public PruneIncremental(IMLDataSet training, INeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, IStatusReportable report) : base(report) { goto Label_008E; Label_0031: this._x7890c8b3a33b26e2 = new double[numTopResults]; return; Label_008E: this._x0b03741e8f17a9f7 = false; this._xab3ddaff42dd298a = new List<HiddenLayerParams>(); this._x823a2b9c8bf459c5 = training; if ((((uint) numTopResults) - ((uint) weightTries)) < 0) { goto Label_0031; } this._x49d5b7c4ad0e0bdd = pattern; if ((((uint) iterations) - ((uint) iterations)) <= uint.MaxValue) { this._xdbf51c857aeb8093 = iterations; this._x64343a0786fb9a3f = report; this._xe009ad1bd0a8245a = weightTries; this._xc5f756e0b4a83af0 = new BasicNetwork[numTopResults]; goto Label_0031; } goto Label_008E; }
public PostureRecognition(PatternType patternType, DataTrainingType dataTrainingType, int iterations) { this.networkPattern = PatternResolver <InputDataType, OutputDataType> .ResolvePattern(patternType, dataTrainingType); var activationFunction = new ActivationFunction(); activationFunction.InitializeSigmodeFunction(1.0); network = new NeuronNetwork(activationFunction, iterations); }
/** * Construct an object to determine the optimal number of hidden layers and * neurons for the specified training data and pattern. * * @param training * The training data to use. * @param pattern * The network pattern to use to solve this data. * @param iterations * How many iterations to try per network. * @param report * Object used to report status to. */ public PruneIncremental(INeuralDataSet training, INeuralNetworkPattern pattern, int iterations, IStatusReportable report) : base(report) { this.training = training; this.pattern = pattern; this.iterations = iterations; }
/// <summary> /// Construct an object to determine the optimal number of hidden layers and /// neurons for the specified training data and pattern. /// </summary> /// /// <param name="training">The training data to use.</param> /// <param name="pattern">The network pattern to use to solve this data.</param> /// <param name="iterations">How many iterations to try per network.</param> /// <param name="weightTries">The number of random weights to use.</param> /// <param name="numTopResults"></param> /// <param name="report">Object used to report status to.</param> public PruneIncremental(IMLDataSet training, INeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, IStatusReportable report) : base(report) { _done = false; _hidden = new List <HiddenLayerParams>(); _training = training; _pattern = pattern; _iterations = iterations; _report = report; _weightTries = weightTries; _topNetworks = new BasicNetwork[numTopResults]; _topErrors = new double[numTopResults]; }
/// <summary> /// Construct an object to determine the optimal number of hidden layers and /// neurons for the specified training data and pattern. /// </summary> /// /// <param name="training">The training data to use.</param> /// <param name="pattern">The network pattern to use to solve this data.</param> /// <param name="iterations">How many iterations to try per network.</param> /// <param name="weightTries">The number of random weights to use.</param> /// <param name="numTopResults"></param> /// <param name="report">Object used to report status to.</param> public PruneIncremental(IMLDataSet training, INeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, IStatusReportable report) : base(report) { _done = false; _hidden = new List<HiddenLayerParams>(); _training = training; _pattern = pattern; _iterations = iterations; _report = report; _weightTries = weightTries; _topNetworks = new BasicNetwork[numTopResults]; _topErrors = new double[numTopResults]; }