198709
  norm of regularized gradient =0.0142521
Iteration 28
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85683
  regularized dual is 1.99866
  norm of gradient =0.0207602
  norm of regularized gradient =0.0157125
Iteration 29
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85675
  regularized dual is 1.99861
  norm of gradient =0.019597
  norm of regularized gradient =0.0139656
Iteration 30
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85668
  regularized dual is 1.9986
  norm of gradient =0.0197068
  norm of regularized gradient =0.0140497
Iteration 31
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85674
  regularized dual is 1.9987
  norm of gradient =0.0196683
  norm of regularized gradient =0.0139978
Iteration 32
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85709
  regularized dual is 1.99924
  norm of gradient =0.0198983
  norm of regularized gradient =0.014353
Iteration 33
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85676
  regularized dual is 1.99913
  norm of gradient =0.0196602
  norm of regularized gradient =0.0140289
Iteration 34
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85656
  regularized dual is 1.99906
  norm of gradient =0.0196105
  norm of regularized gradient =0.0139592
Iteration 35
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85622
  regularized dual is 1.99905
  norm of gradient =0.019561
  norm of regularized gradient =0.0139033
Iteration 36
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85632
  regularized dual is 1.99923
  norm of gradient =0.0195582
  norm of regularized gradient =0.0139196
Iteration 37
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85641
  regularized dual is 1.99941
  norm of gradient =0.0207849
  norm of regularized gradient =0.0154313
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85634
  regularized dual is 1.99929
  norm of gradient =0.0198557
  norm of regularized gradient =0.0142442
Iteration 38
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85643
  regularized dual is 1.9994
  norm of gradient =0.0196162
  norm of regularized gradient =0.0139561
Iteration 39
OWL-BFGS terminated with the stopping criterion
Duration: 0 seconds
ok
test_to_ngram_lm (test_maxent.TestMaxentLm) ... Starting fitting...
Starting OWL-BFGS with c1=0.000456204, sigma2=6576, max_iters=1000
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.68213
  regularized dual is 4.68213
  norm of gradient =0.424182
  norm of regularized gradient =0.424182
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.26599
  regularized dual is 4.26607
  norm of gradient =0.405679
  norm of regularized gradient =0.405527
Iteration 1
  No of NaNs in logZs: 0, No infs: 0
  dual is 12.6053
  regularized dual is 12.6493
  norm of gradient =0.702017
  norm of regularized gradient =0.704752
  No of NaNs in logZs: 0, No infs: 0
  dual is 6.60097
  regularized dual is 6.61289
  norm of gradient =0.700588
  norm of regularized gradient =0.702018
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.77496
  regularized dual is 3.77842
  norm of gradient =0.540149
  norm of regularized gradient =0.540885
Iteration 2
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.20445
  regularized dual is 3.20612
  norm of gradient =0.142862
  norm of regularized gradient =0.142163
Iteration 3
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.10224
  regularized dual is 3.10467
  norm of gradient =0.0930172
  norm of regularized gradient =0.0926131
Iteration 4
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.00898
  regularized dual is 3.01256
  norm of gradient =0.099428
  norm of regularized gradient =0.0990038
Iteration 5
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.94199
  regularized dual is 2.94677
  norm of gradient =0.0660072
  norm of regularized gradient =0.0657694
Iteration 6
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.88191
  regularized dual is 2.88783
  norm of gradient =0.0590299
  norm of regularized gradient =0.0590307
Iteration 7
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.81161
  regularized dual is 2.81963
  norm of gradient =0.0470536
  norm of regularized gradient =0.0467976
Iteration 8
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.74549
  regularized dual is 2.75595
  norm of gradient =0.0400534
  norm of regularized gradient =0.0395721
Iteration 9
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.64902
  regularized dual is 2.66465
  norm of gradient =0.0348613
  norm of regularized gradient =0.0342515
Iteration 10
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.50365
  regularized dual is 2.53153
  norm of gradient =0.0706963
  norm of regularized gradient =0.0704513
Iteration 11
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.36064
  regularized dual is 2.4049
  norm of gradient =0.0920386
  norm of regularized gradient =0.0911455
Iteration 12
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.27304
  regularized dual is 2.32685
  norm of gradient =0.0343558
  norm of regularized gradient =0.0323156
Iteration 13
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.16259
