Examples of com.greentea.relaxation.jnmf.model.Layer

Package com.greentea.relaxation.jnmf.model

Examples of com.greentea.relaxation.jnmf.model.Layer

com.greentea.relaxation.jnmf.model.Layer
Class Layer represents the layer of neurons. The feature of layer is that all neurons within it executing at the same time.

   public void beforeStep()
   {
      if (isFirstStep)
      {
         isFirstStep = false;
         Layer lastLayer = builder.getNetwork().getLayers().getLast();
         for (int i = 0; i < targetImage.length(); ++i)
         {
            Neuron owner = lastLayer.getNeurons().get(i);
            Double value = targetImage.charAt(i) == '0' ? -1.0 : 1.0;
            owner.getOutputSynapses().get(0).sendSignal(owner, value);
//            ThresholdFunction function 
//               = (ThresholdFunction) owner.getActivationFunction();
//            function.setThresholdValue(getValue);

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      return ZERO;
   }


   public DoubleList getResult()
   {
      Layer last = builder.getNetwork().getLayers().getLast();


      DoubleList result = new ArrayDoubleList();
      for (Neuron neuron : last.getNeurons())
      {
         result.add(neuron.getActivation());
      }


      return result;

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      createClusters(data, p, 5);
   }


   public int getClustersCount()
   {
      Layer lastLayer = getNetwork().getLayers().getLast();
      return lastLayer.getNeurons().size();
   }

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      return lastLayer.getNeurons().size();
   }


   public DoubleList resolveClusterCenter(int clusterNum)
   {
      Layer lastLayer = getNetwork().getLayers().getLast();
      Neuron clusterNeuron = lastLayer.getNeurons().get(clusterNum);


      DoubleList res = new ArrayDoubleList(clusterNeuron.getInputSynapses().size());
      for (Synapse s : clusterNeuron.getInputSynapses())
      {
         res.add(s.getWeight());

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   public int getCluster(DoubleList input)
   {
      step(input);


      Layer lastLayer = getNetwork().getLayers().getLast();
      return lastLayer.getMinActivationNeuronIndex();
   }

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      for (int i = 0; i < learningData.getInputsCount(); ++i)
      {
         builder.addDataInputSynapseGenerative(i, 1, i);
      }


      Layer outputLayer = new Layer();
      builder.getNetwork().addLayer(outputLayer);
      builder.setSynapseClass(SignalDiferenceSynapse.class);
      builder.setNeuronClass(OutputNeuron.class);
   }

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   private void changeStateToLearningCandidates()
   {
      out(CasCorState.CANDIDATES_COLLECT_INFO.toString() + " = " + (++newLayersCount));
      int numNewLayer = builder.getNetwork().getLayers().size() - 1;
      builder.getNetwork().getLayers().add(numNewLayer, new Layer());


      candidates.clear();
      for (int i = 0; i < candidatesCount; ++i)
      {
         candidates.add(builder.addNeuronGenerative(numNewLayer));


         for (int j = 0; j < numNewLayer; ++j)
         {
            Layer layer = builder.getNetwork().getLayers().get(j);
            for (int k = 0; k < layer.getNeurons().size(); ++k)
            {
               double w = JNMFMathUtils
                       .reflectToInterval(rand.nextDouble(), 0, 1, -inputWeightsOfCandidatesRange,
                               inputWeightsOfCandidatesRange);
               builder.addSynapseGenerative(j, k, w, numNewLayer, i);

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      {
         for (
                 int sourceLayerIndex = 0;
                 sourceLayerIndex < network.getLayers().size(); ++sourceLayerIndex)
         {
            Layer sourceLayer = network.getLayers().get(sourceLayerIndex);


            for (int i = 0; i < sourceLayer.getNeurons().size(); ++i)
            {
               Neuron sourceNeuron = sourceLayer.getNeurons().get(i);
               String layerString = (i == 0) ? "" + sourceLayerIndex : emptyString;
               String neuronString = "" + sourceNeuronkIndex;


               Color layerColor = (sourceLayerIndex % 2 == 0) ? CellColor.EvenClass.getColor() :
                       CellColor.OddClass.getColor();
               Color neuronColor = CellColor.DefaultCell.getColor();
               row1Colors.add(layerColor);
               row2Colors.add(neuronColor);


               layersRow1.add(layerString);
               neuronsRow2.add(neuronString);


               Vector<Object> contentRow = new Vector<Object>();
               contentRow.add(layerString);
               contentRow.add(neuronString);


               List<Color> rowColors = new ArrayList<Color>();
               rowColors.add(layerColor);
               rowColors.add(neuronColor);


               for (
                       int destLayerIndex = 0;
                       destLayerIndex < network.getLayers().size(); ++destLayerIndex)
               {
                  Layer destLayer = network.getLayers().get(destLayerIndex);
                  for (Neuron destNeuron : destLayer.getNeurons())
                  {
                     double w = 0.0;


                     Color connectionColor = CellColor.DefaultCell.getColor();
                     Collection<Synapse> synapses = CollectionUtils

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Related Classes of com.greentea.relaxation.jnmf.model.Layer

com.greentea.relaxation.algorithms.art2m.Art2mAlgorithm

com.greentea.relaxation.algorithms.cascor.CasCorAlgorithm

com.greentea.relaxation.jnmf.gui.components.project.network.NetworkComponent

com.greentea.relaxation.jnmf.tests.HopfieldAlgorithm

org.apache.commons.collections.primitives.ArrayDoubleList

org.apache.commons.collections.primitives.DoubleList

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