failing tests (17, 24, 31, 32, 42, 46, 50, 54, 58) with cygwin build (head and latest release) about vowpal_wabbit

[JohnLangford]

I'm not sure what to do about this because:

(a) Most people just use the native windows binary on windows.
(b) None of the issues pointed out below look particularly alarming.

If someone wants to investigate the source of the issues, I would be
interested.

-John

On 06/07/2014 03:09 PM, LetterRip wrote:

here is my make test results with -d.

$ ./RunTests -d -f -E 0.001 ../vowpalwabbit/vw ../vowpalwabbit/vw
testing cygwin Testing vw: ../vowpalwabbit/vw
Testing lda: ../vowpalwabbit/vw
RunTests: '-D' to see any diff output
RunTests: '-o' to force overwrite references
RunTests: '-e' to abort on first failure
RunTests: test 1: minor (<0.001) precision differences ignored
RunTests: test 1: stderr OK
RunTests: test 2: stderr OK
RunTests: test 2: predict OK
RunTests: test 3: stderr OK
RunTests: test 4: stdout OK
RunTests: test 4: stderr OK
RunTests: test 5: minor (<0.001) precision differences ignored
RunTests: test 5: stderr OK
RunTests: test 6: minor (<0.001) precision differences ignored
RunTests: test 6: stderr OK
RunTests: test 6: minor (<0.001) precision differences ignored
RunTests: test 6: predict OK
RunTests: test 7: stderr OK
RunTests: test 8: stderr OK
RunTests: test 8: minor (<0.001) precision differences ignored
RunTests: test 8: predict OK
RunTests: test 9: stderr OK
RunTests: test 9: predict OK
RunTests: test 10: stderr OK
RunTests: test 10: predict OK
RunTests: test 11: stderr OK
RunTests: test 12: stderr OK
RunTests: test 13: stderr OK
RunTests: test 14: stdout OK
RunTests: test 14: stderr OK
RunTests: test 15: stdout OK
RunTests: test 15: stderr OK
RunTests: test 16: stdout OK
RunTests: test 16: minor (<0.001) precision differences ignored
RunTests: test 16: stderr OK
--- c:/cygwin/bin/diff.exe -u --minimal train-sets/ref/wiki1K.stderr
stderr.tmp
--- train-sets/ref/wiki1K.stderr 2014-06-07 10:19:30.457299200 -0700
+++ stderr.tmp 2014-06-07 10:55:49.469939700 -0700
@@ -7,21 +7,21 @@
num sources = 1
average since example example current current current
loss last counter weight label predict features
-10.149301 10.149301 1 1.0 unknown 0.0000 732
-10.369812 10.590324 2 2.0 unknown 0.0000 27
-10.325923 10.282033 4 4.0 unknown 0.0000 53
-10.401762 10.477602 8 8.0 unknown 0.0000 60
-10.356291 10.310820 16 16.0 unknown 0.0000 26
-10.472940 10.589588 32 32.0 unknown 0.0000 125
-10.474844 10.476749 64 64.0 unknown 0.0000 313
-10.425304 10.375763 128 128.0 unknown 0.0000 50
-10.005548 9.585792 256 256.0 unknown 0.0000 33
-9.331692 8.657836 512 512.0 unknown 0.0000 26
+10.149613 10.149613 1 1.0 unknown 0.0000 732
+10.369892 10.590171 2 2.0 unknown 0.0000 27
+10.325892 10.281891 4 4.0 unknown 0.0000 53
+10.401685 10.477478 8 8.0 unknown 0.0000 60
+10.356175 10.310665 16 16.0 unknown 0.0000 26
+10.472894 10.589612 32 32.0 unknown 0.0000 125
+10.474811 10.476727 64 64.0 unknown 0.0000 313
+10.425250 10.375689 128 128.0 unknown 0.0000 50
+9.620644 8.816037 256 256.0 unknown 0.0000 33
+8.965084 8.309524 512 512.0 unknown 0.0000 26