  regularized dual is 2.23523
  norm of gradient =0.0279268
  norm of regularized gradient =0.0254337
Iteration 14
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.09053
  regularized dual is 2.17633
  norm of gradient =0.0421421
  norm of regularized gradient =0.0403006
Iteration 15
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.03505
  regularized dual is 2.13228
  norm of gradient =0.0271689
  norm of regularized gradient =0.024379
Iteration 16
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.01308
  regularized dual is 2.11385
  norm of gradient =0.021937
  norm of regularized gradient =0.0180038
Iteration 17
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.9581
  regularized dual is 2.0703
  norm of gradient =0.024164
  norm of regularized gradient =0.020173
Iteration 18
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.91613
  regularized dual is 2.03855
  norm of gradient =0.0230848
  norm of regularized gradient =0.0186832
Iteration 19
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87709
  regularized dual is 2.01744
  norm of gradient =0.101709
  norm of regularized gradient =0.101195
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.88923
  regularized dual is 2.01995
  norm of gradient =0.0485505
  norm of regularized gradient =0.0469903
Iteration 20
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87557
  regularized dual is 2.00922
  norm of gradient =0.0200276
  norm of regularized gradient =0.0148839
Iteration 21
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87054
  regularized dual is 2.00577
  norm of gradient =0.019616
  norm of regularized gradient =0.0141275
Iteration 22
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86262
  regularized dual is 2.0008
  norm of gradient =0.0195721
  norm of regularized gradient =0.0139688
Iteration 23
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86826
  regularized dual is 2.00589
  norm of gradient =0.0233995
  norm of regularized gradient =0.0190818
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86482
  regularized dual is 2.00263
  norm of gradient =0.0202747
  norm of regularized gradient =0.0149986
Iteration 24
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86117
  regularized dual is 2.00053
  norm of gradient =0.0204487
  norm of regularized gradient =0.0150996
Iteration 25
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86038
  regularized dual is 2.00014
  norm of gradient =0.0197198
  norm of regularized gradient =0.0141236
Iteration 26
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85947
  regularized dual is 1.99986
  norm of gradient =0.0197912
  norm of regularized gradient =0.014258
Iteration 27
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85859
  regularized dual is 1.99951
  norm of gradient =0.0198709
  norm of regularized gradient =0.0142521
Iteration 28
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85683
  regularized dual is 1.99866
  norm of gradient =0.0207602
  norm of regularized gradient =0.0157125
Iteration 29
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85675
  regularized dual is 1.99861
  norm of gradient =0.019597
  norm of regularized gradient =0.0139656
Iteration 30
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85668
  regularized dual is 1.9986
  norm of gradient =0.0197068
  norm of regularized gradient =0.0140497
Iteration 31
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85674
  regularized dual is 1.9987
  norm of gradient =0.0196683
  norm of regularized gradient =0.0139978
Iteration 32
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85709
  regularized dual is 1.99924
  norm of gradient =0.0198983
  norm of regularized gradient =0.014353
Iteration 33
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85676
  regularized dual is 1.99913
  norm of gradient =0.0196602
  norm of regularized gradient =0.0140289
Iteration 34
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85656
  regularized dual is 1.99906
  norm of gradient =0.0196105
  norm of regularized gradient =0.0139592
Iteration 35
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85622
  regularized dual is 1.99905
  norm of gradient =0.019561
  norm of regularized gradient =0.0139033
Iteration 36
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85632
  regularized dual is 1.99923
  norm of gradient =0.0195582
  norm of regularized gradient =0.0139196
Iteration 37
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85641
  regularized dual is 1.99941
  norm of gradient =0.0207849
  norm of regularized gradient =0.0154313
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85634
  regularized dual is 1.99929
  norm of gradient =0.0198557
  norm of regularized gradient =0.0142442
Iteration 38
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85643
  regularized dual is 1.9994
  norm of gradient =0.0196162
  norm of regularized gradient =0.0139561
Iteration 39
OWL-BFGS terminated with the stopping criterion
Duration: 0 seconds
ok
test_train_test (test_maxent.TestMaxentLm) ... Starting fitting...
