Package org.encog.neural.networks.training.propagation.resilient

Examples of org.encog.neural.networks.training.propagation.resilient.ResilientPropagation.iteration()

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    jordanPattern.setOutputNeurons(ideal);
    BasicNetwork network = (BasicNetwork)jordanPattern.generate();
    MLDataSet training = RandomTrainingFactory.generate(1000, 5, network.getInputCount(), network.getOutputCount(), -1, 1);
    ResilientPropagation prop = new ResilientPropagation(network,training);
    prop.iteration();
    prop.iteration();   
  }
 
  public void testElman() 
  {   
    performElmanTest(1,2,1);
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  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();   
   
    ResilientPropagation rprop = new ResilientPropagation(network,trainingData);
    rprop.iteration();
    rprop.iteration();
    network.enableConnection(1, 0, 0, false);
    network.enableConnection(1, 1, 0, false);
   
    Assert.assertTrue(network.getStructure().isConnectionLimited());
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    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();   
   
    ResilientPropagation rprop = new ResilientPropagation(network,trainingData);
    rprop.iteration();
    rprop.iteration();
    network.enableConnection(1, 0, 0, false);
    network.enableConnection(1, 1, 0, false);
   
    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
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    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
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    Assert.assertTrue(network.getStructure().isConnectionLimited());
   
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);   
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    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);   
    rprop.finishTraining();
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    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    rprop.iteration();
    // these connections were removed, and should not have been "trained"
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[0], 0.01);
    Assert.assertEquals(0.0, network.getStructure().getFlat().getWeights()[1], 0.01);   
    rprop.finishTraining();
  }
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    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    final MLTrain train = new ResilientPropagation(network, trainingSet);
    //
    int epoch = 1;
    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01 && epoch<5000);
   
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      train.addStrategy(strat);
      train.setThreadCount(1); // force single thread mode

      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }

      error = Math.min(error, train.getError());
    }
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    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);
   
    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
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