Examples of ca.nengo.model.nef.NEFEnsembleFactory.make()

Class ca.nengo.model.nef.NEFEnsembleFactory

Examples of ca.nengo.model.nef.NEFEnsembleFactory.make()

ca.nengo.model.nef.NEFEnsembleFactory.make()
@param name Name of the NEFEnsemble @param n Number of neurons in the ensemble @param dim Dimension of the ensemble. @return NEFEnsemble containing Neurons generated with the default NeuronFactory @throws StructuralException if there is any error attempting to create the ensemble

                }
                ef.setEncoderFactory(vectorGen);
            }


            if (radius==null) {
                NEFEnsemble ensemble = ef.make(name, numOfNeurons, dimensions);
                return ensemble;
            } else {
                float[] radii=new float[dimensions];
                for (int i=0; i<dimensions; i++) {
                    radii[i]=radius.floatValue();

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            } else {
                float[] radii=new float[dimensions];
                for (int i=0; i<dimensions; i++) {
                    radii[i]=radius.floatValue();
                }
                NEFEnsemble ensemble = ef.make(name, numOfNeurons, radii);
                return ensemble;
            }
        } catch (StructuralException e) {
          Util.debugMsg("StructuralException: " + e.toString());

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   * Test method for 'ca.nengo.sim.impl.WriteToDiskSimulatorListener'
   */
  public void testBasicWriting() throws StructuralException, SimulationException {
    Network network = new NetworkImpl();
    NEFEnsembleFactory factory = new NEFEnsembleFactoryImpl();
    NEFEnsemble ensemble = factory.make("Ensemble",100,2);
    File file = new File("testWTDSL.csv");
    
    network.addNode(ensemble);
    
    Probe probe = network.getSimulator().addProbe("Ensemble", "X", true);

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   * Test method for 'ca.nengo.sim.impl.WriteToDiskSimulatorListener'
   */
  public void testInterval() throws StructuralException, SimulationException, FileNotFoundException {
    Network network = new NetworkImpl();
    NEFEnsembleFactory factory = new NEFEnsembleFactoryImpl();
    NEFEnsemble ensemble = factory.make("Ensemble",100,2);
    File file = new File("testWTDSL.csv");
    
    network.addNode(ensemble);
    
    float interval = 0.005f;

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    Network network = new NetworkImpl();
    FunctionInput input = new FunctionInput("input", new Function[]{new IdentityFunction(1, 0)}, Units.UNK);
    network.addNode(input);
    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
    int n = 300;
    NEFEnsemble pre = ef.make("pre", n, 2);
    pre.addDecodedTermination("input", MU.uniform(2, 1, 1), .005f, false);
    network.addNode(pre);
    network.addProjection(input.getOrigin(FunctionInput.ORIGIN_NAME), pre.getTermination("input"));
    NEFEnsemble post = ef.make("post", n, 2);
    network.addNode(post);

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    int n = 300;
    NEFEnsemble pre = ef.make("pre", n, 2);
    pre.addDecodedTermination("input", MU.uniform(2, 1, 1), .005f, false);
    network.addNode(pre);
    network.addProjection(input.getOrigin(FunctionInput.ORIGIN_NAME), pre.getTermination("input"));
    NEFEnsemble post = ef.make("post", n, 2);
    network.addNode(post);
    post.addDecodedTermination("input", MU.I(2), .01f, false);
    Projection p = network.addProjection(pre.getOrigin(NEFEnsemble.X), post.getTermination("input"));


    DecodedOrigin o = (DecodedOrigin) pre.getOrigin(NEFEnsemble.X);

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    FunctionInput input = new FunctionInput("input", new Function[]{new SineFunction(5)}, Units.UNK);
    network.addNode(input);
    
    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
    NEFEnsemble ensemble = ef.make("ensemble", 100, 1);
    ensemble.addDecodedTermination("input", MU.I(1), .005f, false);
    ensemble.collectSpikes(true);
    network.addNode(ensemble);
    
    network.addProjection(input.getOrigin(FunctionInput.ORIGIN_NAME), ensemble.getTermination("input"));

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        LinearNeuronFactory lnf = new LinearNeuronFactory(new IndicatorPDF(200, 400), new IndicatorPDF(-1, 1), true);
        NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
        ef.setNodeFactory(lnf);


        try {
            NEFEnsemble ensemble = ef.make("test", 20, 1);
            ensemble.addDecodedTermination("input", MU.I(1), .01f, false);
            Plotter.plot(ensemble);


            Network network = new NetworkImpl();
            network.addNode(ensemble);

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    FunctionInput input = new FunctionInput("input", new Function[]{f}, Units.UNK);
    network.addNode(input);
    
    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
    
    NEFEnsemble integrator = ef.make("integrator", 500, 1, "integrator1", true);  
    network.addNode(integrator);
    integrator.collectSpikes(true);


    Plotter.plot(integrator);
    Plotter.plot(integrator, NEFEnsemble.X);

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  public static void rasterMemoryTest() {
    try {
      Network network = new NetworkImpl();
      
      NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl();
      final NEFEnsemble ensemble = ef.make("ensemble", 200, 1);
      ensemble.collectSpikes(true);
      network.addNode(ensemble);
      
      FunctionInput input = new FunctionInput("input", new Function[]{new ConstantFunction(1, 1)}, Units.UNK);
      network.addNode(input);

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