/// <summary> /// Creates a deep copy /// </summary> public INonRecurrentNetwork DeepClone() { ParallelPerceptron clone = new ParallelPerceptron(NumOfInputValues, NumOfGates, Resolution); clone.SetWeights(_flatWeights); return(clone); }
//Constructor /// <summary> /// Constructs a parallel perceptron P-Delta rule trainer /// </summary> /// <param name="net">PP to be trained</param> /// <param name="inputVectorCollection">Predictors (input)</param> /// <param name="outputVectorCollection">Ideal outputs (the same number of rows as predictors rows)</param> /// <param name="settings">Optional startup parameters of the trainer</param> public PDeltaRuleTrainer(ParallelPerceptron net, List <double[]> inputVectorCollection, List <double[]> outputVectorCollection, PDeltaRuleTrainerSettings settings = null ) { //Parameters _settings = settings; if (_settings == null) { //Default parameters _settings = new PDeltaRuleTrainerSettings(); } _net = net; _inputVectorCollection = inputVectorCollection; _outputVectorCollection = outputVectorCollection; _resSquashCoeff = _net.ResSquashCoeff; _acceptableError = 1d / (2d * _resSquashCoeff); _marginSignificance = 1; _clearMargin = 0.05; _minM = _acceptableError * _resSquashCoeff; _maxM = 4d * _minM; _learningRate = _settings.IniLR; _prevWeights = _net.GetWeights(); _prevMSE = 0; _currMSE = 0; _epoch = 0; //Parallel workers / batch ranges preparation _workerRangeCollection = new List <WorkerRange>(); int numOfWorkers = Math.Min(Environment.ProcessorCount, _inputVectorCollection.Count); numOfWorkers = Math.Max(1, numOfWorkers); int workerBatchSize = _inputVectorCollection.Count / numOfWorkers; for (int workerIdx = 0, fromRow = 0; workerIdx < numOfWorkers; workerIdx++, fromRow += workerBatchSize) { int toRow = 0; if (workerIdx == numOfWorkers - 1) { toRow = _inputVectorCollection.Count - 1; } else { toRow = (fromRow + workerBatchSize) - 1; } WorkerRange workerRange = new WorkerRange(fromRow, toRow, _net.NumOfWeights); _workerRangeCollection.Add(workerRange); } return; }
//Constructor /// <summary> /// Constructs a parallel perceptron P-Delta rule trainer /// </summary> /// <param name="net">PP to be trained</param> /// <param name="inputVectorCollection">Predictors (input)</param> /// <param name="outputVectorCollection">Ideal outputs (the same number of rows as predictors rows)</param> /// <param name="settings">Configuration of the trainer</param> /// <param name="rand">Random object to be used</param> public PDeltaRuleTrainer(ParallelPerceptron net, List <double[]> inputVectorCollection, List <double[]> outputVectorCollection, PDeltaRuleTrainerSettings settings, Random rand ) { //Parameters _settings = (PDeltaRuleTrainerSettings)settings.DeepClone(); MaxAttempt = _settings.NumOfAttempts; MaxAttemptEpoch = _settings.NumOfAttemptEpochs; _net = net; _rand = rand; _inputVectorCollection = inputVectorCollection; _outputVectorCollection = outputVectorCollection; _resSquashCoeff = _net.ResSquashCoeff; _acceptableError = 1d / (2d * _resSquashCoeff); _marginSignificance = 1; _clearMargin = 0.05; _minM = _acceptableError * _resSquashCoeff; _maxM = 4d * _minM; //Parallel workers / batch ranges preparation _workerRangeCollection = new List <WorkerRange>(); int numOfWorkers = Math.Min(Environment.ProcessorCount, _inputVectorCollection.Count); numOfWorkers = Math.Max(1, numOfWorkers); int workerBatchSize = _inputVectorCollection.Count / numOfWorkers; for (int workerIdx = 0, fromRow = 0; workerIdx < numOfWorkers; workerIdx++, fromRow += workerBatchSize) { int toRow = 0; if (workerIdx == numOfWorkers - 1) { toRow = _inputVectorCollection.Count - 1; } else { toRow = (fromRow + workerBatchSize) - 1; } WorkerRange workerRange = new WorkerRange(fromRow, toRow, _net.NumOfWeights); _workerRangeCollection.Add(workerRange); } InfoMessage = string.Empty; //Start training attempt Attempt = 0; NextAttempt(); return; }