Examples of NeuralNetwork

com.github.neuralnetworks.architecture.NeuralNetwork
this interface is implemented by everything that wants to present itself as a black box with with a list of input/output layers for example these could be whole neural network taking part in committee of machines, single convolutional/subsamplingo layers or even a single connection between the layers
com.wiieditor.neuralnetwork.NeuralNetwork
@author zohaibrauf

org.neuroph.core.NeuralNetwork

 Base class for artificial neural networks. It provides generic structure and functionality for the neural networks. Neural network contains a collection of neuron layers and learning rule. Custom neural networks are created by deriving from this class, creating layers of interconnected network specific neurons, and setting network specific learning rule.

@see Layer @see LearningRule @author Zoran Sevarac

tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork
weka.classifiers.pmml.consumer.NeuralNetwork
Class implementing import of PMML Neural Network model. Can be used as a Weka classifier for prediction (buildClassifier() raises an Exception). @author Mark Hall (mhall{[at]}pentaho{[dot]}com) @version $Revision 1.0 $
zdenekdrahos.AI.NeuralNetwork.NeuralNetwork

Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

  public void testCollectNetworks() throws Exception {


  
    NeuralNetworkUtil util = new NeuralNetworkUtil();
    
    NeuralNetwork nn0 =  buildXORMLP();
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0));
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(1));
    util.AccumulateWorkerNetwork( nn0 );

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Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

  @Test
  public void testAverageTwoCollectedNetworks() throws Exception {
    
    NeuralNetworkUtil util = new NeuralNetworkUtil();


    NeuralNetwork nn0 =  buildXORMLP();
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0));
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(1));
    
    nn0.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(0).setWeight(new Weight(2));
    nn0.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(1).setWeight(new Weight(3));
    
    nn0.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(0).setWeight(new Weight(4));
    nn0.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(1).setWeight(new Weight(5));


    // output layer
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0.1));
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0.2));
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(2).setWeight(new Weight(0.3));
    
    
    util.AccumulateWorkerNetwork( nn0 );


    NeuralNetwork nn1 =  buildXORMLP();
    nn1.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(1));
    nn1.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0));


    nn1.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(0).setWeight(new Weight(4));
    nn1.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(1).setWeight(new Weight(5));
    
    nn1.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(0).setWeight(new Weight(6));
    nn1.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(1).setWeight(new Weight(7));


    // output layer
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0.4));
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0.6));
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(2).setWeight(new Weight(0.8));
    
    
    util.AccumulateWorkerNetwork( nn1 );

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Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

  @Test
  public void testAverageThreeCollectedNetworks() throws Exception {
    
    NeuralNetworkUtil util = new NeuralNetworkUtil();


    NeuralNetwork nn0 =  buildXORMLP();
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0));
    nn0.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(1));
/*    
    nn0.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(0).setWeight(new Weight(2));
    nn0.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(1).setWeight(new Weight(3));
    
    nn0.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(0).setWeight(new Weight(4));
    nn0.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(1).setWeight(new Weight(5));


    // output layer
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0.1));
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0.2));
    nn0.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(2).setWeight(new Weight(0.3));
    */
    
    util.AccumulateWorkerNetwork( nn0 );


    NeuralNetwork nn1 =  buildXORMLP();
    nn1.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(1));
    nn1.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0));
/*
    nn1.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(0).setWeight(new Weight(4));
    nn1.getLayerByIndex(1).getNeuronAt(1).getInConnections().get(1).setWeight(new Weight(5));
    
    nn1.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(0).setWeight(new Weight(6));
    nn1.getLayerByIndex(1).getNeuronAt(2).getInConnections().get(1).setWeight(new Weight(7));


    // output layer
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(0.4));
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(0.6));
    nn1.getLayerByIndex(2).getNeuronAt(0).getInConnections().get(2).setWeight(new Weight(0.8));
    
*/    
    util.AccumulateWorkerNetwork( nn1 );
    


    NeuralNetwork nn2 =  buildXORMLP();
    nn2.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(0).setWeight(new Weight(2));
    nn2.getLayerByIndex(1).getNeuronAt(0).getInConnections().get(1).setWeight(new Weight(5));
    util.AccumulateWorkerNetwork( nn2 );
    
    //NeuralNetwork nn_out = util.AverageNetworkWeights();
    
    NetworkAccumulator accumNet = NetworkAccumulator.buildAveragingNetworkFromConf(nn0.getConfig());

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Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
    
    NeuralNetwork nn = new MultiLayerPerceptronNetwork();
    
    try {
      nn.buildFromConf(c);
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }

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Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
    
    NeuralNetwork nn = new MultiLayerPerceptronNetwork();
    
    try {
      nn.buildFromConf(c);
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
    /*

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Examples of tv.floe.metronome.classification.neuralnetworks.core.NeuralNetwork

    }
    
    // TODO: we need some sort of factory setup to rebuild a network based on
    // the config object
    // create accumulation network to return
    NeuralNetwork averaged_network = this.buildAveragingNetworkFromConf(this.worker_networks.get(0).getConfig());
    // mock up the layer count / neuron counts
    
    
    for (int x = 0; x < this.worker_networks.size(); x++ ) {

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Examples of weka.classifiers.pmml.consumer.NeuralNetwork

    case GENERAL_REGRESSION_MODEL:
      pmmlM = new GeneralRegression(model, dataDictionary, miningSchema);
      //System.out.println(pmmlM);
      break;
    case NEURAL_NETWORK_MODEL:
      pmmlM = new NeuralNetwork(model, dataDictionary, miningSchema);
      break;
    case TREE_MODEL:
      pmmlM = new TreeModel(model, dataDictionary, miningSchema);
      break;
    case RULESET_MODEL:

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Examples of zdenekdrahos.AI.NeuralNetwork.NeuralNetwork

    @Override
    public INeuralNetwork build(int[] topology, Activations[] functions) {
        if (topology.length != functions.length) {
            throw new IllegalArgumentException("Layers & functions - different length");
        }
        INeuralNetwork network = new NeuralNetwork();
        ILayer layer;
        for (int i = 0; i < topology.length; i++) {
            layer = new Layer(topology[i], getActivation(functions[i]));
            network.addLayer(layer);
        }
        network.generateWeights();
        return network;
    }

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