Starting OWL-BFGS with c1=0.000456204, sigma2=6576, max_iters=1000
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.68213
  regularized dual is 4.68213
  norm of gradient =0.424182
  norm of regularized gradient =0.424182
  No of NaNs in logZs: 0, No infs: 0
  dual is 4.26599
  regularized dual is 4.26607
  norm of gradient =0.405679
  norm of regularized gradient =0.405527
Iteration 1
  No of NaNs in logZs: 0, No infs: 0
  dual is 12.6053
  regularized dual is 12.6493
  norm of gradient =0.702017
  norm of regularized gradient =0.704752
  No of NaNs in logZs: 0, No infs: 0
  dual is 6.60097
  regularized dual is 6.61289
  norm of gradient =0.700588
  norm of regularized gradient =0.702018
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.77496
  regularized dual is 3.77842
  norm of gradient =0.540149
  norm of regularized gradient =0.540885
Iteration 2
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.20445
  regularized dual is 3.20612
  norm of gradient =0.142862
  norm of regularized gradient =0.142163
Iteration 3
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.10224
  regularized dual is 3.10467
  norm of gradient =0.0930172
  norm of regularized gradient =0.0926131
Iteration 4
  No of NaNs in logZs: 0, No infs: 0
  dual is 3.00898
  regularized dual is 3.01256
  norm of gradient =0.099428
  norm of regularized gradient =0.0990038
Iteration 5
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.94199
  regularized dual is 2.94677
  norm of gradient =0.0660072
  norm of regularized gradient =0.0657694
Iteration 6
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.88191
  regularized dual is 2.88783
  norm of gradient =0.0590299
  norm of regularized gradient =0.0590307
Iteration 7
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.81161
  regularized dual is 2.81963
  norm of gradient =0.0470536
  norm of regularized gradient =0.0467976
Iteration 8
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.74549
  regularized dual is 2.75595
  norm of gradient =0.0400534
  norm of regularized gradient =0.0395721
Iteration 9
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.64902
  regularized dual is 2.66465
  norm of gradient =0.0348613
  norm of regularized gradient =0.0342515
Iteration 10
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.50365
  regularized dual is 2.53153
  norm of gradient =0.0706963
  norm of regularized gradient =0.0704513
Iteration 11
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.36064
  regularized dual is 2.4049
  norm of gradient =0.0920386
  norm of regularized gradient =0.0911455
Iteration 12
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.27304
  regularized dual is 2.32685
  norm of gradient =0.0343558
  norm of regularized gradient =0.0323156
Iteration 13
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.16259
  regularized dual is 2.23523
  norm of gradient =0.0279268
  norm of regularized gradient =0.0254337
Iteration 14
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.09053
  regularized dual is 2.17633
  norm of gradient =0.0421421
  norm of regularized gradient =0.0403006
Iteration 15
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.03505
  regularized dual is 2.13228
  norm of gradient =0.0271689
  norm of regularized gradient =0.024379
Iteration 16
  No of NaNs in logZs: 0, No infs: 0
  dual is 2.01308
  regularized dual is 2.11385
  norm of gradient =0.021937
  norm of regularized gradient =0.0180038
Iteration 17
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.9581
  regularized dual is 2.0703
  norm of gradient =0.024164
  norm of regularized gradient =0.020173
Iteration 18
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.91613
  regularized dual is 2.03855
  norm of gradient =0.0230848
  norm of regularized gradient =0.0186832
Iteration 19
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87709
  regularized dual is 2.01744
  norm of gradient =0.101709
  norm of regularized gradient =0.101195
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.88923
  regularized dual is 2.01995
  norm of gradient =0.0485505
  norm of regularized gradient =0.0469903
Iteration 20
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87557
  regularized dual is 2.00922
  norm of gradient =0.0200276
  norm of regularized gradient =0.0148839
Iteration 21
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.87054
  regularized dual is 2.00577
  norm of gradient =0.019616
  norm of regularized gradient =0.0141275
Iteration 22
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86262
  regularized dual is 2.0008
  norm of gradient =0.0195721
  norm of regularized gradient =0.0139688
Iteration 23
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86826
  regularized dual is 2.00589
  norm of gradient =0.0233995
  norm of regularized gradient =0.0190818
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86482
  regularized dual is 2.00263
  norm of gradient =0.0202747
  norm of regularized gradient =0.0149986
Iteration 24
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86117
  regularized dual is 2.00053
  norm of gradient =0.0204487
  norm of regularized gradient =0.0150996
Iteration 25
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.86038
  regularized dual is 2.00014
  norm of gradient =0.0197198
  norm of regularized gradient =0.0141236
Iteration 26
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85947
  regularized dual is 1.99986
  norm of gradient =0.0197912
  norm of regularized gradient =0.014258
Iteration 27
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85859
  regularized dual is 1.99951
  norm of gradient =0.0198709
  norm of regularized gradient =0.0142521
Iteration 28
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85683
  regularized dual is 1.99866
