/// <summary>
/// Trains the adaptive image recognizer on one of the incorrectly recognized gates
/// in the training set.
/// </summary>
private void learnOne()
{
// Make a list of the incorrectly recognized gates from the training set
List <Sketch.Shape> wrong = new List <Sketch.Shape>();
foreach (Sketch.Shape train in _trainSet.Keys)
{
if (!_trainSet[train])
{
wrong.Add(train);
}
}
if (wrong.Count == 0)
{
return;
}
// chose a random gate from your list
int index = (new Random()).Next(wrong.Count);
Sketch.Shape learn = wrong[index];
// learnFromExample requires a shape to have all it's attributes updated by the recognizer
// (ie. - template used and recognition results)
// However, we don't want to change the shapes in _gates, so clone it first.
Sketch.Shape clone = learn.Clone();
_recognizer.processShape(clone, null);
clone.Type = learn.Type;
// Teach the recognizer the correct recognition for the shape, then
// update dictionaries, etc. accordingly.
_recognizer.learnFromExample(clone);
_numTemplatesAdded++;
_trainSet.Remove(learn);
_alreadySet.Add(learn, false);
}
public override void finalize(System.IO.TextWriter handle, string path)
{
foreach (Sketch.Shape shape in _gates)
{
if (recognizeShape(shape))
{
_right.Add(shape);
}
else
{
_wrong.Add(shape);
}
}
while (_numTemplates < _maxTemplates)
{
//We need to add some wront templates to the train set, and others to the
//test set. This is the percentage that is added to the testing set.
_tempPercent = 0.5;
//The loop only stops when there are no more wrongly recognized shapes anywhere.
if (Math.Ceiling(_wrong.Count * _tempPercent) > _wrong.Count)
{
break;
}
_train.RemoveRange(0, _train.Count);
_test.RemoveRange(0, _test.Count);
shuffleList(ref _right);
shuffleList(ref _wrong);
//add all of the "right" list to the test set
//this is because having a "right" template in the
//train set does us no good.
for (int i = 0; i < _right.Count; i++)
{
_test.Add(_right[i]);
}
//add a percentage of the "wrong" list to the test set
for (int i = 0; i < _wrong.Count * _tempPercent; i++)
{
_test.Add(_wrong[i]);
}
//add a percentage of the "wrong" list to the train set
for (int i = (int)Math.Ceiling(_wrong.Count * _tempPercent); i < _wrong.Count; i++)
{
_train.Add(_wrong[i]);
}
if (_train.Count == 0)
{
break;
}
//just for good measure, to make sure they're random
shuffleList(ref _test);
shuffleList(ref _train);
#region Learn One Gate
// chose a random gate from your list
Sketch.Shape learn = new Sketch.Shape();
int index = (new Random()).Next(_train.Count - 1);
learn = _train[index];
//We don't want to test on what we train on, and
//we don't want to train on what we already have
//trained on.
_gates.Remove(learn);
// learnFromExample requires a shape to have all it's attributes updated by the recognizer
// (ie. - template used and recognition results)
// However, we don't want to change the shapes in _gates, so clone it first.
Sketch.Shape clone = learn.Clone();
_recognizer.processShape(clone, null);
clone.Type = learn.Type;
// Teach the recognizer the correct recognition for the shape, then
// update dictionaries, etc. accordingly.
_recognizer.learnFromExample(clone);
_numTemplates++;
#endregion
// We empty the right and wrong lists so that we can refill them with
// new recognition results based on the adaptation
_right.RemoveRange(0, _right.Count);
_wrong.RemoveRange(0, _wrong.Count);
foreach (Sketch.Shape shape in _gates)
{
if (recognizeShape(shape))
{
_right.Add(shape);
}
else
{
_wrong.Add(shape);
}
}
if (_right.Count > _test.Count)
{
break;
}
_results.Add(_numTemplates, (double)_right.Count / (double)_test.Count);
if (_right.Count == _test.Count)
{
break;
}
}
#region Write Results
// general information about the tests we ran
handle.WriteLine("Total number of gates: " + _gates.Count);
handle.WriteLine("Max templates per gate: " + _maxTemplates);
handle.WriteLine("First file: " + _filename);
handle.WriteLine();
// headers
handle.Write("Num templates added,");
handle.Write("Percent accuracy,");
handle.WriteLine();
// our results
foreach (var result in _results)
{
handle.WriteLine(result.Key + "," + result.Value);
}
#endregion
}
