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

  
  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++)
      {

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          * 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));
      }

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    } 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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        final double newValue = originalValue + ((i - centerPoint))
            * stepSize[jj];


        this.network.setWeight(layer, weight, neuron, newValue);


        final MLData output = this.network.compute(inputData);
        points[i] = output.getData(0);
      } else {
        points[i] = networkOutput;
      }
    }

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      // Calculate the new error
      sumOfSquaredErrors = 0.0;
      for (int i = 0; i < this.trainingLength; i++) {
        this.indexableTraining.getRecord(i, this.pair);
        final MLData actual = this.network
            .compute(this.pair.getInput());
        final double e = this.pair.getIdeal().getData(0)
            - actual.getData(0);
        sumOfSquaredErrors += e * e;
      }
      sumOfSquaredErrors /= 2.0;


      // Update the objective function

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    int ji = 0;


    // foreach training vector
    for (final MLDataPair pair : getTraining()) {
      final MLData networkOutput = this.network.compute(pair.getInput());


      // Calculate network error to build the residuals vector
      e = pair.getIdeal().getData(0) - networkOutput.getData(0);
      this.errors[ji] = e;
      sumOfSquaredErrors += e * e;


      // Computation of one of the Jacobian Matrix rows by nummerical differentiation:
      // for each weight wj in the network, we have to compute its partial
      //   derivative to build the jacobian matrix.
      int jj = 0;


      // So, for each layer:
      for (int layer = this.network.getLayerCount() - 1; layer > 0; layer--) {
        // for each neuron:
        for (int neuron = 0; neuron < this.network
            .getLayerNeuronCount(layer); neuron++) {
          // for each weight:
          for (int weight = 0; weight < this.network
              .getLayerTotalNeuronCount(layer - 1); weight++) {
            // Compute its partial derivative
            this.jacobian[ji][jj] = computeDerivative(
                pair.getInput(), layer - 1, neuron, weight,
                this.derivativeStepSize,
                networkOutput.getData(0), jj);
            jj++;
          }
        }
      }

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      training.getRecord(r, pair);
      this.network.setExclude(this.network.getExclude() - 1);


      err = 0.0;


      final MLData input = pair.getInput();
      final MLData target = pair.getIdeal();


      if (this.network.getOutputMode() == PNNOutputMode.Unsupervised) {
        if (deriv) {
          final MLData output = computeDeriv(input, target);
          for (int z = 0; z < this.network.getOutputCount(); z++) {
            out[z] = output.getData(z);
          }
        } else {
          final MLData output = this.network.compute(input);
          for (int z = 0; z < this.network.getOutputCount(); z++) {
            out[z] = output.getData(z);
          }
        }
        for (int i = 0; i < this.network.getOutputCount(); i++) {
          diff = input.getData(i) - out[i];
          err += diff * diff;
        }
      } else if (this.network.getOutputMode() == PNNOutputMode.Classification) {
        final int tclass = (int) target.getData(0);
        MLData output;


        if (deriv) {
          output = computeDeriv(input, pair.getIdeal());
          output.getData(0);
        } else {
          output = this.network.compute(input);
          output.getData(0);
        }


        out[0] = output.getData(0);


        for (int i = 0; i < out.length; i++) {
          if (i == tclass) {
            diff = 1.0 - out[i];
            err += diff * diff;
          } else {
            err += out[i] * out[i];
          }
        }
      }


      else if (this.network.getOutputMode() == PNNOutputMode.Regression) {
        if (deriv) {
          final MLData output = this.network.compute(input);
          for (int z = 0; z < this.network.getOutputCount(); z++) {
            out[z] = output.getData(z);
          }
        } else {
          final MLData output = this.network.compute(input);
          for (int z = 0; z < this.network.getOutputCount(); z++) {
            out[z] = output.getData(z);
          }
        }
        for (int i = 0; i < this.network.getOutputCount(); i++) {
          diff = target.getData(i) - out[i];
          err += diff * diff;

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            * der1;
      }
    }


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


    return null;
  }

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      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;
      }

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    bmu.reset();


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


    // update the error
    return bmu.getWorstDistance() / 100.0;

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