finished run
number of examples = 1000
weighted example sum = 1000
weighted label sum = 0
-average loss = 8.87286
-best constant = -nan
+average loss = 8.61022
+best constant = nan
total feature number = 86919
RunTests: test 17: FAILED: ref(train-sets/ref/wiki1K.stderr) !=
stderr(stderr.tm p)
RunTests: test 18: stderr OK
RunTests: test 19: stderr OK
RunTests: test 20: stderr OK
RunTests: test 20: predict OK
RunTests: test 21: minor (<0.001) precision differences ignored
RunTests: test 21: stderr OK
RunTests: test 22: stdout OK
RunTests: test 22: minor (<0.001) precision differences ignored
RunTests: test 22: stderr OK
RunTests: test 23: stdout OK
RunTests: test 23: minor (<0.001) precision differences ignored
RunTests: test 23: stderr OK
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/active-simulation.t24.stderr stderr.tmp
--- train-sets/ref/active-simulation.t24.stderr 2014-06-07
10:19:29.248492900 -0700
+++ stderr.tmp 2014-06-07 10:56:09.757455700 -0700
@@ -8,29 +8,29 @@
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 490.2 1.0000 0.0000 50
-0.948717 0.769927 69 630.8 -1.0000 -0.2203 87
-0.858263 0.497263 158 788.8 -1.0000 -0.0597 271
-0.735962 0.333673 222 1028.6 -1.0000 -0.5260 210
-0.681838 0.467658 357 1288.6 -1.0000 0.1321 33
-0.605799 0.319710 569 1631.1 1.0000 0.4440 29
-0.532594 0.240221 802 2039.5 unknown 0.2668 94
-0.481641 0.277913 1168 2549.6 unknown -0.4865 49
-0.413761 0.145727 1492 3195.2 -1.0000 -0.5541 62
-0.362012 0.155045 1957 3994.1 -1.0000 -0.1249 57
-0.348557 0.294790 2420 4993.7 unknown 1.0000 27
-0.326618 0.244195 3208 6322.9 -1.0000 -0.5406 32
-0.292655 0.157249 3824 7908.8 -1.0000 -0.4031 155
-0.307975 0.367171 4921 9955.6 1.0000 -0.6614 42
-0.272426 0.130283 6425 12445.4 unknown 1.0000 83
-0.254926 0.184944 8367 15557.5 unknown -0.9977 193
+0.948708 0.769842 69 630.7 -1.0000 -0.2203 87
+0.858257 0.497185 158 788.8 -1.0000 -0.0597 271
+0.736019 0.333786 222 1028.5 -1.0000 -0.5258 210
+0.681825 0.467234 357 1288.2 -1.0000 0.1319 33
+0.605749 0.319566 569 1630.6 1.0000 0.4441 29
+0.532547 0.240214 802 2038.9 unknown 0.2668 94
+0.481565 0.277754 1168 2549.0 unknown -0.4865 49
+0.413670 0.145694 1492 3194.8 -1.0000 -0.5545 62
+0.361942 0.155087 1957 3993.7 -1.0000 -0.1249 57
+0.348583 0.295150 2419 4992.2 unknown 0.2537 48
+0.326559 0.243901 3208 6322.4 -1.0000 -0.5406 32
+0.292596 0.157204 3824 7908.4 -1.0000 -0.4030 155
+0.307897 0.367010 4921 9955.4 1.0000 -0.6612 42
+0.269469 0.117621 6374 12474.8 -1.0000 -0.8160 38
+0.247154 0.158283 8269 15607.2 -1.0000 -0.2450 34

finished run
number of examples per pass = 10000
passes used = 1
-weighted example sum = 17822.7
-weighted label sum = -1553.71
-average loss = 0.2416
-best constant = -0.181495
+weighted example sum = 17652.2
+weighted label sum = -1387.78
+average loss = 0.239165
+best constant = -0.165277
total feature number = 779394
-total queries = 803
+total queries = 815

