public NeuralWrapper(NeuralSettings settings, int inputSize, int outputSize)
{
if (settings == null)
{
throw new ArgumentNullException(nameof(settings));
}
_settings = settings;
_inputLayerSize = inputSize;
_outputLayerSize = outputSize;
}
public static NeuralSettings ApplyArguments(string[] arguments)
{
var parser = new CommandLineParser(new List <CommandLineOption>
{
new CommandLineOption {
ShortNotation = 'i', LongNotation = "iterations", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'h', LongNotation = "hidden-layers", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 't', LongNotation = "problem-type", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'f', LongNotation = "activation-function", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'b', LongNotation = "bias", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'l', LongNotation = "learning-rate", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'm', LongNotation = "momentum", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'd', LongNotation = "training-data", ParameterRequired = true
},
new CommandLineOption {
ShortNotation = 'o', LongNotation = "test-data", ParameterRequired = true
}
});
parser.Parse(arguments);
var settings = new NeuralSettings {
HiddenLayers = new int[0], HasBias = true
};
string value = null;
if (parser.TryGet("iterations", out value))
{
settings.Iterations = int.Parse(value);
}
else
{
settings.Iterations = 200;
}
if (parser.TryGet("momentum", out value))
{
settings.Momentum = double.Parse(value);
}
else
{
settings.Momentum = 0.3;
}
if (parser.TryGet("learning-rate", out value))
{
settings.LearningRate = double.Parse(value);
}
else
{
settings.LearningRate = 0.07;
}
if (parser.TryGet("bias", out value))
{
settings.HasBias = bool.Parse(value);
}
else
{
settings.HasBias = true;
}
if (parser.TryGet("activation-function", out value))
{
ActivationFunction tmp;
Enum.TryParse(value, true, out tmp);
settings.ActivationFunction = tmp;
}
else
{
settings.ActivationFunction = ActivationFunction.Unipolar;
}
if (parser.TryGet("problem-type", out value))
{
ProblemType tmp;
Enum.TryParse(value, true, out tmp);
settings.Type = tmp;
}
else
{
settings.Type = ProblemType.Classification;
}
if (parser.TryGet("hidden-layers", out value))
{
settings.HiddenLayers = value.Split(',').Select(int.Parse).ToArray();
}
else
{
settings.HiddenLayers = new int[] { 40, 30 }
};
if (parser.TryGet("training-data", out value))
{
FILENAME = value;
}
if (parser.TryGet("test-data", out value))
{
TEST_FILENAME = value;
}
return(settings);
}
}
