Package ca.nengo.util.impl

Examples of ca.nengo.util.impl.TimeSeriesImpl

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    };
   
    TimeSeries1D s1 = new TimeSeries1DImpl(times, v1, Units.UNK);   
    Plotter.plot(s1, "test1");
   
    TimeSeries s3 = new TimeSeriesImpl(times, v3, new Units[]{Units.UNK, Units.UNK, Units.UNK});
    Plotter.plot(s3, "test2");
  }
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    float[] times = MU.makeVector(1, 1, 10);
    float[][] values1 = new float[][]{MU.makeVector(.1f, .1f, 1), MU.makeVector(.2f, .1f, 1.1f)};
    float[][] values2 = new float[][]{MU.makeVector(1.1f, .1f, 2), MU.makeVector(1.2f, .1f, 2.1f)};
    float[][] values3 = new float[][]{MU.makeVector(2.1f, .1f, 3), MU.makeVector(2.2f, .1f, 3.1f)};
    Units[] units = Units.uniform(Units.UNK, 2);
    series.add(new TimeSeriesImpl(times, MU.transpose(values1), units));
    series.add(new TimeSeriesImpl(times, MU.transpose(values2), units));
    series.add(new TimeSeriesImpl(times, MU.transpose(values3), units));
   
    List<SpikePattern> patterns = new ArrayList<SpikePattern>(10);
    SpikePatternImpl p1 = new SpikePatternImpl(2);
    p1.addSpike(0, 1);
    p1.addSpike(1, 2);
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public class RK45IntegratorTest extends TestCase {

  public void testIntegrate() {
    VanderPol system = new VanderPol(new float[]{.1f, .1f});
    Integrator integrator = new RK45Integrator();
    TimeSeries input = new TimeSeriesImpl(new float[]{0, 10f}, new float[][]{new float[0], new float[0]}, new Units[]{});
    TimeSeries result = integrator.integrate(system, input);
   
    assertTrue(result.getTimes().length < 60);
   
    //check results against selected hard-coded values from matlab solution ...
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    Units[] units = new Units[system.getOutputDimension()];
    for (int i = 0; i < units.length; i++) {
      units[i] = system.getOutputUnits(i);
    }
   
    return new TimeSeriesImpl(times.toArray(), values.toArray(), units);
  }
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    Units[] units = new Units[dynamics.getOutputDimension()];
    for (int i = 0; i < units.length; i++) {
      units[i] = dynamics.getOutputUnits(i);
    }
    myDynamicsOutput = new TimeSeriesImpl(new float[]{0}, MU.uniform(1, dynamics.getOutputDimension(), 0), units);

    myMode = SimulationMode.DEFAULT;
    mySupportedModes = new SimulationMode[]{SimulationMode.DEFAULT};
  }
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    Units[] units = new Units[dynamics.getOutputDimension()];
    for (int i = 0; i < units.length; i++) {
      units[i] = dynamics.getOutputUnits(i);
    }
    myDynamicsOutput = new TimeSeriesImpl(new float[]{0}, MU.uniform(1, dynamics.getOutputDimension(), 0), units);

    myMode = SimulationMode.DEFAULT;
    mySupportedModes = new SimulationMode[]{SimulationMode.DEFAULT, SimulationMode.CONSTANT_RATE};
  }
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    System.out.println("Minimum weight without bias: " + MU.min(projection.getWeights()));
    projection.addBias(100, .005f, tauPSC, true, false);
    System.out.println("Minimum weight with bias: " + MU.min(projection.getWeights()));
    pPost.reset();
    network.run(0, 2);
    TimeSeries diff = new TimeSeriesImpl(ideal.getTimes(), MU.difference(ideal.getValues(), pPost.getData().getValues()), ideal.getUnits());
    Plotter.plot(diff, .01f, "positive weights");
   
    projection.removeBias();
    projection.addBias(100, tauPSC/5f, tauPSC, true, true);
    pPost.reset();
    Probe pInter = network.getSimulator().addProbe("post:pre:interneurons", NEFEnsemble.X, true);
    network.run(0, 2);
    diff = new TimeSeriesImpl(ideal.getTimes(), MU.difference(ideal.getValues(), pPost.getData().getValues()), ideal.getUnits());
    Plotter.plot(diff, .01f, "positive weights optimized");
    Plotter.plot(pInter.getData(), .01f, "interneurons");