RunTests: test 24: FAILED:
ref(train-sets/ref/active-simulation.t24.stderr) != stderr(stderr.tmp)
RunTests: test 25: minor (<0.001) precision differences ignored
RunTests: test 25: stderr OK
RunTests: test 25: minor (<0.001) precision differences ignored
RunTests: test 25: predict OK
RunTests: test 26: minor (<0.001) precision differences ignored
RunTests: test 26: stderr OK
RunTests: test 26: minor (<0.001) precision differences ignored
RunTests: test 26: predict OK
RunTests: test 27: stderr OK
RunTests: test 27: minor (<0.001) precision differences ignored
RunTests: test 27: predict OK
RunTests: test 28: stderr OK
RunTests: test 28: minor (<0.001) precision differences ignored
RunTests: test 28: predict OK
RunTests: test 29: minor (<0.001) precision differences ignored
RunTests: test 29: stderr OK
RunTests: test 30: minor (<0.001) precision differences ignored
RunTests: test 30: stderr OK
--- c:/cygwin/bin/diff.exe -u --minimal train-sets/ref/remask.stderr
stderr.tmp
--- train-sets/ref/remask.stderr 2014-06-07 10:19:29.945958000 -0700
+++ stderr.tmp 2014-06-07 10:59:08.335425400 -0700
@@ -8,21 +8,21 @@
num sources = 1
average since example example current current current
loss last counter weight label predict features
-0.217147 0.217147 1 1.0 1.0000 0.5340 51
-0.286438 0.355730 2 2.0 0.0000 0.5964 104
-0.184439 0.082439 4 4.0 0.0000 0.2333 135
-0.163583 0.142727 8 8.0 0.0000 0.1131 146
-0.154976 0.146369 16 16.0 1.0000 0.5777 24
-0.175539 0.196103 32 32.0 0.0000 0.1863 32
-0.187968 0.200398 64 64.0 0.0000 0.0000 61
-0.166674 0.145379 128 128.0 1.0000 0.8292 106
+0.217245 0.217245 1 1.0 1.0000 0.5339 51
+0.286434 0.355623 2 2.0 0.0000 0.5963 104
+0.184443 0.082453 4 4.0 0.0000 0.2332 135
+0.163595 0.142747 8 8.0 0.0000 0.1133 146
+0.155013 0.146431 16 16.0 1.0000 0.5775 24
+0.175604 0.196195 32 32.0 0.0000 0.1864 32
+0.188473 0.201341 64 64.0 0.0000 0.0000 61
+0.167958 0.147443 128 128.0 1.0000 0.8168 106

finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
-average loss = 0.135049
+average loss = 0.137744
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482
RunTests: test 31: FAILED: ref(train-sets/ref/remask.stderr) !=
stderr(stderr.tmp)
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/remask.final.stderr stderr.tmp
--- train-sets/ref/remask.final.stderr 2014-06-07 10:19:29.940954700 -0700
+++ stderr.tmp 2014-06-07 10:59:08.590594500 -0700
@@ -8,20 +8,20 @@
average since example example current current current
loss last counter weight label predict features
0.000000 0.000000 1 1.0 1.0000 1.0000 51
-0.191596 0.383191 2 2.0 0.0000 0.6190 104
-0.095798 0.000000 4 4.0 0.0000 0.0000 135
-0.091403 0.087007 8 8.0 0.0000 0.0000 146
-0.075219 0.059035 16 16.0 1.0000 1.0000 24
-0.063804 0.052389 32 32.0 0.0000 0.0000 32
-0.081903 0.100002 64 64.0 0.0000 0.0000 61
-0.081818 0.081734 128 128.0 1.0000 1.0000 106
+0.168834 0.337668 2 2.0 0.0000 0.5811 104
+0.084417 0.000000 4 4.0 0.0000 0.0000 135
+0.083558 0.082699 8 8.0 0.0000 0.0000 146
+0.073237 0.062917 16 16.0 1.0000 1.0000 24
+0.063764 0.054291 32 32.0 0.0000 0.0066 32
+0.082466 0.101168 64 64.0 0.0000 0.0000 61
+0.083072 0.083678 128 128.0 1.0000 1.0000 106

finished run
number of examples per pass = 200
passes used = 1
weighted example sum = 200
weighted label sum = 91
-average loss = 0.0706821
+average loss = 0.0737064
best constant = 0.455
best constant's loss = 0.247975
total feature number = 15482
RunTests: test 32: FAILED: ref(train-sets/ref/remask.final.stderr) !=
stderr(stderr.tmp)
RunTests: test 33: minor (<0.001) precision differences ignored
RunTests: test 33: stderr OK
RunTests: test 34: minor (<0.001) precision differences ignored
RunTests: test 34: stderr OK
RunTests: test 34: minor (<0.001) precision differences ignored
RunTests: test 34: predict OK
RunTests: test 35: minor (<0.001) precision differences ignored
RunTests: test 35: stderr OK
RunTests: test 36: minor (<0.001) precision differences ignored
RunTests: test 36: stderr OK
RunTests: test 37: minor (<0.001) precision differences ignored
RunTests: test 37: stderr OK
RunTests: test 38: minor (<0.001) precision differences ignored
RunTests: test 38: stderr OK
RunTests: test 39: minor (<0.001) precision differences ignored
RunTests: test 39: stderr OK
RunTests: test 40: minor (<0.001) precision differences ignored
RunTests: test 40: stderr OK
RunTests: test 41: minor (<0.001) precision differences ignored
RunTests: test 41: stderr OK
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/lda-2pass-hang.stderr stderr.tmp
--- train-sets/ref/lda-2pass-hang.stderr 2014-06-07 10:19:29.770840900
-0700
+++ stderr.tmp 2014-06-07 10:59:15.127949200 -0700
@@ -8,21 +8,21 @@
num sources = 1
average since example example current current current
loss last counter weight label predict features
-12.797082 12.797082 1 1.0 unknown 0.0000 201
-12.934175 13.071269 2 2.0 unknown 0.0000 220
-13.475964 14.017752 4 4.0 unknown 0.0000 136
-14.728280 15.980597 8 8.0 unknown 0.0000 371
-15.885340 17.042400 16 16.0 unknown 0.0000 138
-17.174329 18.463318 32 32.0 unknown 0.0000 276
-17.150571 17.126814 64 64.0 unknown 0.0000 55
-16.497889 15.845206 128 128.0 unknown 0.0000 131
-15.940465 15.383042 256 256.0 unknown 0.0000 433
-15.306914 14.673363 512 512.0 unknown 0.0000 61
+12.796932 12.796932 1 1.0 unknown 0.0000 201
+12.904143 13.011354 2 2.0 unknown 0.0000 220
+12.981576 13.059008 4 4.0 unknown 0.0000 136
+12.921413 12.861250 8 8.0 unknown 0.0000 371
+12.610071 12.298730 16 16.0 unknown 0.0000 138
+12.427485 12.244900 32 32.0 unknown 0.0000 276
+12.062268 11.697051 64 64.0 unknown 0.0000 55
+11.726858 11.391447 128 128.0 unknown 0.0000 131
+11.544197 11.361536 256 256.0 unknown 0.0000 433
+11.339482 11.134766 512 512.0 unknown 0.0000 61