  norm of gradient =0.0207602
  norm of regularized gradient =0.0157125
Iteration 29
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85675
  regularized dual is 1.99861
  norm of gradient =0.019597
  norm of regularized gradient =0.0139656
Iteration 30
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85668
  regularized dual is 1.9986
  norm of gradient =0.0197068
  norm of regularized gradient =0.0140497
Iteration 31
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85674
  regularized dual is 1.9987
  norm of gradient =0.0196683
  norm of regularized gradient =0.0139978
Iteration 32
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85709
  regularized dual is 1.99924
  norm of gradient =0.0198983
  norm of regularized gradient =0.014353
Iteration 33
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85676
  regularized dual is 1.99913
  norm of gradient =0.0196602
  norm of regularized gradient =0.0140289
Iteration 34
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85656
  regularized dual is 1.99906
  norm of gradient =0.0196105
  norm of regularized gradient =0.0139592
Iteration 35
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85622
  regularized dual is 1.99905
  norm of gradient =0.019561
  norm of regularized gradient =0.0139033
Iteration 36
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85632
  regularized dual is 1.99923
  norm of gradient =0.0195582
  norm of regularized gradient =0.0139196
Iteration 37
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85641
  regularized dual is 1.99941
  norm of gradient =0.0207849
  norm of regularized gradient =0.0154313
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85634
  regularized dual is 1.99929
  norm of gradient =0.0198557
  norm of regularized gradient =0.0142442
Iteration 38
  No of NaNs in logZs: 0, No infs: 0
  dual is 1.85643
  regularized dual is 1.9994
  norm of gradient =0.0196162
  norm of regularized gradient =0.0139561
Iteration 39
OWL-BFGS terminated with the stopping criterion
Duration: 0 seconds
ok
test_length (test_ngram.TestNgramCacheLM) ... ok
test_prob (test_ngram.TestNgramCacheLM) ... ok
test_prob (test_ngram.TestNgramCountLM) ... iteration 0: log likelihood = -199.326
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 1: log likelihood = -186.778
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 2: log likelihood = -184.043
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 3: log likelihood = -183.032
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 4: log likelihood = -182.549
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 5: log likelihood = -182.276
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 6: log likelihood = -182.106
ok
test_read_write (test_ngram.TestNgramCountLM) ... ok
test_train (test_ngram.TestNgramCountLM) ... iteration 0: log likelihood = -199.326
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 1: log likelihood = -186.778
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 2: log likelihood = -184.043
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 3: log likelihood = -183.032
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 4: log likelihood = -182.549
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 5: log likelihood = -182.276
warning: no data to estimate mixture weight for count 0, order 1
warning: no data to estimate mixture weight for count 1, order 1
iteration 6: log likelihood = -182.106
ok
test_compare_with_command_line (test_ngram.TestNgramLM) ... ok
test_len (test_ngram.TestNgramLM) ... ok
test_order (test_ngram.TestNgramLM) ... ok
test_prob (test_ngram.TestNgramLM) ... ok
test_iter (test_ngram.TestNgramLMInDepth) ... ok
test_mix (test_ngram.TestNgramLMInDepth) ... ok
test_prune (test_ngram.TestNgramLMInDepth) ... ok
test_rand_gen (test_ngram.TestNgramLMInDepth) ... ok
test_read_write (test_ngram.TestNgramLMInDepth) ... ok
test_test (test_ngram.TestNgramLMInDepth) ... ok
test_train (test_ngram.TestNgramLMInDepth) ... ok
test_order (test_ngram.TestNgramSimpleClassLM) ... ok
test_train (test_ngram.TestNgramSimpleClassLM) ... one of required KneserNey count-of-counts is zero
ok
test_train_class (test_ngram.TestNgramSimpleClassLM) ... ok
test_add (test_stats.TestNgramStats) ... ok
test_count (test_stats.TestNgramStats) ... ok
test_count_file (test_stats.TestNgramStats) ... ok
test_count_string (test_stats.TestNgramStats) ... ok
test_get (test_stats.TestNgramStats) ... ok
test_iter (test_stats.TestNgramStats) ... ok
test_len (test_stats.TestNgramStats) ... ok
test_make_test (test_stats.TestNgramStats) ... ok
test_order (test_stats.TestNgramStats) ... ok
test_read_write (test_stats.TestNgramStats) ... ok
test_read_write_binary (test_stats.TestNgramStats) ... ok
test_remove (test_stats.TestNgramStats) ... ok
test_set (test_stats.TestNgramStats) ... ok
test_sum_counts (test_stats.TestNgramStats) ... ok
test_add (test_vocab.TestVocab) ... ok
test_delete (test_vocab.TestVocab) ... ok
test_get (test_vocab.TestVocab) ... ok
test_in (test_vocab.TestVocab) ... ok
test_index (test_vocab.TestVocab) ... ok
test_iter (test_vocab.TestVocab) ... ok
test_property (test_vocab.TestVocab) ... ok
test_string (test_vocab.TestVocab) ... ok

======================================================================
FAIL: test_prob (test_maxent.TestMaxentLm)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/think/pkg/srilm-1.7.3/srilm-python/tests/test_maxent.py", line 22, in test_prob
    self.assertAlmostEqual(
AssertionError: -0.09901417791843414 != -1.2563170194625854 within 7 places (1.1573028415441513 difference)

----------------------------------------------------------------------
Ran 49 tests in 0.106s

FAILED (failures=1)
make: *** [Makefile:16: test] Error 1

[warnak]

Facing the exact same error, any updates on this?