Examples of org.encog.ml.data.basic.BasicMLData

org.encog.ml.data.basic.BasicMLData
Basic implementation of the MLData interface that stores the data in an array. @author jheaton

    {
      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));
  }

View Full Code Here

  
  public void test(CPN network,String[][] pattern,double[][] input)
  {
    for(int i=0;i<pattern.length;i++)
    {
      MLData inputData = new BasicMLData(input[i]);
      MLData outputData = network.compute(inputData);
      double angle = determineAngle(outputData);
      
      // display image
      for(int j=0;j<HEIGHT;j++)

View Full Code Here


    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();

View Full Code Here

          "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();

View Full Code Here

          * OCR.DOWNSAMPLE_WIDTH;
      final int outputNeuron = this.letterListModel.size();


      final MLDataSet trainingSet = new BasicMLDataSet();
      for (int t = 0; t < this.letterListModel.size(); t++) {
        final MLData item = new BasicMLData(inputNeuron);
        int idx = 0;
        final SampleData ds = (SampleData) this.letterListModel
            .getElementAt(t);
        for (int y = 0; y < ds.getHeight(); y++) {
          for (int x = 0; x < ds.getWidth(); x++) {
            item.setData(idx++, ds.getData(x, y) ? .5 : -.5);
          }
        }


        trainingSet.add(new BasicMLDataPair(item, null));
      }

View Full Code Here

    this.gradient = new double[this.parametersLength];
    this.diagonal = new double[this.parametersLength];
    this.errors = new double[this.trainingLength];
    this.jacobian = new double[this.trainingLength][this.parametersLength];


    final BasicMLData input = new BasicMLData(
        this.indexableTraining.getInputSize());
    final BasicMLData ideal = new BasicMLData(
        this.indexableTraining.getIdealSize());
    this.pair = new BasicMLDataPair(input, ideal);


    // setup coefficient arrays for finite difference method
    // create differential coefficient arrays

View Full Code Here

            * der1;
      }
    }


    if (this.network.getOutputMode() == PNNOutputMode.Classification) {
      final MLData result = new BasicMLData(1);
      result.setData(0, ibest);
      return result;
    }


    return null;
  }

View Full Code Here


      for (int i = 0; i < getOutputCount(); i++) {
        out[i] /= psum;
      }


      final MLData result = new BasicMLData(1);
      result.setData(0, EncogMath.maxIndex(out));
      return result;
    } else if (getOutputMode() == PNNOutputMode.Unsupervised) {
      for (int i = 0; i < getInputCount(); i++) {
        out[i] /= psum;
      }
    } else if (getOutputMode() == PNNOutputMode.Regression) {
      for (int i = 0; i < getOutputCount(); i++) {
        out[i] /= psum;
      }
    }


    return new BasicMLData(out);
  }

View Full Code Here

   *            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;
  }

View Full Code Here

          && 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);

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.encog.ml.data.basic.BasicMLData

com.wiieditor.neuralnetwork.NeuralNetworkWrapper

NeuralPilot

org.encog.app.analyst.csv.AnalystClusterCSV

org.encog.app.analyst.csv.AnalystEvaluateCSV

org.encog.app.analyst.csv.AnalystEvaluateRawCSV

org.encog.app.analyst.csv.ClusterRow

org.encog.app.analyst.csv.normalize.AnalystNormalizeToEGB

org.encog.ensemble.aggregator.Averaging

org.encog.ensemble.aggregator.MajorityVoting

org.encog.ensemble.aggregator.MetaClassifier

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.