diff options
author | ariel faigon <github.2009@yendor.com> | 2013-12-27 08:14:11 +0400 |
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committer | ariel faigon <github.2009@yendor.com> | 2013-12-27 08:14:11 +0400 |
commit | ee075717753fd740d3f8741db75c76248a84af16 (patch) | |
tree | dfaa6f574a9731613ee72761addc978d93d05383 /test/RunTests | |
parent | 68ba558925d7105c9a7caca1b3047672a455d36b (diff) |
Remove a few empty ref/*.stdout - not needed anymore.
Diffstat (limited to 'test/RunTests')
-rwxr-xr-x | test/RunTests | 9 |
1 files changed, 2 insertions, 7 deletions
diff --git a/test/RunTests b/test/RunTests index 064daeb6..33881a77 100755 --- a/test/RunTests +++ b/test/RunTests @@ -8,7 +8,8 @@ # See __DATA__ below for how to add more tests # use Getopt::Std; -use vars qw($opt_d $opt_D $opt_c $opt_e $opt_f $opt_E $opt_o $opt_w $opt_y $opt_v); +use vars qw($opt_d $opt_D $opt_c $opt_e $opt_f + $opt_E $opt_o $opt_w $opt_y $opt_v); my $Epsilon = 1e-4; @@ -586,12 +587,10 @@ run_tests(); __DATA__ # Test 1: {VW} -k -l 20 --initial_t 128000 --power_t 1 -d train-sets/0001.dat -f models/0001.model -c --passes 8 --invariant --ngram 3 --skips 1 --holdout_off - train-sets/ref/0001.stdout train-sets/ref/0001.stderr # Test 2: checking predictions as well {VW} -k -t train-sets/0001.dat -i models/0001.model -p 001.predict.tmp --invariant - test-sets/ref/0001.stdout test-sets/ref/0001.stderr pred-sets/ref/0001.predict @@ -613,18 +612,15 @@ __DATA__ # Test 6: run predictions on Test 4 model # Pretending the labels aren't there {VW} -k -t -i models/0002.model -d train-sets/0002.dat -p 0002b.predict - test-sets/ref/0002b.stdout test-sets/ref/0002b.stderr pred-sets/ref/0002b.predict # Test 7: using normalized adaptive updates and a low --power_t {VW} -k --power_t 0.45 -f models/0002c.model train-sets/0002.dat - train-sets/ref/0002c.stdout train-sets/ref/0002c.stderr # Test 8: predicts on test 7 model {VW} -k -t -i models/0002c.model -d train-sets/0002.dat -p 0002c.predict - test-sets/ref/0002c.stdout test-sets/ref/0002c.stderr pred-sets/ref/0002c.predict @@ -698,7 +694,6 @@ __DATA__ # Test 22: matrix factorization -- training {VW} -k -d train-sets/ml100k_small_train -b 16 -q ui --rank 10 --l2 0.001 --learning_rate 0.025 --passes 2 --decay_learning_rate 0.97 --power_t 0 -f movielens.reg --cache_file movielens.cache --loss_function classic --holdout_off - train-sets/ref/ml100k_small.stdout train-sets/ref/ml100k_small.stderr # Test 23: matrix factorization -- testing |