Examples of org.encog.neural.networks.BasicNetwork

org.encog.neural.networks.BasicNetwork
This class implements a neural network. This class works in conjunction the Layer classes. Layers are added to the BasicNetwork to specify the structure of the neural network. The first layer added is the input layer, the final layer added is the output layer. Any layers added between these two layers are the hidden layers. The network structure is stored in the structure member. It is important to call: network.getStructure().finalizeStructure(); Once the neural network has been completely constructed.

    Assert.assertArrayEquals(model.getWeightIndex(),pruned.getWeightIndex());
  }
  
  public void testPruneNeuronInput()
  {
    BasicNetwork network = obtainNetwork();
    Assert.assertEquals(2, network.getInputCount());
    PruneSelective prune = new PruneSelective(network);
    prune.prune(0, 1);
    Assert.assertEquals(22, network.encodedArrayLength());
    Assert.assertEquals(1,network.getLayerNeuronCount(0));
    Assert.assertEquals("1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,22,23,25", network.dumpWeights());
    
    BasicNetwork model = EncogUtility.simpleFeedForward(1,3,0,4,false);
    checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
    Assert.assertEquals(1, network.getInputCount());
  }

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    Assert.assertEquals(1, network.getInputCount());
  }
  
  public void testPruneNeuronHidden()
  {
    BasicNetwork network = obtainNetwork();
    PruneSelective prune = new PruneSelective(network);
    prune.prune(1, 1);
    Assert.assertEquals(18, network.encodedArrayLength());
    Assert.assertEquals(2,network.getLayerNeuronCount(1));
    Assert.assertEquals("1,3,4,5,7,8,9,11,12,13,15,16,17,18,19,23,24,25", network.dumpWeights());
    
    BasicNetwork model = EncogUtility.simpleFeedForward(2,2,0,4,false);
    checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());    
  }

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    checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());    
  }
  
  public void testPruneNeuronOutput()
  {
    BasicNetwork network = obtainNetwork();
    Assert.assertEquals(4, network.getOutputCount());
    PruneSelective prune = new PruneSelective(network);
    prune.prune(2, 1);
    Assert.assertEquals(21, network.encodedArrayLength());
    Assert.assertEquals(3,network.getLayerNeuronCount(2));
    Assert.assertEquals("1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25", network.dumpWeights());
    
    BasicNetwork model = EncogUtility.simpleFeedForward(2,3,0,3,false);
    checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
    Assert.assertEquals(3, network.getOutputCount());
  }

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    Assert.assertEquals(3, network.getOutputCount());
  }
  
  public void testNeuronSignificance()
  {
    BasicNetwork network = obtainNetwork();    
    PruneSelective prune = new PruneSelective(network);
    double inputSig = prune.determineNeuronSignificance(0, 1);
    double hiddenSig = prune.determineNeuronSignificance(1, 1);
    double outputSig = prune.determineNeuronSignificance(2, 1);
    Assert.assertEquals(63.0, inputSig,0.01);

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    Assert.assertEquals(26.0, outputSig,0.01);
  }
  
  public void testIncreaseNeuronCountHidden()
  {
    BasicNetwork network = XOR.createTrainedXOR();
    Assert.assertTrue( XOR.verifyXOR(network, 0.10) );
    PruneSelective prune = new PruneSelective(network);
    prune.changeNeuronCount(1, 5);
    
    BasicNetwork model = EncogUtility.simpleFeedForward(2,5,0,1,false);
    checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
    
    Assert.assertTrue( XOR.verifyXOR(network, 0.10) );
  }

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  }
  
  public void testRandomizeNeuronInput()
  {
    double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
    BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
    NetworkCODEC.arrayToNetwork(d, network);
    PruneSelective prune = new PruneSelective(network);
    prune.randomizeNeuron(100, 100, 0,1);
    Assert.assertEquals("0,0,0,0,0,100,0,0,100,0,0,100,0", network.dumpWeights());
  }

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  }
  
  public void testRandomizeNeuronHidden()
  {
    double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
    BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
    NetworkCODEC.arrayToNetwork(d, network);
    PruneSelective prune = new PruneSelective(network);
    prune.randomizeNeuron(100, 100, 1,1);
    Assert.assertEquals("0,100,0,0,0,0,0,100,100,100,0,0,0", network.dumpWeights());
  }

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  }
  
  public void testRandomizeNeuronOutput()
  {
    double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
    BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
    NetworkCODEC.arrayToNetwork(d, network);
    PruneSelective prune = new PruneSelective(network);
    prune.randomizeNeuron(100, 100, 2,0);
    Assert.assertEquals("100,100,100,100,0,0,0,0,0,0,0,0,0", network.dumpWeights());
  }

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public class TestClone extends TestCase {


  public void testClone() throws Throwable
  {
    BasicNetwork source = XOR.createThreeLayerNet();
    source.reset();
    
    BasicNetwork target = (BasicNetwork)source.clone();
    
    TestCase.assertTrue(target.equals(source));
  }

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    FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(3);
    pattern.addHiddenLayer(50);
    pattern.setOutputNeurons(1);
    pattern.setActivationFunction(new ActivationTANH());
    BasicNetwork network = (BasicNetwork)pattern.generate();
    network.reset();
    return network;
  }

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