Examples of ca.nengo.util.impl.RandomHypersphereVG

ca.nengo.util.impl.RandomHypersphereVG
Generates random vectors distributed on or in a hypersphere. TODO: Reference Deak, Muller @author Bryan Tripp


            if (encodingSign != null) {
                if (encodingDistribution == null) {
                    encodingDistribution = 0f;
                }
                VectorGenerator vectorGen = new RandomHypersphereVG(true, 1, encodingDistribution);
                if (encodingSign == Sign.Positive) {
                    vectorGen = new Rectifier(vectorGen, true);
                } else if (encodingSign == Sign.Negative) {
                    vectorGen = new Rectifier(vectorGen, false);
                }

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   * Test method for 'ca.nengo.util.impl.RandomHypersphereVG.genVectors(int, int)'
   */
  public void testGenVectors() {
    VectorGenerator vg = null; 
    
    vg = new RandomHypersphereVG(true, 1f, 1f);
    float[][] v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      float radius = (float) Math.pow(v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2], .5);
      TestUtil.assertClose(1f, radius, .0001f);
    }


    vg = new RandomHypersphereVG(true, 1f, 0f);
    v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      float radius = (float) Math.pow(v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2], .5);
      TestUtil.assertClose(1f, radius, .0001f);
    }


    vg = new RandomHypersphereVG(true, 1f, .5f);
    v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      float radius = (float) Math.pow(v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2], .5);
      TestUtil.assertClose(1f, radius, .0001f);
    }
    
    vg = new RandomHypersphereVG(false, 1f, 1f);
    v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      float radius = (float) Math.pow(v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2], .5);
      assertTrue(radius < 1f);
    }
    
    vg = new RandomHypersphereVG(true, 2f, 1f);
    v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      float radius = (float) Math.pow(v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2], .5);
      TestUtil.assertClose(2f, radius, .0001f);
    }


    vg = new RandomHypersphereVG(true, 1f, 1f);
    v = vg.genVectors(10, 3);
    for (int i = 0; i < v.length; i++) {
      TestUtil.assertClose(1f, Math.abs(v[i][0]) + Math.abs(v[i][1]) + Math.abs(v[i][2]), .0001f);
    }
  }

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    /**
     * @param radius Radius
     */
    public void setRadius(float radius) {
      myRadius = radius;
      myVG = new RandomHypersphereVG(false, radius, 1);
    }

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    /**
     * @param radius Radius
     */
    public void setRadius(float radius) {
      myRadius = radius;
      myVG = new RandomHypersphereVG(true, radius, 1);
    }

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  /**
   * Default constructor. Sets up factories.
   */
  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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    NEFEnsembleFactory ef = new NEFEnsembleFactoryImpl() {
      private static final long serialVersionUID = 1L;
      protected void addDefaultOrigins(NEFEnsemble ensemble) {} //wait until some neurons are adjusted
    };
    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));

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    float[] result = new float[getDimensions()];


    if (myNode instanceof NEFEnsemble) {
      NEFEnsemble ensemble = (NEFEnsemble) myNode;


      VectorGenerator vg = new RandomHypersphereVG(false, 1, 0);
      float[][] unscaled = vg.genVectors(samples, ensemble.getDimension());
      float[][] input = new float[unscaled.length][];
      for (int i = 0; i < input.length; i++) {
        input[i] = MU.prodElementwise(unscaled[i], ensemble.getRadii());
      }

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Related Classes of ca.nengo.util.impl.RandomHypersphereVG

ca.nengo.math.impl.IndependentDimensionApproximator$EncoderFactory

ca.nengo.math.impl.IndependentDimensionApproximator$EvalPointFactory

ca.nengo.model.nef.impl.BiasOrigin

ca.nengo.model.nef.impl.DecodedOrigin

ca.nengo.model.nef.impl.NEFEnsembleFactoryImpl

ca.nengo.ui.models.constructors.CNEFEnsemble

ca.nengo.util.impl.RandomHypersphereVGTest

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