Examples of ca.nengo.math.impl.IndicatorPDF

ca.nengo.math.impl.IndicatorPDF
Uniform probability between upper and lower limits, zero elsewhere. @author Bryan Tripp

   * @param weights Each row is used as a 1 by m matrix of weights in a new termination on the nth expandable node
   *
   * @see ca.nengo.model.ExpandableNode#addTermination(java.lang.String, float[][], float, boolean)
   */
    public synchronized Termination addTermination(String name, float[][] weights, float tauPSC, boolean modulatory) throws StructuralException {
      return addTermination(name, weights, new IndicatorPDF(tauPSC,tauPSC), null, modulatory);
  }

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            myGain[i] = neuron.getScale();
        }
        
        // If initial theta not passed in, randomly generate
        if (initialTheta == null) {
          IndicatorPDF uniform = new IndicatorPDF(0.00001f, 0.00002f);
          myInitialTheta = new float[nodeTerminations.length];
          for (int i = 0; i < myInitialTheta.length; i ++) {
              // Reasonable assumption: high gain, high theta
            myInitialTheta[i] = uniform.sample()[0] * myGain[i];
          }
        } else {
          myInitialTheta = initialTheta;
        }
        myTheta = myInitialTheta.clone();

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    /**
     * Set reasonable defaults
     */
    public SigmoidFactory() {
      mySlope = new IndicatorPDF(1, 10);
      myInflection = new IndicatorPDF(-1f, 1f);
      myMaxRate = new IndicatorPDF(200, 400);
    }

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  /**
   * Uses default parameters. 
   */
  public ALIFNeuronFactory() {
    this(new IndicatorPDF(200, 400), new IndicatorPDF(-.9f, .9f), new IndicatorPDF(.1f, .2f), .001f, .02f, .2f);
  }

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  /**
   * Uses default parameters. 
   */
  public LIFNeuronFactory() {
    this(.02f, .001f, new IndicatorPDF(200, 400), new IndicatorPDF(-.9f, .9f));
  }

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    /**
     * Sets reasonable defaults
     */
    public Factory() {
      myTauRef = new IndicatorPDF(.002f);
      myTauRC = new IndicatorPDF(.02f);
      myTauN = new IndicatorPDF(.2f);
      myIncN = new IndicatorPDF(.1f);
    }

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    };
    ef.setEncoderFactory(new Rectifier(ef.getEncoderFactory(), true));
    ef.setEvalPointFactory(new BiasedVG(new RandomHypersphereVG(false, 0.5f, 0f), 0, excitatoryProjection ? .5f : -.5f));


//    PDF interceptPDF = excitatoryProjection ? new IndicatorPDF(-.5f, .75f) : new IndicatorPDF(-.99f, .35f);
    PDF interceptPDF = excitatoryProjection ? new IndicatorPDF(-.15f, .9f) : new IndicatorPDF(-1.2f, .1f); //was -.5f, .75f for excitatory
    PDF maxRatePDF = excitatoryProjection ? new IndicatorPDF(200f, 500f) : new IndicatorPDF(400f, 800f);
    ef.setNodeFactory(new LIFNeuronFactory(.02f, .0001f, maxRatePDF, interceptPDF));
    ef.setApproximatorFactory(new GradientDescentApproximator.Factory(
        new GradientDescentApproximator.CoefficientsSameSign(true), false));


    NEFEnsemble result = ef.make(name, num, 1);

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   */
  public NEFEnsembleFactoryImpl() {
    myApproximatorFactory = new WeightedCostApproximator.Factory(0.1f);
    myEncoderFactory = new RandomHypersphereVG(true, 1f, 0f);
    myEvalPointFactory = new RandomHypersphereVG(false, 1f, 0f);
    myNodeFactory = new LIFNeuronFactory(.02f, .002f, new IndicatorPDF(200f, 400f), new IndicatorPDF(-.9f, .9f));
  }

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ca.nengo.model.neuron.impl.ALIFNeuronFactory

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