Examples of Neuron

ca.nengo.model.neuron.Neuron

A model of a single neuron cell. Neurons are Nodes which can have multiple Terminations, and multiple Origins (corresponding to axonal output and possibly other outpus such as gap junctions), but they always have a primary Origin which is named Neuron.AXON.
@author Bryan Tripp
cnslab.cnsnetwork.Neuron
Interface for a Neuron object. @version $Date: 2012-08-04 20:43:22 +0200 (Sat, 04 Aug 2012) $ $Rev: 104 $ $Author: croft $ @author Yi Dong @author David Wallace Croft, M.Sc. @author Jeremy Cohen
com.ansj.vec.domain.Neuron
com.greentea.relaxation.jnmf.model.Neuron
Class Neuron represents neuron - the block of neural network. Neurons belonged to neuron layers. Usually (but not always) the neurons from layer k coupled with neurons from layer k+1.
com.neuralnetwork.shared.neurons.Neuron
An abstract Neuron class implements the INeuron interface. @author fredladeroute
eas.simulation.brain.neural.Neuron
@author Lukas König
javasim.Neuron
org.apache.commons.math3.ml.neuralnet.Neuron
Describes a neuron element of a neural network. This class aims to be thread-safe. @version $Id: Neuron.java 1566092 2014-02-08 18:48:29Z tn $ @since 3.3
org.dmg.pmml._40.Neuron
A representation of the model object 'Neuron'. @see org.dmg.pmml._40._40Package#getNeuron() @model abstract="true" @generated

org.neuroph.core.Neuron

 Basic general neuron model according to McCulloch-Pitts neuron model. Different neuron models can be created by using different input and transfer functions for instances of this class, or by deriving from this class. The neuron is basic processing element of neural network. This class implements the following behaviour: output = transferFunction( inputFunction(inputConnections) )

@see InputFunction @see TransferFunction @author Zoran Sevarac

tv.floe.metronome.classification.neuralnetworks.core.neurons.Neuron

Examples of org.apache.commons.math3.ml.neuralnet.Neuron

     */
    private Neuron findAndUpdateBestNeuron(Network net,
                                           double[] features,
                                           double learningRate) {
        while (true) {
            final Neuron best = MapUtils.findBest(features, net, distance);


            final double[] expect = best.getFeatures();
            final double[] update = computeFeatures(expect,
                                                    features,
                                                    learningRate);
            if (best.compareAndSetFeatures(expect, update)) {
                return best;
            }


            // If another thread modified the state of the winning neuron,
            // it may not be the best match anymore for the given training

View Full Code Here

Examples of org.dmg.pmml._40.Neuron

   * <!-- begin-user-doc -->
   * <!-- end-user-doc -->
   * @generated
   */
  public void setFromNeuron(Neuron newFromNeuron) {
    Neuron oldFromNeuron = fromNeuron;
    fromNeuron = newFromNeuron;
    if (eNotificationRequired())
      eNotify(new ENotificationImpl(this, Notification.SET, _40Package.NEURAL_OUTPUT_TYPE__FROM_NEURON, oldFromNeuron, fromNeuron));
  }

View Full Code Here

Examples of org.dmg.pmml._40.Neuron

   * <!-- begin-user-doc -->
   * <!-- end-user-doc -->
   * @generated
   */
  public void setFromNeuron(Neuron newFromNeuron) {
    Neuron oldFromNeuron = fromNeuron;
    fromNeuron = newFromNeuron;
    if (eNotificationRequired())
      eNotify(new ENotificationImpl(this, Notification.SET, _40Package.CON_TYPE__FROM_NEURON, oldFromNeuron, fromNeuron));
  }

View Full Code Here

Examples of org.neuroph.core.Neuron

  }
  
  private void createTypeConnections() {
    // input connections for the first product type
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(i);
      Neuron toNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(0);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the second product type
    for(int i = 7; i < 14; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(i);
      Neuron toNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(1);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the third product type
    for(int i = 14; i < 20; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(i);
      Neuron toNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(2);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }    
    
    
    // output connections for the first product type
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(0);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(i);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the second product type
    for(int i = 7; i < 14; i++) {
      Neuron fromNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(1);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(i);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the third product type
    for(int i = 14; i < 20; i++) {
      Neuron fromNeuron = this.getLayerAt(typeLayerIdx).getNeuronAt(2);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(i);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }      
  }

View Full Code Here

Examples of org.neuroph.core.Neuron

    int lg[] = {2, 3, 10, 11, 16, 17};
    int sony[] = {4, 5, 6, 12, 13, 18, 19};
    
