Examples of RbfNetwork

Examples of com.heatonresearch.aifh.learning.RBFNetwork

77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
        // Create random particles for the RBF.
        GenerateRandom rnd = new MersenneTwisterGenerateRandom();
        RBFNetwork[] particles = new RBFNetwork[TitanicConfig.ParticleCount];
        for (int i = 0; i < particles.length; i++) {
            particles[i] = new RBFNetwork(TitanicConfig.InputFeatureCount, TitanicConfig.RBF_COUNT, 1);
            particles[i].reset(rnd);
        }

        /**
         * Construct a network to hold the best network.
         */
        if (bestNetwork == null) {
            bestNetwork = new RBFNetwork(TitanicConfig.InputFeatureCount, TitanicConfig.RBF_COUNT, 1);
        }

        /**
         * Setup the scoring function.
         */
        ScoreFunction score = new ScoreTitanic(training);
        ScoreFunction scoreValidate = new ScoreTitanic(validation);

        /**
         * Setup particle swarm.
         */
        boolean done = false;
        TrainPSO train = new TrainPSO(particles, score);
        int iterationNumber = 0;
        StringBuilder line = new StringBuilder();

        do {
            iterationNumber++;

            train.iteration();

            RBFNetwork best = (RBFNetwork) train.getBestParticle();

            double trainingScore = train.getLastError();
            double validationScore = scoreValidate.calculateScore(best);

            if (validationScore > bestScore) {
                System.arraycopy(best.getLongTermMemory(), 0, this.bestNetwork.getLongTermMemory(), 0, best.getLongTermMemory().length);
                this.bestScore = validationScore;
            }

            if (validationScore > localBest) {
                noImprove = 0;
View Full Code Here

Examples of com.heatonresearch.aifh.learning.RBFNetwork

74
75
76
77
78
79
80
81
82
83
84
            ds.normalizeRange(2, -1, 1);
            ds.normalizeRange(3, -1, 1);
            final Map<String, Integer> species = ds.encodeOneOfN(4);
            istream.close();

            RBFNetwork network = new RBFNetwork(4, 4, 3);

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);

            ScoreFunction score = new ScoreRegressionData(trainingData);
View Full Code Here

Examples of com.heatonresearch.aifh.learning.RBFNetwork

85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
            final Map<String, Integer> species = ds.encodeOneOfN(4);
            istream.close();

            RBFNetwork[] particles = new RBFNetwork[PARTICLE_COUNT];
            for (int i = 0; i < particles.length; i++) {
                particles[i] = new RBFNetwork(4, 4, 3);
                particles[i].reset(rnd);
            }

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 3);

            ScoreFunction score = new ScoreRegressionData(trainingData);

            TrainPSO train = new TrainPSO(particles, score);

            performIterations(train, 100000, 0.05, true);

            RBFNetwork winner = (RBFNetwork) train.getBestParticle();

            queryOneOfN(winner, trainingData, species);


        } catch (Throwable t) {
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
 
  public void testPersistNetworkRBF()
  {
    MLDataSet trainingSet = XOR.createXORDataSet();
    RBFNetwork network = new RBFNetwork(2,4,1, RBFEnum.Gaussian);

    SVDTraining training = new SVDTraining(network,trainingSet);
    training.iteration();
    XOR.verifyXOR(network, 0.1);
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    RBFNetwork network2 = (RBFNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);

    XOR.verifyXOR(network2, 0.1);   
  }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

87
88
89
90
91
92
93
94
   *
   * @return The neural network.
   */
  public final MLMethod generate() {

    RBFNetwork result = new RBFNetwork(inputNeurons, this.hiddenNeurons ,outputNeurons,this.rbfType);
    return result;
  }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
        int numNeurons = (int)Math.pow(numNeuronsPerDimension, dimensions);
        int numEdges = (int)(dimensions * Math.pow(2, dimensions - 1));

        pattern.addHiddenLayer(numNeurons);

        RBFNetwork network = (RBFNetwork)pattern.generate();

        //Position the multidimensional RBF neurons, with equal spacing, within the provided sample space from 0 to 1.
        network.setRBFCentersAndWidthsEqualSpacing(0, 1, RBFEnum.Gaussian, volumeNeuronWidth, includeEdgeRBFs);

