Examples of org.encog.ml.data.MLDataPair

org.encog.ml.data.MLDataPair
Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items. @author jheaton

  @Override
  public final boolean read(final double[] input, final double[] ideal, final double[] significance) {
    if (!iterator.hasNext()) {
      return false;
    } else {
      MLDataPair pair = iterator.next();
      EngineArray.arrayCopy(pair.getInputArray(), input);
      EngineArray.arrayCopy(pair.getIdealArray(), ideal);
      significance[0] = pair.getSignificance();
      return true;
    }
  }

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  /**
   * {@inheritDoc}
   */
  @Override
  public final void write(final double[] input, final double[] ideal, double significance) {
    MLDataPair pair = BasicMLDataPair.createPair(inputSize,
        idealSize);
    EngineArray.arrayCopy(input, pair.getIdealArray());
    EngineArray.arrayCopy(ideal, pair.getIdealArray());
    pair.setSignificance(significance);
  }

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    final long start = System.currentTimeMillis();
    final long stop = start + (10 * Evaluate.MILIS);
    int record = 0;


    MLDataPair pair = BasicMLDataPair.createPair(10, 10);


    int iterations = 0;
    while (System.currentTimeMillis() < stop) {
      iterations++;
      training.getRecord(record++, pair);

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    final long start = System.currentTimeMillis();
    final long stop = start + (10 * Evaluate.MILIS);
    int record = 0;


    MLDataPair pair = BasicMLDataPair.createPair(10, 10);


    int iterations = 0;
    while (System.currentTimeMillis() < stop) {
      iterations++;
      training2.getRecord(record++, pair);

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


      final MLDataPair pair = new BasicMLDataPair(a, b);
      pair.setSignificance(significance[0]);
      this.result.add(pair);


      currentRecord++;
      lastUpdate++;
      if (lastUpdate >= 10000) {

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    final long start = System.currentTimeMillis();
    final long stop = start + (10*Evaluate.MILIS);
    int record = 0;
    
    MLDataPair pair = BasicMLDataPair.createPair(10, 10);
    
    int iterations = 0;
    while( System.currentTimeMillis()<stop ) {
      iterations++;
      training.getRecord(record++, pair);

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    final long start = System.currentTimeMillis();
    final long stop = start + (10*Evaluate.MILIS);
    int record = 0;
    
    MLDataPair pair = BasicMLDataPair.createPair(10, 10);
    
    int iterations = 0;
    while( System.currentTimeMillis()<stop ) {
      iterations++;
      training2.getRecord(record++, pair);

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      }
    }


    this.network.setExclude((int) training.getRecordCount());


    final MLDataPair pair = BasicMLDataPair.createPair(
        training.getInputSize(), training.getIdealSize());


    final double[] out = new double[this.network.getOutputCount()];


    for (int r = 0; r < training.getRecordCount(); r++) {
      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);
        }

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        this.v[vsptr + ivar] = 0.0;
        this.w[wsptr + ivar] = 0.0;
      }
    }


    final MLDataPair pair = BasicMLDataPair.createPair(this.network
        .getSamples().getInputSize(), this.network.getSamples()
        .getIdealSize());


    for (int r = 0; r < this.network.getSamples().getRecordCount(); r++) {


      this.network.getSamples().getRecord(r, pair);


      if (r == this.network.getExclude()) {
        continue;
      }


      dist = 0.0;
      for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
        diff = input.getData(ivar) - pair.getInput().getData(ivar);
        diff /= this.network.getSigma()[ivar];
        this.dsqr[ivar] = diff * diff;
        dist += this.dsqr[ivar];
      }


      if (this.network.getKernel() == PNNKernelType.Gaussian) {
        dist = Math.exp(-dist);
      } else if (this.network.getKernel() == PNNKernelType.Reciprocal) {
        dist = 1.0 / (1.0 + dist);
      }


      truedist = dist;
      if (dist < 1.e-40) {
        dist = 1.e-40;
      }


      if (this.network.getOutputMode() == PNNOutputMode.Classification) {
        pop = (int) pair.getIdeal().getData(0);
        out[pop] += dist;
        vptr = pop * this.network.getInputCount();
        wptr = pop * this.network.getInputCount();
        for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
          temp = truedist * this.dsqr[ivar];
          this.v[vptr + ivar] += temp;
          this.w[wptr + ivar] += temp * (2.0 * this.dsqr[ivar] - 3.0);
        }
      }


      else if (this.network.getOutputMode() == PNNOutputMode.Unsupervised) {
        for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
          out[ivar] += dist * pair.getInput().getData(ivar);
          temp = truedist * this.dsqr[ivar];
          this.v[vsptr + ivar] += temp;
          this.w[wsptr + ivar] += temp
              * (2.0 * this.dsqr[ivar] - 3.0);
        }
        vptr = 0;
        wptr = 0;
        for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
          for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
            temp = truedist * this.dsqr[ivar]
                * pair.getInput().getData(ivar);
            this.v[vptr++] += temp;
            this.w[wptr++] += temp * (2.0 * this.dsqr[ivar] - 3.0);
          }
        }
        psum += dist;
      } else if (this.network.getOutputMode() == PNNOutputMode.Regression) {


        for (ivar = 0; ivar < this.network.getOutputCount(); ivar++) {
          out[ivar] += dist * pair.getIdeal().getData(ivar);
        }
        vptr = 0;
        wptr = 0;
        for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
          for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
            temp = truedist * this.dsqr[ivar]
                * pair.getIdeal().getData(outvar);
            this.v[vptr++] += temp;
            this.w[wptr++] += temp * (2.0 * this.dsqr[ivar] - 3.0);
          }
        }
        for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {

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          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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