finished run
number of examples = 1000
weighted example sum = 1000
weighted label sum = 0
-average loss = 14.3325
-best constant = -nan
+average loss = 11.0719
+best constant = nan
total feature number = 193156
RunTests: test 42: FAILED: ref(train-sets/ref/lda-2pass-hang.stderr)
!= stderr(stderr.tmp)
RunTests: test 43: stderr OK
RunTests: test 44: stderr OK
RunTests: test 44: predict OK
RunTests: test 45: stderr OK
RunTests: test 45: predict OK
RunTests: test 46: stderr OK
RunTests: test 46: sequence_data.predict: no data. Can't compare
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/sequence_data.nonldf.test-beam20.predict
sequence_data.predict
--- train-sets/ref/sequence_data.nonldf.test-beam20.predict 2014-06-07
10:19:30.324210000 -0700
+++ sequence_data.predict 2014-06-07 10:59:15.974513200 -0700
@@ -13,8 +13,8 @@
1.76761 5 4 3 1 2
1.76761 5 4 2 1 2
1.76948 5 3 2 1 3
-1.76948 5 4 3 1 3
1.76948 5 4 2 1 3
+1.76948 5 4 3 1 3
1.77888 5 3 2 1 4
1.77889 5 4 3 1 4
1.77889 5 4 2 1 4
RunTests: test 46: FAILED:
ref(train-sets/ref/sequence_data.nonldf.test-beam20.predict) !=
predict(sequence_data.predict)
RunTests: test 47: stderr OK
RunTests: test 48: stderr OK
RunTests: test 48: predict OK
RunTests: test 49: stderr OK
RunTests: test 49: predict OK
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/sequence_data.ldf.test-beam20.stderr stderr.tmp
--- train-sets/ref/sequence_data.ldf.test-beam20.stderr 2014-06-07
10:19:30.147091600 -0700
+++ stderr.tmp 2014-06-07 10:59:16.933152300 -0700
@@ -11,13 +11,13 @@
loss last counter weight label predict features
average since sequence example current label current predicted current
cur cur predic. examples
loss last counter weight sequence prefix sequence prefix features pass
pol made gener.
-3.000000 3.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 0 1155 0
+4.000000 4.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 0 1155 0

finished run
number of examples per pass = 1
passes used = 1
weighted example sum = 1
weighted label sum = 0
-average loss = 3
+average loss = 4
best constant = -inf
total feature number = 0
RunTests: test 50: FAILED:
ref(train-sets/ref/sequence_data.ldf.test-beam20.stderr) !=
stderr(stderr.tmp)
RunTests: test 50: sequence_data.predict: no data. Can't compare
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/sequence_data.ldf.test-beam20.predict sequence_data.predict
--- train-sets/ref/sequence_data.ldf.test-beam20.predict 2014-06-07
10:19:30.142088700 -0700
+++ sequence_data.predict 2014-06-07 10:59:16.930150700 -0700
@@ -1,6 +1,6 @@
1.78814e-07 5 4 3 2 1
-1 5 4 3 2 4
1 5 4 3 2 5
+1 5 4 3 2 4
1.00594 5 4 3 2 3
1.0319 5 4 3 5 4
1.0332 5 4 3 4 3
@@ -9,13 +9,13 @@
1.1154 5 4 5 4 3
1.13135 5 4 4 3 2
1.24821 5 5 4 3 2
-1.71179 5 4 2 1 2
1.71179 5 3 2 1 2
+1.71179 5 4 2 1 2
1.71179 5 4 3 1 2
1.71438 5 4 2 1 3
1.71438 5 3 2 1 3
1.71438 5 4 3 1 3
1.72888 5 2 1 2 1
1.73146 5 2 1 3 2
-1.73258 5 4 2 1 5
+1.73258 5 3 2 1 5

