List of usage examples for weka.classifiers.functions.neural SigmoidUnit SigmoidUnit
SigmoidUnit
From source file:CJWeka.java
License:Open Source License
/** * Default constructor//ww w . j a v a 2 s .co m */ public CJWeka() { m_instances = null; m_currentInstance = null; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_nextId = 0; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); //setting all the options to their defaults. To completely change these //defaults they will also need to be changed down the bottom in the //setoptions function (the text info in the accompanying functions should //also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_useNomToBin = true; m_numEpochs = 4000; m_randomSeed = 0; m_hiddenLayers = 2; m_learningRate = .3; m_momentum = 0; m_resetAfterTraining = true; m_decay = false; my_attributes = new ArrayList<Attribute>(); classvals = new ArrayList<String>(); }
From source file:classifiers.mlp.MultilayerPerceptronCustom.java
License:Open Source License
/** * The constructor.//from w w w.j av a2 s . com */ public MultilayerPerceptronCustom() { m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_epoch = 0; m_error = 0; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); //setting all the options to their defaults. To completely change these //defaults they will also need to be changed down the bottom in the //setoptions function (the text info in the accompanying functions should //also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_autoBuild = true; m_gui = false; m_useNomToBin = true; m_driftThreshold = 20; m_numEpochs = 500; m_valSize = 0; m_randomSeed = 0; m_hiddenLayers = "a"; m_learningRate = .3; m_momentum = .2; m_reset = true; m_decay = false; }
From source file:com.ifmo.recommendersystem.metafeatures.classifierbased.internal.extractors.MultilayerPerceptron.java
License:Open Source License
/** * The constructor./*from w w w . j a v a2s .c om*/ */ public MultilayerPerceptron() { m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_epoch = 0; m_error = 0; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new ArrayList<NeuralConnection>(4); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); // setting all the options to their defaults. To completely change these // defaults they will also need to be changed down the bottom in the // setoptions function (the text info in the accompanying functions should // also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_autoBuild = true; m_gui = false; m_useNomToBin = true; m_driftThreshold = 20; m_numEpochs = 500; m_valSize = 0; m_randomSeed = 0; m_hiddenLayers = "a"; m_learningRate = .3; m_momentum = .2; m_reset = true; m_decay = false; }
From source file:org.ssase.debt.classification.OnlineMultilayerPerceptron.java
License:Open Source License
/** * The constructor.// www . j a va2 s .c o m */ public OnlineMultilayerPerceptron() { m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_epoch = 0; m_error = 0; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); // setting all the options to their defaults. To completely change these // defaults they will also need to be changed down the bottom in the // setoptions function (the text info in the accompanying functions // should // also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_autoBuild = true; m_gui = false; m_useNomToBin = true; m_driftThreshold = 20; m_numEpochs = 1; m_valSize = 0; m_randomSeed = 0; m_hiddenLayers = "a"; m_learningRate = .3; m_momentum = .2; m_reset = true; m_decay = false; }
From source file:uzholdem.classifier.OnlineMultilayerPerceptron.java
License:Open Source License
/** * The constructor./*from www. j a va2s .co m*/ */ public OnlineMultilayerPerceptron() { m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_epoch = 0; m_error = 0; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); //setting all the options to their defaults. To completely change these //defaults they will also need to be changed down the bottom in the //setoptions function (the text info in the accompanying functions should //also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_autoBuild = true; m_gui = false; m_useNomToBin = true; m_driftThreshold = 20; m_numEpochs = 500; m_valSize = 0; m_randomSeed = 0; m_hiddenLayers = "a"; m_learningRate = .3; m_momentum = .2; m_reset = true; m_decay = false; }
From source file:uzholdem.classifier.UpdateableMultilayerPerceptron.java
License:Open Source License
/** * The constructor.//from w ww . j a v a2 s . com */ public UpdateableMultilayerPerceptron() { m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_epoch = 0; m_error = 0; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_numeric = false; m_random = null; m_nominalToBinaryFilter = new NominalToBinary(); m_sigmoidUnit = new SigmoidUnit(); m_linearUnit = new LinearUnit(); //setting all the options to their defaults. To completely change these //defaults they will also need to be changed down the bottom in the //setoptions function (the text info in the accompanying functions should //also be changed to reflect the new defaults m_normalizeClass = true; m_normalizeAttributes = true; m_autoBuild = true; m_gui = false; m_useNomToBin = true; m_driftThreshold = 20; m_numEpochs = 500; m_valSize = 0; m_randomSeed = 0; m_hiddenLayers = "a"; m_learningRate = .3; m_momentum = .2; m_reset = true; m_decay = false; }