Package org.encog.neural.networks

Examples of org.encog.neural.networks.BasicNetwork.encodedArrayLength()

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public class TestAnalyzeNetwork extends TestCase {
  public void testAnalyze()
  {
    BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
    double[] weights = new double[network.encodedArrayLength()];
    EngineArray.fill(weights, 1.0);
    network.decodeFromArray(weights);
    AnalyzeNetwork analyze = new AnalyzeNetwork(network);
    Assert.assertEquals(weights.length, analyze.getWeightsAndBias().getSamples());
    Assert.assertEquals(3,analyze.getBias().getSamples());
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    Assert.assertTrue(network.isLayerBiased(2));
    Assert.assertEquals(3, network.getLayerCount());
    Assert.assertTrue(network.getActivation(0) instanceof ActivationLinear );
    Assert.assertTrue(network.getActivation(1) instanceof ActivationTANH );
    Assert.assertTrue(network.getActivation(2) instanceof ActivationLinear );
    Assert.assertEquals(18,network.encodedArrayLength());
    Assert.assertEquals(1,network.getLayerNeuronCount(0));
    Assert.assertEquals(3,network.getLayerNeuronCount(1));
    Assert.assertEquals(4,network.getLayerNeuronCount(2));
  }
 
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  {
    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());
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  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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  {
    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());
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  {
    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());
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  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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  {
    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());
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public class TestAnalyzeNetwork extends TestCase {
  public void testAnalyze()
  {
    BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
    double[] weights = new double[network.encodedArrayLength()];
    EngineArray.fill(weights, 1.0);
    network.decodeFromArray(weights);
    AnalyzeNetwork analyze = new AnalyzeNetwork(network);
    Assert.assertEquals(weights.length, analyze.getWeightsAndBias().getSamples());
    Assert.assertEquals(3,analyze.getBias().getSamples());
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    FreeformNetwork freeformNetwork = new FreeformNetwork(basicNetwork);
    Assert.assertEquals(basicNetwork.getInputCount(),
        freeformNetwork.getInputCount());
    Assert.assertEquals(basicNetwork.getOutputCount(),
        freeformNetwork.getOutputCount());
    Assert.assertEquals(basicNetwork.encodedArrayLength(),
        freeformNetwork.encodedArrayLength());
  }
 
  public void testEncode() {
   
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