/** {@inheritDoc} */
@Override
public final boolean executeCommand(final String args) {
// get filenames
final String evalID = getProp().getPropertyString(ScriptProperties.ML_CONFIG_EVAL_FILE);
final String resourceID =
getProp().getPropertyString(ScriptProperties.ML_CONFIG_MACHINE_LEARNING_FILE);
final String outputID = getProp().getPropertyString(ScriptProperties.ML_CONFIG_OUTPUT_FILE);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Beginning evaluate raw");
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "evaluate file:" + evalID);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "resource file:" + resourceID);
final File evalFile = getScript().resolveFilename(evalID);
final File resourceFile = getScript().resolveFilename(resourceID);
final File outputFile = getAnalyst().getScript().resolveFilename(outputID);
final MLRegression method = (MLRegression) EncogDirectoryPersistence.loadObject(resourceFile);
final boolean headers = getScript().expectInputHeaders(evalID);
final AnalystEvaluateRawCSV eval = new AnalystEvaluateRawCSV();
eval.setScript(getScript());
getAnalyst().setCurrentQuantTask(eval);
eval.setReport(new AnalystReportBridge(getAnalyst()));
eval.analyze(
getAnalyst(),
evalFile,
headers,
getProp().getPropertyCSVFormat(ScriptProperties.SETUP_CONFIG_CSV_FORMAT));
eval.process(outputFile, method);
getAnalyst().setCurrentQuantTask(null);
return eval.shouldStop();
}
public static void train(File dataDir) {
final File networkFile = new File(dataDir, Config.NETWORK_FILE);
final File trainingFile = new File(dataDir, Config.TRAINING_FILE);
// network file
if (!networkFile.exists()) {
System.out.println("Can't read file: " + networkFile.getAbsolutePath());
return;
}
BasicNetwork network = (BasicNetwork) EncogDirectoryPersistence.loadObject(networkFile);
// training file
if (!trainingFile.exists()) {
System.out.println("Can't read file: " + trainingFile.getAbsolutePath());
return;
}
final MLDataSet trainingSet = EncogUtility.loadEGB2Memory(trainingFile);
// train the neural network
EncogUtility.trainConsole(network, trainingSet, Config.TRAINING_MINUTES);
System.out.println("Final Error: " + (float) network.calculateError(trainingSet));
System.out.println("Training complete, saving network.");
EncogDirectoryPersistence.saveObject(networkFile, network);
System.out.println("Network saved.");
Encog.getInstance().shutdown();
}