Examples of org.encog.ml.data.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

  /**
   * {@inheritDoc}
   */
  @Override
  public final int classify(final MLData input) {
    final MLData result = compute(input);
    return EngineArray.maxIndex(result.getData());
  }

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   *            The input pattern.
   * @return The winning neuron.
   */
  public final MLData compute(final MLData input) {


    final MLData result = new BasicMLData(this.outputNeuronCount);


    for (int i = 0; i < this.outputNeuronCount; i++) {
      final Matrix optr = this.weights.getCol(i);
      final Matrix inputMatrix = Matrix.createRowMatrix(input.getData());
      result.setData(i, MatrixMath.dotProduct(inputMatrix, optr));
    }


    return result;
  }

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   *            The input pattern.
   * @return The winning neuron.
   */
  public final int winner(final MLData input) {


    final MLData output = compute(input);
    final int win = EngineArray.indexOfLargest(output.getData());
    return win;
  }

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          && section.getSubSectionName().equals("SAMPLES")) {
        for (final String line : section.getLines()) {
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          int index = 0;
          final MLData inputData = new BasicMLData(inputCount);
          for (int i = 0; i < inputCount; i++) {
            inputData.setData(i,
                CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          }
          final MLData idealData = new BasicMLData(inputCount);
          for (int i = 0; i < outputCount; i++) {
            idealData.setData(i,
                CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          }
          final MLDataPair pair = new BasicMLDataPair(inputData,
              idealData);
          samples.add(pair);

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      final MLData leastRepresented) {


    double maxActivation = Double.MIN_VALUE;
    int maxActivationNeuron = -1;


    final MLData output = this.network.compute(leastRepresented);


    // Loop over all of the output neurons. Consider any neurons that were
    // not the BMU (winner) for any pattern. Track which of these
    // non-winning neurons had the highest activation.
    for (int outputNeuron = 0; outputNeuron < won.length; outputNeuron++) {
      // Only consider neurons that did not "win".
      if (won[outputNeuron] == 0) {
        if ((maxActivationNeuron == -1)
            || (output.getData(outputNeuron) > maxActivation)) {
          maxActivation = output.getData(outputNeuron);
          maxActivationNeuron = outputNeuron;
        }
      }
    }

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    // Reset the BMU and begin this iteration.
    this.bmuUtil.reset();
    final int[] won = new int[this.outputNeuronCount];
    double leastRepresentedActivation = Double.MAX_VALUE;
    MLData leastRepresented = null;


    // Reset the correction matrix for this synapse and iteration.
    this.correctionMatrix.clear();


    // Determine the BMU for each training element.
    for (final MLDataPair pair : getTraining()) {
      final MLData input = pair.getInput();


      final int bmu = this.bmuUtil.calculateBMU(input);


      // If we are to force a winner each time, then track how many
      // times each output neuron becomes the BMU (winner).
      if (this.forceWinner) {
        won[bmu]++;


        // Get the "output" from the network for this pattern. This
        // gets the activation level of the BMU.
        final MLData output = this.network.compute(pair.getInput());


        // Track which training entry produces the least BMU. This
        // pattern is the least represented by the network.
        if (output.getData(bmu) < leastRepresentedActivation) {
          leastRepresentedActivation = output.getData(bmu);
          leastRepresented = pair.getInput();
        }
      }


      train(bmu, this.network.getWeights(), input);

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   * @param pattern
   *            The pattern to train.
   */
  public final void trainPattern(final MLData pattern) {


    final MLData input = pattern;
    final int bmu = this.bmuUtil.calculateBMU(input);
    train(bmu, this.network.getWeights(), input);
    applyCorrection();


  }

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    ImageNeuralData input = new ImageNeuralData(image);
    set.add(input,ideal);
    set.downsample(2,2);
    Iterator<MLDataPair> itr = set.iterator();
    MLDataPair pair = (MLDataPair)itr.next();
    MLData data = pair.getInput();
    double[] d = data.getData();
    //Assert.assertEquals(d[0],-1.0, 0.1);
    //Assert.assertEquals(d[5],1, 0.1);
    
    // just "flex" these for no exceptions
    input.toString();

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    public static boolean verifyXOR(MLRegression network,double tolerance) 
    {
      for(int trainingSet=0;trainingSet<XOR.XOR_IDEAL.length;trainingSet++)
      {
        MLData actual = network.compute(new BasicMLData(XOR.XOR_INPUT[trainingSet]));
        
        for(int i=0;i<XOR.XOR_IDEAL[0].length;i++)
        {
          double diff = Math.abs(actual.getData(i)-XOR.XOR_IDEAL[trainingSet][i]);
          if( diff>tolerance )
            return false;
        }
        
      }

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    public static MLDataSet createNoisyXORDataSet(int count) {
      MLDataSet result = new BasicMLDataSet();
      for(int i=0;i<count;i++) {
        for(int j=0;j<4;j++) {
          MLData inputData = new BasicMLData(XOR_INPUT[j]);
          MLData idealData = new BasicMLData(XOR_IDEAL[j]);
          MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          inputData.setData(0, inputData.getData(0)+RangeRandomizer.randomize(-0.1, 0.1));
          inputData.setData(1, inputData.getData(1)+RangeRandomizer.randomize(-0.1, 0.1));
          result.add(pair);
        }

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