   
   
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    NEFEnsembleFactoryImpl ef = new NEFEnsembleFactoryImpl();
    NEFEnsembleImpl ensemble = (NEFEnsembleImpl)ef.make("test", 5, 1);
    float[][] vals = new float[2][1];
    vals[0][0] = 1;
    vals[1][0] = 1;
    TimeSeriesImpl targetSignal = new TimeSeriesImpl(new float[]{0,1}, vals, new Units[]{Units.UNK});
    TimeSeriesImpl[] evalSignals = new TimeSeriesImpl[1];
   
    //test the per-dimension eval signals
    evalSignals[0] = new TimeSeriesImpl(new float[]{0,1}, vals, new Units[]{Units.UNK});
    ensemble.addDecodedSignalOrigin("test1", targetSignal, evalSignals, "AXON");
    if(ensemble.getOrigin("test1") == null)
      fail("Error creating per-dimension signal origin");
   
    //test the per-node eval signals
    vals[0] = new float[]{1, 1, 1, 1, 1};
    vals[1] = new float[]{1, 1, 1, 1, 1};
    evalSignals[0] = new TimeSeriesImpl(new float[]{0,1}, vals, new Units[]{Units.UNK,Units.UNK,Units.UNK,Units.UNK,Units.UNK});
    ensemble.addDecodedSignalOrigin("test2", targetSignal, evalSignals, "AXON");
    if(ensemble.getOrigin("test2") == null)
      fail("Error creating per-node signal origin");
  }
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    float[] current = new float[]{0f, .25f, .5f, .75f, 1f, 1.25f, 1.5f, 1.75f, 2f, 2.25f, 2.5f, 2.75f, 3f, 3.5f, 4f, 5f, 6f, 8f, 10f, 15f, 20f, 30f, 40f, 50f, 60f};
    float[] Vm = new float[current.length];
    float[] rt = new float[current.length];
    for (int i = 0; i < current.length; i++) {
      TimeSeries input = new TimeSeriesImpl(new float[]{0, 0.5f},
          new float[][]{new float[]{current[i], 1.0f}, new float[]{current[i], 1.0f}}, new Units[]{Units.uAcm2, Units.UNK});
      TimeSeries output = myIntegrator.integrate(myDynamics, input);
      Vm[i] = output.getValues()[output.getValues().length - 1][0];
      reset(false);
//      Plotter.plot(output, "simulation "+i);
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      for (int i = 0; i < input.length; i++) {
        input[i] = new float[]{current[i], myDopamine};
      }

      TimeSeries output = myIntegrator.integrate(myDynamics,
          new TimeSeriesImpl(time, input, new Units[]{Units.uAcm2, Units.UNK}));

      myMembranePotentialHistory = output;

      if (myMode.equals(SimulationMode.RATE)) {
        float Vm = output.getValues()[0][0];
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Related Classes of ca.nengo.util.impl.TimeSeriesImpl

  • ca.nengo.dynamics.impl.EulerIntegrator
  • ca.nengo.dynamics.impl.ImpulseIntegral
  • ca.nengo.dynamics.impl.RK45Integrator
  • ca.nengo.dynamics.impl.RK45IntegratorTest
  • ca.nengo.io.DelimitedFileExporterTest
  • ca.nengo.model.impl.AbstractEnsemble
  • ca.nengo.model.impl.BasicTermination
  • ca.nengo.model.impl.FunctionInput
  • ca.nengo.model.impl.NetworkArrayImpl
  • ca.nengo.model.impl.NoiseFactory$NoiseImplPDF
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