Examples of MLData

org.encog.ml.data.MLData
Defines an array of data. This is an array of double values that could be used either for input data, actual output data or ideal output data. @author jheaton

Examples of org.encog.ml.data.MLData


    System.out.println("Year\tActual\tPredict\tClosed Loop Predict");


    for (int year = EVALUATE_START; year < EVALUATE_END; year++) {
      // calculate based on actual data
      MLData input = new BasicMLData(WINDOW_SIZE);
      for (int i = 0; i < input.size(); i++) {
        input.setData(i, this.normalizedSunspots[(year - WINDOW_SIZE)
            + i]);
      }
      MLData output = network.compute(input);
      double prediction = output.getData(0);
      this.closedLoopSunspots[year] = prediction;


      // calculate "closed loop", based on predicted data
      for (int i = 0; i < input.size(); i++) {
        input.setData(i, this.closedLoopSunspots[(year - WINDOW_SIZE)
            + i]);
      }
      output = network.compute(input);
      double closedLoopPrediction = output.getData(0);


      // display
      System.out.println((STARTING_YEAR + year) + "\t"
          + f.format(this.normalizedSunspots[year]) + "\t"
          + f.format(prediction) + "\t"

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Examples of org.encog.ml.data.MLData

  }
  
  public int autonomousMoveDirection()
  {
    updateVision();
    MLData result = this.brain.compute(this.vision);
    
    double winningOutput = Double.NEGATIVE_INFINITY;
    int winningDirection = 0;
    
    for(int i=0;i<result.size();i++)
    {
      // determine direction
      int direction = directionFromIndex(i);      
      
      if( this.environment.isWall(this.x, this.y, direction))
        continue;
      
      // evaluate if this is a "winning" direction
      double thisOutput = result.getData(i);
      if( thisOutput>winningOutput)
      {
        winningOutput = thisOutput;
        winningDirection = direction;
      }

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Examples of org.encog.ml.data.MLData

    LinearCongruentialGenerator rand = 
      new LinearCongruentialGenerator(seed);
    
    final BasicMLDataSet result = new BasicMLDataSet();
    for (int i = 0; i < count; i++) {
      final MLData inputData = new BasicMLData(inputCount);


      for (int j = 0; j < inputCount; j++) {
        inputData.setData(j, rand.range(min, max));
      }


      final MLData idealData = new BasicMLData(idealCount);


      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.range(min, max));
      }


      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);

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Examples of org.encog.ml.data.MLData

    
    int inputCount = training.getInputSize();
    int idealCount = training.getIdealSize();
    
    for (int i = 0; i < count; i++) {
      final MLData inputData = new BasicMLData(inputCount);


      for (int j = 0; j < inputCount; j++) {
        inputData.setData(j, rand.range(min, max));
      }


      final MLData idealData = new BasicMLData(idealCount);


      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.range(min, max));
      }


      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      training.add(pair);

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Examples of org.encog.ml.data.MLData


    int currentRecord = 0;
    int lastUpdate = 0;


    while (this.codec.read(input, ideal, significance)) {
      MLData a = null, b = null;


      a = new BasicMLData(input);


      if (this.codec.getIdealSize() > 0) {
        b = new BasicMLData(ideal);

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Examples of org.encog.ml.data.MLData


  private void processNetwork() throws IOException {
    System.out.println("Downsampling images...");


    for (final ImagePair pair : this.imageList) {
      final MLData ideal = new BasicMLData(this.outputCount);
      final int idx = pair.getIdentity();
      for (int i = 0; i < this.outputCount; i++) {
        if (i == idx) {
          ideal.setData(i, 1);
        } else {
          ideal.setData(i, -1);
        }
      }


      final Image img = ImageIO.read(pair.getFile());
      final ImageNeuralData data = new ImageNeuralData(img);

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Examples of org.encog.ml.data.MLData

    } while(train.getError() > 0.01);


    // test the neural network
    System.out.println("Neural Network Results:");
    for(MLDataPair pair: trainingSet ) {
      final MLData output = network.compute(pair.getInput());
      System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
          + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
    }
  }

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Examples of org.encog.ml.data.MLData

    {
      train.iteration();
      System.out.println("Iteration: " + iteration + ", Error:" + train.getError());
    }
    
    MLData data1 = new BasicMLData(SOM_INPUT[0]);
    MLData data2 = new BasicMLData(SOM_INPUT[1]);
    System.out.println("Pattern 1 winner: " + network.winner(data1));
    System.out.println("Pattern 2 winner: " + network.winner(data2));
  }

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Examples of org.encog.ml.data.MLData


    for (int i = 0; i < map.length; i++) {
      map[i] = '?';
    }
    for (int i = 0; i < this.letterListModel.size(); i++) {
      final MLData input = new BasicMLData(5 * 7);
      int idx = 0;
      final SampleData ds = (SampleData) this.letterListModel
          .getElementAt(i);
      for (int y = 0; y < ds.getHeight(); y++) {
        for (int x = 0; x < ds.getWidth(); x++) {
          input.setData(idx++, ds.getData(x, y) ? .5 : -.5);
        }
      }


      final int best = this.net.winner(input);
      map[best] = ds.getLetter();

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Examples of org.encog.ml.data.MLData

          "Error", JOptionPane.ERROR_MESSAGE);
      return;
    }
    this.entry.downSample();


    final MLData input = new BasicMLData(5 * 7);
    int idx = 0;
    final SampleData ds = this.sample.getData();
    for (int y = 0; y < ds.getHeight(); y++) {
      for (int x = 0; x < ds.getWidth(); x++) {
        input.setData(idx++, ds.getData(x, y) ? .5 : -.5);
      }
    }


    final int best = this.net.winner(input);
    final char map[] = mapNeurons();

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