RunTests: test 50: FAILED:
ref(train-sets/ref/sequence_data.ldf.test-beam20.predict) !=
predict(sequence_data.predict)
RunTests: test 51: stderr OK
RunTests: test 52: stderr OK
RunTests: test 52: predict OK
RunTests: test 53: stderr OK
RunTests: test 53: predict OK
RunTests: test 54: minor (<0.001) precision differences ignored
RunTests: test 54: stderr OK
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/sequencespan_data.nonldf.test-beam20.predict
sequencespan_data.predict
--- train-sets/ref/sequencespan_data.nonldf.test-beam20.predict
2014-06-07 10:19:30.396257100 -0700
+++ sequencespan_data.predict 2014-06-07 10:59:17.894792900 -0700
@@ -1,21 +1,21 @@
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 6
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 6
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 7
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 7 6
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 7
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 6
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 6
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 7
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 7
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 7
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 6
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 4 6 6
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 7 7
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 7 7
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 6
+-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 6 6
-0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 7 6
--0.781838 2 6 1 6 2 1 6 4 5 4 5 1 6 7 6
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 4 6 6
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 4 6 6
+-0.781811 2 6 1 6 2 6 7 4 5 4 5 1 6 6 7
-0.781811 2 6 1 6 2 6 6 4 5 4 5 1 4 6 7
-0.781811 2 6 1 6 2 6 6 4 5 4 5 1 4 6 7
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 6 6
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 6 6
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 6 7
--0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 6 7
+-0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 7 7
+-0.781811 2 6 1 6 2 6 6 4 5 4 5 1 6 7 7
+-0.781811 2 6 1 6 2 6 7 4 5 4 5 1 6 6 7
+-0.781811 2 6 1 6 2 6 7 4 5 4 5 1 6 6 6
+-0.781811 2 6 1 6 2 6 7 4 5 4 5 1 6 6 6

RunTests: test 54: FAILED:
ref(train-sets/ref/sequencespan_data.nonldf.test-beam20.predict) !=
predict(sequencespan_data.predict)
RunTests: test 55: stderr OK
RunTests: test 56: stderr OK
RunTests: test 56: predict OK
RunTests: test 57: stderr OK
RunTests: test 57: predict OK
RunTests: test 58: stderr OK
RunTests: test 58: sequencespan_data.predict: no data. Can't compare
--- c:/cygwin/bin/diff.exe -u --minimal
train-sets/ref/sequencespan_data.nonldf-bilou.test-beam20.predict
sequencespan_data.predict
--- train-sets/ref/sequencespan_data.nonldf-bilou.test-beam20.predict
2014-06-07 10:19:30.361234200 -0700
+++ sequencespan_data.predict 2014-06-07 10:59:18.975512900 -0700
@@ -8,10 +8,10 @@
0.638117 2 1 1 2 2 1 6 7 7 7 7 1 6 4 2
0.641751 2 1 1 2 2 1 6 7 7 7 7 1 6 6 7
0.665272 2 1 1 2 2 1 6 7 7 7 7 1 6 4 4
-0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 2 3
-0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 2 3
0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 4 5
0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 4 5
+0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 2 3
+0.665499 2 1 1 2 2 1 6 7 7 7 7 1 6 2 3
0.668673 2 1 1 2 2 1 6 7 7 7 7 1 6 4 6
0.706717 2 1 6 2 2 1 6 7 7 7 7 1 6 4 1
0.733527 2 1 1 2 2 1 6 7 7 7 7 2 3 3 3
RunTests: test 58: FAILED:
ref(train-sets/ref/sequencespan_data.nonldf-bilou.test-beam20.predict)
!= predict(sequencespan_data.predict)
RunTests: test 59: stderr OK
RunTests: test 60: minor (<0.001) precision differences ignored
RunTests: test 60: stderr OK

—
Reply to this email directly or view it on GitHub
#320.

[JohnLangford]

Closing this---recent changes likely make this obsolete.