Examples of Ensemble

ca.nengo.model.Ensemble
A group of Nodes with largely overlapping inputs and outputs.

There are no strict rules for how to group Nodes into Ensembles, but here are some things to consider:
- A group of Nodes that together 'represent' something through a population code should be modelled as an Ensemble. (Also consider using NEFEnsemble to make such representation explicit.)
- Making ensembles that correspond to physical structures (e.g. nuclei) and naming them appropriately will make the model clearer.
- Outputs from an Ensemble are grouped together and passed to other Ensembles during a simulation, and practical issues may arise from this. For example, putting all your Nodes in a single large ensemble could result in a very large matrix of synaptic weights, which would impair performance.
The membership of an Ensemble is fixed once the Ensemble is created. This means that the Ensemble model doesn't deal explicitly with growth and death of components during simulation (although you can set input/output weights to zero to mimic this). It also means that an Ensemble isn't a good model of a functional "assembly".
@author Bryan Tripp
edu.uci.jforestsx.learning.trees.Ensemble
@author Yasser Ganjisaffar

Examples of ca.nengo.model.Ensemble


                UINeuron neuronUI = new UINeuron(neuron);


                addUINode(neuronUI, false, false);
              } else if (node instanceof Ensemble) {
                Ensemble ensemble = (Ensemble)node;
                UIEnsemble ensembleUI = new UIEnsemble(ensemble);
                addUINode(ensembleUI,false,false);
                
              } else if (node instanceof Network) {
                Network network = (Network)node;

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Examples of ca.nengo.model.Ensemble

      getProbeParent().getNetworkParent().getSimulator().removeProbe(getModel());
      getProbeParent().showPopupMessage("Probe removed from Simulator");
      
      Probe model = getModel();
      if(model.isInEnsemble()){
        Ensemble target = (Ensemble) model.getTarget();
        target.stopProbing(model.getStateName());
      }
    } catch (SimulationException e) {
      UserMessages.showError("Could not remove probe: " + e.getMessage());
    }

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Examples of ca.nengo.model.Ensemble

      result.myNode = node;
      
      // get origins for nodes in new ensemble
      Origin[] origins = new Origin[myNodeOrigins.length];
      if (node instanceof Ensemble) {
        Ensemble ensemble = (Ensemble)node;
        for (int i = 0; i < myNodeOrigins.length; i++)
          origins[i] = ensemble.getNodes()[i].getOrigin(myNodeOrigins[i].getName());
      }
      if (node instanceof Network) {
        Network network = (Network)node;
        for (int i = 0; i < myNodeOrigins.length; i++)
          origins[i] = network.getNodes()[i].getOrigin(myNodeOrigins[i].getName());

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Examples of ca.nengo.model.Ensemble

    // get terminations for nodes in new ensemble
    result.myNodeTerminations = myNodeTerminations.clone();
    if (node instanceof Ensemble || node instanceof Network) {
      try {
        if (node instanceof Ensemble) {
          Ensemble ensemble = (Ensemble)node;
          for (int i = 0; i < result.myNodeTerminations.length; i++){
            result.myNodeTerminations[i] = ensemble.getNodes()[i].getTermination(myNodeTerminations[i].getName());
          }
        }
        if (node instanceof Network) {
          Network network = (Network)node;
          for (int i = 0; i < result.myNodeTerminations.length; i++){

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Examples of ca.nengo.model.Ensemble


  /*
   * Test method for 'ca.bpt.cn.model.impl.NetworkImpl.getNodes()'
   */
  public void testGetNodes() throws StructuralException {
    Ensemble a = new MockEnsemble("a");
    myNetwork.addNode(a);
    myNetwork.addNode(new MockEnsemble("b"));


    assertEquals(2, myNetwork.getNodes().length);

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Examples of ca.nengo.model.Ensemble

    return numDead;
  }
  
  public void testAddNode() throws StructuralException
  {
    Ensemble a = new MockEnsemble("a");
    
    try
    {
      myNetwork.getNode("a");
      fail("Node is present in network when it shouldn't be");

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Examples of ca.nengo.model.Ensemble

    
  }
  
  public void testRemoveNode() throws StructuralException, SimulationException
  {
    Ensemble a = new MockEnsemble("a");
    
    myNetwork.addNode(a);
    if(myNetwork.getNode("a") == null)
      fail("Node not added");

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Examples of ca.nengo.model.Ensemble

      Probeable target = oldProbe.getTarget();
      if (target instanceof Node) {
        Node oldNode = (Node) target;
        if (oldProbe.isInEnsemble()) {
          try {
            Ensemble oldEnsemble = (Ensemble) getNode(oldProbe.getEnsembleName());
            int neuronIndex = -1;
            for (int j = 0; j < oldEnsemble.getNodes().length && neuronIndex < 0; j++) {
              if (oldNode == oldEnsemble.getNodes()[j]) {
                                neuronIndex = j;
                            }
            }
            result.mySimulator.addProbe(oldProbe.getEnsembleName(), neuronIndex, oldProbe.getStateName(), true);
          } catch (SimulationException e) {

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Examples of ca.nengo.model.Ensemble

        Node[] nodes = network.getNodes();


        boolean childCollecting = false;
        for (Node node : nodes) {
            if (node instanceof Ensemble) {
                Ensemble ensemble = (Ensemble) node;


                if (ensemble.isCollectingSpikes()) {
                    SortableMutableTreeNode ensNode = createSortableNode(top, ensemble);
                    /*
                     * Make a clone of the data
                     */
                    SpikePattern spikePattern = (SpikePattern) Util.cloneSerializable(ensemble.getSpikePattern());
                    DefaultMutableTreeNode spNode = new SpikePatternNode(spikePattern);
                    ensNode.add(spNode);


                    childCollecting = true;
                }

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Examples of edu.uci.jforestsx.learning.trees.Ensemble


    curValidSet = validSet;
    validPredictions.setSample(curValidSet);
    validPredictions.reset();


    Ensemble ensemble = new Ensemble();
    subLearner.setTreeWeight(treeWeight / bagCount);
    for (int iteration = 1; iteration <= bagCount; iteration++) {
      System.out.println("Iteration: " + iteration);
      Sample subLearnerTrainSet = trainSet.getRandomSubSample(baggingTrainFraction, rnd);
      //((TreeLearner) subLearner).setRnd();
      Sample subLearnerOutOfTrainSet = trainSet.getOutOfSample(subLearnerTrainSet);
      Sample subLearnerValidSet = (validSet == null || validSet.isEmpty() ? subLearnerOutOfTrainSet : validSet);
      Ensemble subEnsemble = subLearner.learn(subLearnerTrainSet, subLearnerValidSet);


      for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
        Tree tree = subEnsemble.getTreeAt(t);
        double curTreeWeight = subEnsemble.getWeightAt(t);
        if (backfit) {
          tree.backfit(subLearnerOutOfTrainSet);
        }
        ensemble.addTree(tree, curTreeWeight);
        System.out.println(tree.numLeaves);
      }


      if (validSet != null && !validSet.isEmpty()) {
        for (int t = 0; t < subEnsemble.getNumTrees(); t++) {
          validPredictions.update(subEnsemble.getTreeAt(t), 1.0 / bagCount);
        }
        lastValidMeasurement = validPredictions.evaluate(evaluationMetric);
        if (printIntermediateValidMeasurements) {
          printValidMeasurement(iteration, lastValidMeasurement, evaluationMetric);
        }

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