public Agent(Algorithm algorithm, InferenceManager inferences, SnapShot snapShot)
{
this.algorithm = algorithm;
this.inferences = inferences;
this.snapShot = snapShot;
}
static void train(TextWriter writer, char csv_seperator, char dec_seperator)
{
// Ask file location questions
string input_location = writer.askFromConfig("Enter the file path to import data from. ", "GENERAL", "input-location");
string snapshot_location = writer.askFromConfig("Enter the directory to output snapshots to. ", "EXPORT", "snapshot-location");
string thoughts_location = writer.askFromConfig("Enter the directory to output inferences to. ", "EXPORT", "inference-location");
string vocabulary_location = writer.askFromConfig("Enter the file path to import the vocabulary form ", "VOCABULARY", "location");
// General settings
string catchinput = writer.askFromConfig("Catch error and output?", "GENERAL", "output-on-error");
bool catcherror = (catchinput == "TRUE");
string model_extension = "txt";
string rules_extension = "rules.txt";
string drawing_extension = "GRAPH";
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
DataController import = new DataController(csv_seperator, dec_seperator);
ObservationSet observations = import.importExamples(input_location);
VocabularyImporter vocab = new VocabularyImporter();
vocab.import(vocabulary_location);
InferenceManager inferences = new InferenceManager(vocab.vocabulary, csv_seperator);
SnapShot snapShot = new SnapShot(writer, stopwatch, snapshot_location, model_extension, rules_extension, drawing_extension);
string algorithmChoice = writer.askFromConfig("What algorithm should be used? [ID3, C4.5]", "GENERAL", "algorithm");
Algorithm algorithm = null;
Dictionary <string, object> parameters = new Dictionary <string, object>();
switch (algorithmChoice)
{
case "ID3":
algorithm = new ID3Algorithm();
break;
case "C4.5":
string confidence_input = writer.askFromConfig("What confidence level should be used", "C4.5", "confidence");
string keep_values_input = writer.askFromConfig("Should all values keep getting considered even when not in subset?", "C4.5", "keep_considering_values");
string minimum_number_of_objects_input = writer.askFromConfig("How many objects should a leaf at least contain?", "C4.5", "minimum_number_of_objects");
parameters["confidence"] = float.Parse(confidence_input);
parameters["keep_values_input"] = bool.Parse(keep_values_input);
parameters["minimum_number_of_objects"] = int.Parse(minimum_number_of_objects_input);
algorithm = new C45Algorithm();
break;
default:
throw new Exception($"Unknown algorithm given: {algorithmChoice}");
}
Agent agent = new Agent(algorithm, inferences, snapShot);
Console.WriteLine($"READY ({stopwatch.ElapsedMilliseconds} ms setup time). Press a key to start training process \n");
Console.ReadKey(true);
// Train the algorithm based on the Training set
Console.WriteLine("Starting Training process (TRAIN).");
Console.WriteLine("");
stopwatch.Reset();
stopwatch.Start();
if (catcherror)
{
try
{
agent.TRAIN(observations, parameters);
}
catch (Exception e)
{
Console.WriteLine("Encountered an error! Writing output and model anyways.");
writer.set_location(thoughts_location);
inferences.write(writer);
throw (e);
}
}
else
{
agent.TRAIN(observations, parameters);
}
Console.WriteLine("");
long training_time = stopwatch.ElapsedMilliseconds;
long snapshot_time = training_time - snapShot.secondsBySnapShot;
Console.WriteLine("Training completed. Processing thoughts.");
writer.set_location(thoughts_location);
inferences.write(writer);
long thought_time = stopwatch.ElapsedMilliseconds;
Console.WriteLine($"Training time: {training_time}ms including snapshotting, {snapshot_time}ms excluding. Thoughts processing time: {thought_time - training_time}ms.");
}