public void Execute(IExampleInterface app)
{
if (app.Args.Length < 1)
{
Console.WriteLine(@"Usage: ForestCover [data directory] [generate/train/traingui/evaluate] [e/o]");
}
else
{
try
{
var config = new ForestConfig(new FileInfo(app.Args[0]));
if (String.Compare(app.Args[1], "generate", true) == 0)
{
if (app.Args.Length < 3)
{
Console.WriteLine(
@"When using generate, you must specify an 'e' or an 'o' as the second parameter.");
}
else
{
bool useOneOf = !app.Args[2].ToLower().Equals("e");
Generate(config, useOneOf);
}
}
else if (String.Compare(app.Args[1], "train", true) == 0)
{
Train(config, false);
}
else if (String.Compare(app.Args[1], "traingui", true) == 0)
{
Train(config, true);
}
else if (String.Compare(app.Args[1], "evaluate", true) == 0)
{
Evaluate(config);
}
}
/*catch (Exception e)
* {
* Console.WriteLine(e.StackTrace);
* }*/
finally
{
EncogFramework.Instance.Shutdown();
}
}
}
public static void Generate(ForestConfig config, bool useOneOf)
{
var generate = new GenerateData(config);
generate.Step1();
generate.Step2();
DataNormalization norm = generate.Step3(useOneOf);
// save the normalize object
SerializeObject.Save(config.NormalizeFile.ToString(), norm);
// create and save the neural network
BasicNetwork network =
EncogUtility.SimpleFeedForward(norm.GetNetworkInputLayerSize(),
config.HiddenCount, 0, norm.GetNetworkOutputLayerSize(), true);
EncogDirectoryPersistence.SaveObject(config.TrainedNetworkFile, network);
}
public Evaluate(ForestConfig config)
{
_config = config;
}
public TrainNetwork(ForestConfig config)
{
_config = config;
}
public void Execute(IExampleInterface app)
{
if (app.Args.Length < 1)
{
Console.WriteLine(@"Usage: ForestCover [data directory] [generate/train/traingui/evaluate] [e/o]");
}
else
{
try
{
var config = new ForestConfig(new FileInfo(app.Args[0]));
if (String.Compare(app.Args[1], "generate", true) == 0)
{
if (app.Args.Length < 3)
{
Console.WriteLine(
@"When using generate, you must specify an 'e' or an 'o' as the second parameter.");
}
else
{
bool useOneOf = !app.Args[2].ToLower().Equals("e");
Generate(config, useOneOf);
}
}
else if (String.Compare(app.Args[1], "train", true) == 0)
{
Train(config, false);
}
else if (String.Compare(app.Args[1], "traingui", true) == 0)
{
Train(config, true);
}
else if (String.Compare(app.Args[1], "evaluate", true) == 0)
{
Evaluate(config);
}
}
/*catch (Exception e)
{
Console.WriteLine(e.StackTrace);
}*/
finally
{
EncogFramework.Instance.Shutdown();
}
}
}
public static void Evaluate(ForestConfig config)
{
var evaluate = new Evaluate(config);
evaluate.EvaluateNetwork();
}
public static void Train(ForestConfig config, bool useGui)
{
var program = new TrainNetwork(config);
program.Train(useGui);
}
public static void Generate(ForestConfig config, bool useOneOf)
{
var generate = new GenerateData(config);
generate.Step1();
generate.Step2();
DataNormalization norm = generate.Step3(useOneOf);
// save the normalize object
SerializeObject.Save(config.NormalizeFile.ToString(), norm);
// create and save the neural network
BasicNetwork network = EncogUtility.SimpleFeedForward(norm
.GetNetworkInputLayerSize(), config.HiddenCount, 0,
norm
.GetNetworkOutputLayerSize(), true);
EncogDirectoryPersistence.SaveObject(config.TrainedNetworkFile, network);
}
public GenerateData(ForestConfig config)
{
_config = config;
}
public GenerateData(ForestConfig config)
{
_config = config;
}
public static void Evaluate(ForestConfig config)
{
var evaluate = new Evaluate(config);
evaluate.EvaluateNetwork();
}
public static void Train(ForestConfig config, bool useGui)
{
var program = new TrainNetwork(config);
program.Train(useGui);
}
public TrainNetwork(ForestConfig config)
{
_config = config;
}
public Evaluate(ForestConfig config)
{
_config = config;
}