static void Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Provide training examples and a test set");
return;
}
List <string> features;
List <KeyValuePair <string, Dictionary <string, object> > > examples = GetExamples(args[0], out features);
NaiveBayesUpdateable bayes = new NaiveBayesUpdateable(features);
foreach (KeyValuePair <string, Dictionary <string, object> > example in examples)
{
bayes.AddExample(example.Key, example.Value);
}
bayes.UpdateClassifier();
NaiveBayes classifier = bayes.Classifier;
List <KeyValuePair <string, Dictionary <string, object> > > testCases = GetExamples(args[1], out features);
foreach (KeyValuePair <string, Dictionary <string, object> > pair in testCases)
{
string className = pair.Key;
Dictionary <string, double> results = classifier.Classify(pair.Value);
Console.WriteLine();
Console.WriteLine("Actual Class: " + className);
foreach (KeyValuePair <string, double> result in results)
{
Console.WriteLine(" " + result.Key + ": " + result.Value.ToString());
}
}
}
/// <summary>
/// Trains the Naive Bayes classifier using pre-computed data. This data
/// is in the form of a List which contains key-value-pairs of intended class
/// to a dictionary containing feature values indexed by feature names.
/// </summary>
/// <param name="featureNames">Names of all the features so that their values
/// can be looked up in the dictionary</param>
/// <param name="data">All the pre-computed feature data</param>
public void TrainCombo(List <string> featureNames, List <KeyValuePair <ShapeType, Dictionary <string, object> > > data)
{
NaiveBayesUpdateable bayes = new NaiveBayesUpdateable(featureNames);
foreach (KeyValuePair <ShapeType, Dictionary <string, object> > example in data)
{
bayes.AddExample(example.Key, example.Value);
}
bayes.UpdateClassifier();
_comboClassifier = bayes;
}
/// <summary>
/// Update the Naive Bayes classifier to learn from an example shape.
/// </summary>
/// <param name="shape"></param>
public void Learn(Sketch.Shape shape)
{
double believedOrientation;
Dictionary <string, object> features = GetIndRecognitionResults(shape, out believedOrientation);
// Since the combo classifier is trained with such a large
// number of training examples (1600+), each additional
// learning example has an extremely small effect.
// However, we want the error-corrected learning to have a
// more meaningful effect. So, we add the same example
// multiple times to the bayes classifier.
for (int i = 0; i < 5; i++) // MAGIC NUMBER
{
_comboClassifier.AddExample(shape.Type, features);
}
_comboClassifier.UpdateClassifier();
}