add oaa test

test/RunTests

@@ -627,63 +627,68 @@ __DATA__
     train-sets/ref/cs_test.ldf.wap.stderr
     train-sets/ref/cs_test.ldf.wap.predict
 
-# Test 11: Error Correcting Tournament
+# Test 11: one-against-all
+{VW} -k --oaa 10 -c --passes 10 train-sets/multiclass
+    train-sets/ref/oaa.stdout
+    train-sets/ref/oaa.stderr
+
+# Test 12: Error Correcting Tournament
 {VW} -k --ect 10 --error 3 -c --passes 10 --invariant train-sets/multiclass
     train-sets/ref/multiclass.stdout
     train-sets/ref/multiclass.stderr
 
-# Test 12: Run searn on wsj_small for 12 passes, 4 passes per policy, extra features
+# Test 13: Run searn on wsj_small for 12 passes, 4 passes per policy, extra features
 {VW} -k -c -d train-sets/wsj_small.dat.gz --passes 12 --invariant --searn_passes_per_policy 4 --searn_task sequence --searn 5 --searn_sequencetask_history 2 --searn_sequencetask_bigrams --searn_sequencetask_features 1 --quiet
     train-sets/ref/searn_wsj.stdout
     train-sets/ref/searn_wsj.stderr
 
-# Test 13: Run searn (wap) on wsj_small for 2 passes, 1 pass per policy, extra features
+# Test 14: Run searn (wap) on wsj_small for 2 passes, 1 pass per policy, extra features
 {VW} -k -b 19 -c -d train-sets/wsj_small.dat.gz --passes 2 --invariant --searn_passes_per_policy 1 --searn_task sequence --searn 5 --wap 5 --searn_sequencetask_history 2 --searn_sequencetask_bigrams --searn_sequencetask_features 1 --quiet
     train-sets/ref/searn_wsj2.dat.stdout
     train-sets/ref/searn_wsj2.dat.stderr
 
-# Test 14: LBFGS on zero derivative input
+# Test 15: LBFGS on zero derivative input
 {VW} -k -c -d train-sets/zero.dat --loss_function=squared -b 20 --bfgs --mem 7 --passes 5 --l2 1.0
     train-sets/ref/zero.stdout
     train-sets/ref/zero.stderr
 
-# Test 15: LBFGS early termination
+# Test 16: LBFGS early termination
 {VW} -k -c -d train-sets/rcv1_small.dat --loss_function=logistic -b 20 --bfgs --mem 7 --passes 20 --termination 0.001 --l2 1.0
     train-sets/ref/rcv1_small.stdout
     train-sets/ref/rcv1_small.stderr
 
-# Test 16: Run LDA with 100 topics on 1000 Wikipedia articles
+# Test 17: Run LDA with 100 topics on 1000 Wikipedia articles
 {LDA} -k --lda 100 --lda_alpha 0.01 --lda_rho 0.01 --lda_D 1000 -b 13 --minibatch 128 --invariant train-sets/wiki1K.dat
     train-sets/ref/wiki1K.stdout
     train-sets/ref/wiki1K.stderr
 
-# Test 17: Run searn on seq_small for 12 passes, 4 passes per policy
+# Test 18: Run searn on seq_small for 12 passes, 4 passes per policy
 {VW} -k -c -d train-sets/seq_small --passes 12 --invariant --searn_passes_per_policy 4 --searn 4 --searn_task sequence --quiet
     train-sets/ref/searn_small.stdout
     train-sets/ref/searn_small.stderr
 
-# Test 18: neural network 3-parity with 2 hidden units
+# Test 19: neural network 3-parity with 2 hidden units
 {VW} -k -c -d train-sets/3parity --hash all --passes 2000 -b 16 --nn 2 -l 10 --invariant -f models/0021.model --random_seed 11 --quiet
     train-sets/ref/3parity.stdout
     train-sets/ref/3parity.stderr
 
-# Test 19: neural network 3-parity with 2 hidden units (predict)
+# Test 20: neural network 3-parity with 2 hidden units (predict)
 {VW} -d train-sets/3parity --hash all -t -i models/0021.model -p 0022.predict.tmp
     pred-sets/ref/0022.stdout
     pred-sets/ref/0022.stderr
     pred-sets/ref/0022.predict
 
-# Test 20: cubic features -- on a parity test case
+# Test 21: cubic features -- on a parity test case
 {VW} -k -c -f models/xxor.model train-sets/xxor.dat --cubic abc --passes 100
     train-sets/ref/xxor.stdout
     train-sets/ref/xxor.stderr
 
-# Test 21: matrix factorization -- training
+# 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
     train-sets/ref/ml100k_small.stdout
     train-sets/ref/ml100k_small.stderr
 
-# Test 22: matrix factorization -- testing
+# Test 23: matrix factorization -- testing
 {VW} -i movielens.reg -t -d test-sets/ml100k_small_test
     test-sets/ref/ml100k_small.stdout
     test-sets/ref/ml100k_small.stderr

test/train-sets/ref/oaa.stderr

@@ -0,0 +1,24 @@
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+decay_learning_rate = 1
+creating cache_file = train-sets/multiclass.cache
+Reading datafile = train-sets/multiclass
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.666667   0.666667          3      3.0          3        1        2
+0.833333   1.000000          6      6.0          6        1        2
+0.818182   0.800000         11     11.0          1        1        2
+0.636364   0.454545         22     22.0          2        2        2
+0.318182   0.000000         44     44.0          4        4        2
+0.160920   0.000000         87     87.0          7        7        2
+
+finished run
+number of examples = 100
+weighted example sum = 100
+weighted label sum = 0
+average loss = 0.14
+best constant = 0
+total feature number = 200