        //Create some training data that can not easily be represented by gaussians
        //There are other training examples for both 1D and 2D
        //Degenerate training data only provides outputs as 1 or 0 (averaging over all outputs for a given set of inputs would produce something approaching the smooth training data).
        //Smooth training data provides true values for the provided input dimensions.
        create2DSmoothTainingDataGit();

        //Create the training set and train.
        MLDataSet trainingSet = new BasicMLDataSet(INPUT, IDEAL);
        MLTrain train = new SVDTraining(network, trainingSet);

        //SVD is a single step solve
        int epoch = 1;
        do
        {
            train.iteration();
            System.out.println("Epoch #" + epoch + " Error:" + train.getError());
            epoch++;
        } while ((epoch < 1) && (train.getError() > 0.001));

        // test the neural network
        System.out.println("Neural Network Results:");

        //Create a testing array which may be to a higher resoltion than the original training data
        set2DTestingArrays(100);
        trainingSet = new BasicMLDataSet(INPUT, IDEAL);

        FileWriter outFile = new FileWriter("results.csv");
        PrintWriter out = new PrintWriter(outFile);
       
       
            for (MLDataPair pair : trainingSet)
            {
                MLData output = network.compute(pair.getInput());
                //1D//sw.WriteLine(InverseScale(pair.Input[0]) + ", " + Chop(InverseScale(output[0])));// + ", " + pair.Ideal[0]);
                out.println(inverseScale(pair.getInputArray()[0]) + ", " + inverseScale(pair.getInputArray()[1]) + ", " + chop(inverseScale(output.getData(0))));// + ", " + pair.Ideal[0]);// + ",ideal=" + pair.Ideal[0]);
                //3D//sw.WriteLine(InverseScale(pair.Input[0]) + ", " + InverseScale(pair.Input[1]) + ", " + InverseScale(pair.Input[2]) + ", " + Chop(InverseScale(output[0])));// + ", " + pair.Ideal[0]);// + ",ideal=" + pair.Ideal[0]);
                //Console.WriteLine(pair.Input[0] + ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]);
            }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

79
80
81
82
83
84
85
86
87
88
  }
 
  public void testFactoryRBF() {
    String architecture = "?->GAUSSIAN(c=4)->?";
    MLMethodFactory factory = new MLMethodFactory();
    RBFNetwork network = (RBFNetwork)factory.create(MLMethodFactory.TYPE_RBFNETWORK, architecture, 1, 4);
    Assert.assertEquals(1,network.getInputCount());
    Assert.assertEquals(4,network.getOutputCount());
    Assert.assertEquals(4,network.getRBF().length); 
  }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

82
83
84
85
86
87
88
89
90
91
      t = RBFEnum.MexicanHat;
    } else {
      t = RBFEnum.Gaussian;
    }

    final RBFNetwork result = new RBFNetwork(inputCount,
        rbfLayer.getCount(), outputCount, t);

    return result;
  }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

87
88
89
90
91
92
93
94
95
96
    final ParamsHolder holder = new ParamsHolder(rbfLayer.getParams());

    final int rbfCount = holder.getInt("C", true, 0);

    final RBFNetwork result = new RBFNetwork(inputCount, rbfCount,
        outputCount, t);

    return result;
  }
View Full Code Here

Examples of org.encog.neural.rbf.RBFNetwork

438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
    }
   
    report.endTable();
   
    if (this.method instanceof RBFNetwork) {
      RBFNetwork rbfNetwork = (RBFNetwork)this.method;
     
      report.h3("RBF Centers");
      report.beginTable();
      report.beginRow();
      report.header("RBF");
      report.header("Peak");
      report.header("Width");
      for(int i=1;i<=rbfNetwork.getInputCount();i++) {
        report.header("Center " + i);
      }
      report.endRow();
     
     
      for( RadialBasisFunction rbf : rbfNetwork.getRBF() ) {
        report.beginRow();
        report.cell(rbf.getClass().getSimpleName());
        report.cell(Format.formatDouble(rbf.getPeak(), 5));
        report.cell(Format.formatDouble(rbf.getWidth(), 5));
        for(int i=0;i<rbfNetwork.getInputCount();i++) {
          report.cell(Format.formatDouble(rbf.getCenter(i), 5));
        }
        report.endRow();
      }
    }
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. All rights reserved.
All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.