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accuracy works

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master
Peter Babič 9 years ago
parent 05dd94dd81
commit 7b38286a9b
8 changed files with 7 additions and 2011 deletions
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  1. 4
      .gitignore
  2. 100
      backup_89/testing.data.2
  3. 900
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  4. BIN
      backup_89/visualize.png
  5. 100
      backup_96/testing.data.2
  6. 900
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  7. BIN
      backup_96/visualize.png
  8. 8
      robocode-svm

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*.data
*.properties
*.battle
*.log

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2 1:0.392786 2:0.374749
1 2:0.617234 3:0.715084
1 1:0.290581 2:0.933868
1 1:0.0941884 2:0.983968
2 1:0.114228 2:0.442886
2 1:0.529058 2:0.90982
2 1:0.0521042 2:0.352705
2 1:0.517034 2:0.771543
1 1:0.142285 2:0.809619
2 1:0.987976 2:0.533066
2 1:0.95992 2:0.338677
2 1:0.695391 2:0.416834
2 1:0.376754 2:0.521042
2 1:0.659319 2:0.511022
2 1:0.308617 2:0.0501002

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8
robocode-svm
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@ -10,9 +10,11 @@ results="$datadir/battle.data"
settings="$datadir/settings.last.battle"
training="$datadir/training.scale.data"
testing="$datadir/testing.scale.data"
model="$datadir/model.data"
output="$datadir/output.data"
# Battle parameters
rounds=6
rounds=1000
width=500
height=500
robot1="sample.Corners"
@ -71,6 +73,10 @@ if [[ "$lines" -eq "$rounds" ]] ; then
# Scale the results to interval <0, 1> and split them to training set and testing set;
# misuse tee for 'process substituion' and send its stdout do /dev/null
svm-scale -l 0 -u 1 "$results" | tee >(head -n "$tr" > "$training") >(tail -n "$te" > "$testing") > /dev/null
# Train the SVM with the training data
svm-train -c 10 -g 2 "$training" "$model"
# Check the accuracy of the testing data against the SVM model made of training data
svm-predict "$testing" "$model" "$output" | grep "Accuracy"
# Generate the battle data (run 1000 battles) for a SVM classification
else
# Repeat the battle desired number of times (till the battle data file does not contain $rounds entries

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