Package aima.core.util.math

Examples of aima.core.util.math.Vector

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  public void processError(Vector error) {
    Matrix weightUpdate = error.times(lastInput.transpose());
    layer.acceptNewWeightUpdate(weightUpdate);

    Vector biasUpdate = layer.getBiasVector().plus(error);
    layer.acceptNewBiasUpdate(biasUpdate);

  }
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    for (int i = 0; i < numberofEpochs; i++) {
      innds.refreshDataset();
      while (innds.hasMoreExamples()) {
        NNExample nne = innds.getExampleAtRandom();
        processInput(nne.getInput());
        Vector error = layer.errorVectorFrom(nne.getTarget());
        processError(error);
      }
    }
  }
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  public int[] testOnDataSet(NNDataSet nnds) {
    int[] result = new int[] { 0, 0 };
    nnds.refreshDataset();
    while (nnds.hasMoreExamples()) {
      NNExample nne = nnds.getExampleAtRandom();
      Vector prediction = predict(nne);
      if (nne.isCorrect(prediction)) {
        result[0] = result[0] + 1;
      } else {
        result[1] = result[1] + 1;
      }
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    penultimateWeightUpdateMatrix = new Matrix(
        weightMatrix.getRowDimension(),
        weightMatrix.getColumnDimension());

    this.biasVector = biasVector;
    lastBiasUpdateVector = new Vector(biasVector.getRowDimension());
    penultimateBiasUpdateVector = new Vector(biasVector.getRowDimension());
  }
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        weightMatrix.getColumnDimension());
    penultimateWeightUpdateMatrix = new Matrix(
        weightMatrix.getRowDimension(),
        weightMatrix.getColumnDimension());

    this.biasVector = new Vector(numberOfNeurons);
    lastBiasUpdateVector = new Vector(biasVector.getRowDimension());
    penultimateBiasUpdateVector = new Vector(biasVector.getRowDimension());

    initializeMatrix(weightMatrix, lowerLimitForWeights,
        upperLimitForWeights);
    initializeVector(biasVector, lowerLimitForWeights, upperLimitForWeights);
  }
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  public Vector feedForward(Vector inputVector) {
    lastInput = inputVector;
    Matrix inducedField = weightMatrix.times(inputVector).plus(biasVector);

    Vector inducedFieldVector = new Vector(numberOfNeurons());
    for (int i = 0; i < numberOfNeurons(); i++) {
      inducedFieldVector.setValue(i, inducedField.get(i, 0));
    }

    lastInducedField = inducedFieldVector.copyVector();
    Vector resultVector = new Vector(numberOfNeurons());
    for (int i = 0; i < numberOfNeurons(); i++) {
      resultVector.setValue(i, activationFunction
          .activation(inducedFieldVector.getValue(i)));
    }
    // set the result as the last activation value
    lastActivationValues = resultVector.copyVector();
    return resultVector;
  }
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    weightMatrix.plusEquals(lastWeightUpdateMatrix);
  }

  public void updateBiases() {
    Matrix biasMatrix = biasVector.plusEquals(lastBiasUpdateVector);
    Vector result = new Vector(biasMatrix.getRowDimension());
    for (int i = 0; i < biasMatrix.getRowDimension(); i++) {
      result.setValue(i, biasMatrix.get(i, 0));
    }
    biasVector = result;
  }
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    }
    return new NNExample(newInput, newTarget);
  }

  public Vector getInput() {
    Vector v = new Vector(normalizedInput);
    return v;

  }
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    return v;

  }

  public Vector getTarget() {
    Vector v = new Vector(normalizedTarget);
    return v;

  }
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        .getSensitivityMatrix().times(alpha).times(-1.0);

    Matrix biasUpdateMatrixWithMomentum = layer.getLastBiasUpdateVector()
        .times(momentum)
        .plus(biasUpdateMatrixWithoutMomentum.times(1.0 - momentum));
    Vector result = new Vector(
        biasUpdateMatrixWithMomentum.getRowDimension());
    for (int i = 0; i < biasUpdateMatrixWithMomentum.getRowDimension(); i++) {
      result.setValue(i, biasUpdateMatrixWithMomentum.get(i, 0));
    }
    layer.acceptNewBiasUpdate(result.copyVector());
    return result;
  }
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Related Classes of aima.core.util.math.Vector

  • aima.core.learning.neural.BackPropLearning
  • aima.core.learning.neural.FeedForwardNeuralNetwork
  • aima.core.learning.neural.Layer
  • aima.core.learning.neural.NNExample
  • aima.core.learning.neural.Perceptron
  • aima.test.core.unit.learning.neural.BackPropagationTests
  • aima.test.core.unit.learning.neural.LayerTests
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