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

   * Compute the outstar layer.
   * @param input The input.
   * @return The output.
   */
  public final MLData computeOutstar(final MLData input) {
    final MLData result = new BasicMLData(this.outstarCount);


    double sum = 0;


    for (int i = 0; i < this.outstarCount; i++) {
      sum = 0;
      for (int j = 0; j < this.instarCount; j++) {
        sum += this.weightsInstarToOutstar.get(j, i) * input.getData(j);
      }
      result.setData(i, sum);
    }
    return result;
  }

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Examples of org.encog.ml.data.MLData

    }


    double worstDistance = Double.NEGATIVE_INFINITY;


    for (final MLDataPair pair : this.training) {
      final MLData out = this.network.computeInstar(pair.getInput());


      // determine winner
      final int winner = EngineArray.indexOfLargest(out.getData());


      // calculate the distance
      double distance = 0;
      for (int i = 0; i < pair.getInput().size(); i++) {
        final double diff = pair.getInput().getData(i)

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Examples of org.encog.ml.data.MLData

                "Please enter a valid input number.");
          }
          input.setData(i, value);
        }


        final MLData output = getData().compute(input);


        for (int i = 0; i < this.outputCount; i++) {
          this.outputTable.setValueAt(output.getData(i), i, 1);
        }
      } catch (EncogError ex) {
        EncogWorkBench.displayError("Query Error", ex.getMessage());
      } catch (Throwable t) {
        ErrorDialog.handleError(t,this.getEncogObject(), null);

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Examples of org.encog.ml.data.MLData

    }


    final ErrorCalculation error = new ErrorCalculation();


    for (final MLDataPair pair : this.training) {
      final MLData out = this.network.computeInstar(pair.getInput());


      final int j = EngineArray.indexOfLargest(out.getData());
      for (int i = 0; i < this.network.getOutstarCount(); i++) {
        final double delta = this.learningRate
            * (pair.getIdeal().getData(i) - this.network
                .getWeightsInstarToOutstar().get(j, i));
        this.network.getWeightsInstarToOutstar().add(j, i, delta);
      }


      final MLData out2 = this.network.computeOutstar(out);
      error.updateError(out2.getData(), pair.getIdeal().getData(), pair.getSignificance());
    }


    setError(error.calculate());
  }

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Examples of org.encog.ml.data.MLData


  public void performQuery() {
    try {
      int outputCount = ((MLOutput) method).getOutputCount();
      boolean[] grid = this.panel.getGrid();
      MLData input = new BasicMLData(grid.length);
      for (int i = 0; i < grid.length; i++) {
        input.setData(i, grid[i] ? 1 : -1);
      }


      if (classification) {
        int output = ((MLClassification) this.method).classify(input);
        this.outputTable.setValueAt(output, 0, 1);
      } else {
        MLData output = ((MLRegression) this.method).compute(input);
        for (int i = 0; i < outputCount; i++) {
          this.outputTable.setValueAt(output.getData(i), i, 1);
        }
      }
    } catch (EncogError ex) {
      EncogWorkBench.displayError("Query Error", ex.getMessage());
    } catch (Throwable t) {

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Examples of org.encog.ml.data.MLData

  /**
   * {@inheritDoc}
   */
  @Override
  public final MLData compute(final MLData input) {
    final MLData output = new BasicMLData(getOutputCount());
    this.flat.compute(input.getData(), output.getData());
    return output;
  }

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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)

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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)

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Examples of org.encog.ml.data.MLData

      throw new EncogError(
          "Can't use the SVM yet, it has not been trained, "
          + "and no model exists.");
    }


    final MLData result = new BasicMLData(1);


    final svm_node[] formattedInput = makeSparse(input);


    final double d = svm.svm_predict(this.model, formattedInput);
    result.setData(0, d);


    return result;
  }

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Examples of org.encog.ml.data.MLData

   *            The training set to evaluate.
   */
  public static void evaluate(final MLRegression network,
      final MLDataSet training) {
    for (final MLDataPair pair : training) {
      final MLData output = network.compute(pair.getInput());
      System.out.println("Input="
          + EncogUtility.formatNeuralData(pair.getInput())
          + ", Actual=" + EncogUtility.formatNeuralData(output)
          + ", Ideal="
          + EncogUtility.formatNeuralData(pair.getIdeal()));

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