    // create samsung input sonnections
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(samsung[i]);
      Neuron toNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(0);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // create input connections for LG
    for(int i = 0; i < 6; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(lg[i]);
      Neuron toNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(1);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // create input connections for sony
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(sony[i]);
      Neuron toNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(2);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }    
    
    
    // output connections for the first brand
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(0);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(samsung[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the second brand
    for(int i = 0; i < 6; i++) {
      Neuron fromNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(1);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(lg[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the third brand
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(brandLayerIdx).getNeuronAt(2);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(sony[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }      
  }

View Full Code Here

Examples of org.neuroph.core.Neuron

    int mid[] = {3, 5, 8, 11, 12, 14, 19};
    int high[] = {1, 6, 9, 13, 15, 17};    
    
    // input connections for the first price class
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(low[i]);
      Neuron toNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(0);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the second price class
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(mid[i]);
      Neuron toNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(1);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the third price class
    for(int i = 0; i < 6; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(high[i]);
      Neuron toNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(2);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }    
    
    
    // output connections for the first price class
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(0);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(low[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the second price class
    for(int i = 0; i < 7; i++) {
      Neuron fromNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(1);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(mid[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the third price class
    for(int i = 0; i < 6; i++) {
      Neuron fromNeuron = this.getLayerAt(priceLayerIdx).getNeuronAt(2);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(high[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }      
  }

View Full Code Here

Examples of org.neuroph.core.Neuron

    int new_products[] = {6, 9};
    int bestsellers[] = {3, 12, 14};    
    
    // input connections for the first promo type
    for(int i = 0; i < sales.length; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(sales[i]);
      Neuron toNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(0);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the second promo type
    for(int i = 0; i < new_products.length; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(new_products[i]);
      Neuron toNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(1);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // input connections for the third promo type
    for(int i = 0; i < bestsellers.length; i++) {
      Neuron fromNeuron = this.getLayerAt(inputLayerIdx).getNeuronAt(bestsellers[i]);
      Neuron toNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(2);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }    


    // output connections for the first promo type
    for(int i = 0; i < sales.length; i++) {
      Neuron fromNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(0);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(sales[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the second promo type
    for(int i = 0; i < new_products.length; i++) {
      Neuron fromNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(1);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(new_products[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }
    
    // output connections for the third promo type
    for(int i = 0; i < bestsellers.length; i++) {
      Neuron fromNeuron = this.getLayerAt(promoLayerIdx).getNeuronAt(2);
      Neuron toNeuron = this.getLayerAt(outputLayerIdx).getNeuronAt(bestsellers[i]);
      this.createConnection(fromNeuron, toNeuron, 0.1);
    }      
  }

View Full Code Here

Examples of org.neuroph.core.Neuron






    TransferFunction transferFunction = createTransferFunction(neuronProperties.getTransferFunctionProperties());


    Neuron neuron = null;
    Class neuronClass = neuronProperties.getNeuronType();


                // use two param constructor to create neuron
                    try {
                        Class[] paramTypes = {InputFunction.class, TransferFunction.class};

View Full Code Here

Examples of org.neuroph.core.Neuron


  public static Layer createLayer(Vector<NeuronProperties> neuronPropertiesVector) {
    Layer layer = new Layer();
    
    for(NeuronProperties neuronProperties : neuronPropertiesVector) {
      Neuron neuron = NeuronFactory.createNeuron(neuronProperties);
      layer.addNeuron(neuron);
    }
    
    return layer;
  }

View Full Code Here

Examples of org.neuroph.core.Neuron

   * 
   * @return one or more image labels with the maximum output
   */
  public HashMap<String, Neuron> getMaxOutput() {
    HashMap<String, Neuron> maxOutput = new HashMap<String, Neuron>();
    Neuron maxNeuron = this.getParentNetwork().getOutputNeurons().get(0);


    for (Neuron neuron : this.getParentNetwork().getOutputNeurons()) {
      if (neuron.getOutput() > maxNeuron.getOutput())
        maxNeuron = neuron;
    }


    LabelsPlugin labels = (LabelsPlugin) this.getParentNetwork().getPlugin(
        LabelsPlugin.class);


    maxOutput.put(labels.getLabel(maxNeuron), maxNeuron);


    for (Neuron neuron : this.getParentNetwork().getOutputNeurons()) {
      if (neuron.getOutput() == maxNeuron.getOutput()) {
        maxOutput.put(labels.getLabel(neuron), neuron);
      }
    }


    return maxOutput;

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.