//private string _filePath;
public ArtificialNeuralNetwork(LayerStructure layerStructure, TrainingParameters trainingParameters,
IDataSet dataSet, IOptimizationStrategy strategy, IInitialRandomDistributionType randomDistribution)
{
_layerStructure = layerStructure;
_trainingParameters = trainingParameters;
DataSet = dataSet;
_strategy = strategy;
_strategy.LayerStructure = layerStructure;
_randomDistribution = randomDistribution;
CreateDataSets();
SetWeights();
InitPreviousDeltaWeights();
SetBiases();
InitPreviousDeltaBiases();
_strategy.FetchInitialWeightsAndBiases(ref _weights, ref _biases);
_strategy.FetchPreviousDeltaWeightsAndBiases(ref _previousDeltaWeights, ref _previousDeltaBiases);
_savePath = Path.GetTempPath();
}
public void Init()
{
_pendingOptimizationResults = new Queue <OptimizationResult>();
Strategy = CreateStrategy();
Strategy.NewParameterSet += (s, parameterSet) =>
{
_pendingOptimizationResults.Enqueue(new OptimizationResult(_stringify(_profit(parameterSet), _drawdown(parameterSet)), parameterSet, ""));
};
}
/// <summary>
/// Creates a new instance
/// </summary>
/// <param name="nodePacket">The optimization node packet to handle</param>
protected LeanOptimizer(OptimizationNodePacket nodePacket)
{
if (nodePacket.OptimizationParameters.IsNullOrEmpty())
{
throw new ArgumentException("Cannot start an optimization job with no parameter to optimize");
}
if (string.IsNullOrEmpty(nodePacket.Criterion?.Target))
{
throw new ArgumentException("Cannot start an optimization job with no target to optimize");
}
NodePacket = nodePacket;
OptimizationTarget = NodePacket.Criterion;
OptimizationTarget.Reached += (s, e) =>
{
// we've reached the optimization target
TriggerOnEndEvent();
};
Strategy = (IOptimizationStrategy)Activator.CreateInstance(Type.GetType(NodePacket.OptimizationStrategy));
RunningParameterSetForBacktest = new ConcurrentDictionary <string, ParameterSet>();
PendingParameterSet = new ConcurrentQueue <ParameterSet>();
Strategy.Initialize(OptimizationTarget, nodePacket.Constraints, NodePacket.OptimizationParameters, NodePacket.OptimizationStrategySettings);
Strategy.NewParameterSet += (s, parameterSet) =>
{
if (parameterSet == null)
{
// shouldn't happen
Log.Error($"Strategy.NewParameterSet({GetLogDetails()}): generated a null {nameof(ParameterSet)} instance");
return;
}
LaunchLeanForParameterSet(parameterSet);
};
}
public INetwork LoadNetwork(string xmlFilePath, IDataSet dataset)
{
_xmlDoc = new XmlDocument();
_filepath = xmlFilePath;
_xmlDoc.Load(_filepath);
//Create Training Params---------------------8::::::::::::::>----------------------------------------
TrainingParameters trainingParams = new TrainingParameters()
{
epochs = Convert.ToInt32(GetXmlValue("NeuralNetwork/Settings/TrainingParameters/Epochs")),
learningRate = Convert.ToDouble(GetXmlValue("NeuralNetwork/Settings/TrainingParameters/LearningRate")),
momentum = Convert.ToDouble(GetXmlValue("NeuralNetwork/Settings/TrainingParameters/Momentum"))
};
//Create LayerStructure---------------------8::::::::::::::>----------------------------------------
List <int> hiddenLayersList = new List <int>();
int numberOfHiddenLayers = Convert.ToInt32(GetXmlValue("NeuralNetwork/Settings/LayerStructure/NumberOfHiddenLayers"));
for (int i = 1; i <= numberOfHiddenLayers; i++)
{
int numberOfNeuronsInHiddenLayer = Convert.ToInt32(GetXmlValue("NeuralNetwork/Settings/LayerStructure/NumberOfNeuronsInHiddenLayer" + i.ToString()));
hiddenLayersList.Add(numberOfNeuronsInHiddenLayer);
}
LayerStructure layerStructure = new LayerStructure()
{
numberOfInputNodes = Convert.ToInt32(GetXmlValue("NeuralNetwork/Settings/LayerStructure/NumberOfInputNeurons")),
numberOfOutputNodes = Convert.ToInt32(GetXmlValue("NeuralNetwork/Settings/LayerStructure/NumberOfOutputNeurons")),
HiddenLayerList = hiddenLayersList
};
//Create Distribution Type---------------------8::::::::::::::>----------------------------------------
string distribution = GetXmlValue("NeuralNetwork/Settings/DistributionType/InitialRandomDistributionType");
var distributionType = Type.GetType(distribution + ",NeuralNetwork.Core");
IInitialRandomDistributionType initialDistribution = Activator.CreateInstance(distributionType) as IInitialRandomDistributionType;
//Create Strategy---------------------8::::::::::::::>----------------------------------------
string hiddenFunction = GetXmlValue("NeuralNetwork/Settings/Strategy/HiddenLayerActivationFunction");
string outputFunction = GetXmlValue("NeuralNetwork/Settings/Strategy/OutputLayerActivationFunction");
string costFunc = GetXmlValue("NeuralNetwork/Settings/Strategy/CostFunction");
string regularizationEnum = GetXmlValue("NeuralNetwork/Settings/Strategy/RegularizationType");
string regularizationStrategyFactory = GetXmlValue("NeuralNetwork/Settings/Strategy/RegularizationStrategyFactory");
var hiddenFunctionType = Type.GetType(hiddenFunction + ",NeuralNetwork.Core");
var outputFunctionType = Type.GetType(outputFunction + ",NeuralNetwork.Core");
var costFunctionType = Type.GetType(costFunc + ",NeuralNetwork.Core");
var regularizationStrategyFactoryType = Type.GetType(regularizationStrategyFactory + ",NeuralNetwork.Core");
IActivationFunction hiddenActivationFunction = Activator.CreateInstance(hiddenFunctionType) as IActivationFunction;
IActivationFunction outputActivationFunction = Activator.CreateInstance(outputFunctionType) as IActivationFunction;
ICostFunction costFunction = Activator.CreateInstance(costFunctionType) as ICostFunction;
RegularizationType regularizationType = (RegularizationType)Enum.Parse(typeof(RegularizationType), regularizationEnum);
IRegularizationStrategyFactory regularizationStrategyFact = Activator.CreateInstance(regularizationStrategyFactoryType) as IRegularizationStrategyFactory;
string optimizationStrategy = GetXmlValue("NeuralNetwork/Settings/Strategy/OptimizationStrategy");
var optStrategy = Type.GetType(optimizationStrategy + ",NeuralNetwork.Core");
IOptimizationStrategy strategy = Activator.CreateInstance(optStrategy, hiddenActivationFunction, outputActivationFunction, costFunction, regularizationType, regularizationStrategyFact) as IOptimizationStrategy;
//Create Network---------------------8::::::::::::::>----------------------------------------
network = new ArtificialNeuralNetwork(layerStructure, trainingParams, dataset, strategy, initialDistribution);
//Set Weights---------------------8::::::::::::::>----------------------------------------
network.Weights.Clear();
LoadInputWeights();
LoadHiddenWeights();
//Set Biases---------------------8::::::::::::::>----------------------------------------
network.Biases.Clear();
LoadHiddenBiases();
LoadOutputBiases();
//Clear XMLDoc---------------------8::::::::::::::>----------------------------------------
_xmlDoc = null;
return(network);
}
