/// <summary> /// Construct the trainer for a self organizing map. /// </summary> /// <param name="som">The self organizing map.</param> /// <param name="train">The training method.</param> /// <param name="learnMethod">The learning method.</param> /// <param name="learnRate">The learning rate.</param> public TrainSelfOrganizingMap(SelfOrganizingMap som, double[][] train, LearningMethod learnMethod, double learnRate) { this.som = som; this.train = train; this.totalError = 1.0; this.learnMethod = learnMethod; this.learnRate = learnRate; this.outputNeuronCount = som.OutputNeuronCount; this.inputNeuronCount = som.InputNeuronCount; this.totalError = 1.0; for (int tset = 0; tset < train.Length; tset++) { Matrix.Matrix dptr = Matrix.Matrix.CreateColumnMatrix(train[tset]); if (MatrixMath.vectorLength(dptr) < VERYSMALL) { throw (new System.Exception( "Multiplicative normalization has null training case")); } } this.bestnet = new SelfOrganizingMap(this.inputNeuronCount, this.outputNeuronCount, this.som.NormalizationType); this.won = new int[this.outputNeuronCount]; this.correc = new Matrix.Matrix(this.outputNeuronCount, this.inputNeuronCount + 1); if (this.learnMethod == LearningMethod.ADDITIVE) { this.work = new Matrix.Matrix(1, this.inputNeuronCount + 1); } else { this.work = null; } Initialize(); this.bestError = Double.MaxValue; }
/// <summary> /// Copy the weights from one matrix to another. /// </summary> /// <param name="source">The source SOM.</param> /// <param name="target">The target SOM.</param> private void CopyWeights(SelfOrganizingMap source, SelfOrganizingMap target) { MatrixMath.Copy(source.OutputWeights, target .OutputWeights); }
/// <summary> /// Construct the trainer for a self organizing map. /// </summary> /// <param name="som">The self organizing map.</param> /// <param name="train">The training method.</param> /// <param name="learnMethod">The learning method.</param> /// <param name="learnRate">The learning rate.</param> public TrainSelfOrganizingMap(SelfOrganizingMap som, double[][] train, LearningMethod learnMethod, double learnRate) { this.som = som; this.train = train; this.totalError = 1.0; this.learnMethod = learnMethod; this.learnRate = learnRate; this.outputNeuronCount = som.OutputNeuronCount; this.inputNeuronCount = som.InputNeuronCount; this.totalError = 1.0; for (int tset = 0; tset < train.Length; tset++) { Matrix.Matrix dptr = Matrix.Matrix.CreateColumnMatrix(train[tset]); if (MatrixMath.vectorLength(dptr) < VERYSMALL) { throw (new System.Exception( "Multiplicative normalization has null training case")); } } this.bestnet = new SelfOrganizingMap(this.inputNeuronCount, this.outputNeuronCount, this.som.NormalizationType); this.won = new int[this.outputNeuronCount]; this.correc = new Matrix.Matrix(this.outputNeuronCount, this.inputNeuronCount + 1); if (this.learnMethod == LearningMethod.ADDITIVE) { this.work = new Matrix.Matrix(1, this.inputNeuronCount + 1); } else { this.work = null; } Initialize(); this.bestError = Double.MaxValue; }
/// <summary> /// Copy the weights from one matrix to another. /// </summary> /// <param name="source">The source SOM.</param> /// <param name="target">The target SOM.</param> private void CopyWeights(SelfOrganizingMap source, SelfOrganizingMap target) { MatrixMath.Copy(source.OutputWeights, target .OutputWeights); }