Package org.encog.ml.factory

Examples of org.encog.ml.factory.MLMethodFactory


  }
 
  public void process(String methodName, String methodArchitecture,String trainerName, String trainerArgs,int outputNeurons) {
   
    // first, create the machine learning method
    MLMethodFactory methodFactory = new MLMethodFactory();   
    MLMethod method = methodFactory.create(methodName, methodArchitecture, 2, outputNeurons);
   
    // second, create the data set   
    MLDataSet dataSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
    // third, create the trainer
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    egb.open();
    final int input = egb.getInputCount();
    final int ideal = egb.getIdealCount();
    egb.close();

    final MLMethodFactory factory = new MLMethodFactory();
    final MLMethod obj = factory.create(type, arch, input, ideal);

    EncogDirectoryPersistence.saveObject(resourceFile, obj);

    return false;
  }
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    egb.open();
    final int input = egb.getInputCount();
    final int ideal = egb.getIdealCount();
    egb.close();

    final MLMethodFactory factory = new MLMethodFactory();
    final MLMethod obj = factory.create(type, arch, input, ideal);

    if (obj instanceof BayesianNetwork) {
      final String query = getProp().getPropertyString(
          ScriptProperties.ML_CONFIG_QUERY);
      ((BayesianNetwork) obj).defineClassificationStructure(query);
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    double inputHigh = 1;
    double outputLow = -1;
    double outputHigh = 1;
   
    // Create a basic neural network, just to examine activation functions.
    MLMethodFactory methodFactory = new MLMethodFactory();   
    BasicNetwork network = (BasicNetwork)methodFactory.create(getMethodName(), architecture, 1, 1);
   
    if( network.getLayerCount()<1 ) {
      throw new EncogError("Neural network does not have an output layer.");
    }
   
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  public MLMethod createMethod() {
    if (this.methodType == null) {
      throw new EncogError(
          "Please call selectMethod first to choose what type of method you wish to use.");
    }
    MLMethodFactory methodFactory = new MLMethodFactory();
    MLMethod method = methodFactory.create(methodType, methodArgs, dataset
        .getNormHelper().calculateNormalizedInputCount(), this.config
        .determineOutputCount(dataset));
    return method;
  }
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