Package com.imsl.datamining.neural

Class QuasiNewtonTrainer

java.lang.Object
com.imsl.datamining.neural.QuasiNewtonTrainer
All Implemented Interfaces:
Trainer, Serializable
public class QuasiNewtonTrainer extends Object implements Trainer, Serializable
Trains a network using the quasi-Newton method, MinUnconMultiVar.
See Also:

  • Field Details

    • SUM_OF_SQUARES

      public static final QuasiNewtonTrainer.Error SUM_OF_SQUARES
      Compute the sum of squares error. The sum of squares error term is \(e(y,\hat{y})=(y-\hat{y})^2/2\).

      This is the default Error object used by QuasiNewtonTrainer.

  • Constructor Details

    • QuasiNewtonTrainer

      public QuasiNewtonTrainer()
      Constructs a QuasiNewtonTrainer object.

  • Method Details

    • setUseBackPropagation

      public void setUseBackPropagation(boolean flag)
      Sets whether or not to use the back propagation algorithm for gradient calculations during network training.

      By default, the quasi-newton algorithm optimizes the network using numerical gradients. This method directs the quasi-newton trainer to use the back propagation algorithm for gradient calculations during network training. Depending upon the data and network architecture, one approach is typically faster than the other, or is less sensitive to finding local network optima.

      Parameters:
      flag - boolean specifies whether or not to use the back propagation algorithm for gradient calculations. Default value is true.

    • getUseBackPropagation

      public boolean getUseBackPropagation()
      Returns the use back propagation setting.
      Returns:
      a boolean specifying whether or not back propagation is being used for gradient calculations.

    • clone

      protected Object clone()
      Clones a copy of the trainer.
      Overrides:
      clone in class Object

    • setParallelMode

      protected void setParallelMode(ArrayList[] allLogRecords)
      Sets the trainer to be used in multi-threaded EpochTainer.
      Parameters:
      allLogRecords - An ArrayList array containing the log records.

    • setEpochNumber

      protected void setEpochNumber(int num)
      Sets the epoch number for the trainer.
      Parameters:
      num - An int array containing the epoch number.

    • setMaximumStepsize

      public void setMaximumStepsize(double maximumStepsize)
      Sets the maximum step size.
      Parameters:
      maximumStepsize - A nonnegative double value specifying the maximum allowable step size in the optimizer.
      See Also:

    • setMaximumTrainingIterations

      public void setMaximumTrainingIterations(int maximumTrainingIterations)
      Sets the maximum number of iterations to use in a training.
      Parameters:
      maximumTrainingIterations - An int representing the maximum number of training iterations. Default: 100.
      See Also:

    • setStepTolerance

      public void setStepTolerance(double stepTolerance)
      Sets the scaled step tolerance.

      The second stopping criterion for MinUnconMultiVar, the optimizer used by this Trainer, is that the scaled distance between the last two steps be less than the step tolerance.

      Parameters:
      stepTolerance - A double which is the step tolerance. Default: 3.66685e-11.
      See Also:

    • setGradientTolerance

      public void setGradientTolerance(double gradientTolerance)
      Set the gradient tolerance.
      Parameters:
      gradientTolerance - A double specifying the gradient tolerance. Default: cube root of machine precision.
      See Also:

    • getTrainingIterations

      public int getTrainingIterations()
      Returns the number of iterations used during training.
      Returns:
      An int representing the number of iterations used during training.
      See Also:

    • getErrorStatus

      public int getErrorStatus()
      Returns the error status from the trainer.
      Specified by:
      getErrorStatus in interface Trainer
      Returns:
      An int representing the error status from the trainer. Zero indicates that no errors were encountered during training. Any non-zero value indicates that some error condition arose during training. In many cases the trainer is able to recover from these conditions and produce a well-trained network.

      Error Status Condition
      0 No error occurred during training.
      1 The last global step failed to locate a lower point than the current error value. The current solution may be an approximate solution and no more accuracy is possible, or the step tolerance may be too large.
      2 Relative function convergence; both the actual and predicted relative reductions in the error function are less than or equal to the relative function convergence tolerance.
      3 Scaled step tolerance satisfied; the current point may be an approximate local solution, or the algorithm is making very slow progress and is not near a solution, or the step tolerance is too big.
      4 MinUnconMultiVar.FalseConvergenceException thrown by optimizer.
      5 MinUnconMultiVar.MaxIterationsException thrown by optimizer.
      6 MinUnconMultiVar.UnboundedBelowException thrown by optimizer.

      See Also:

    • train

      public void train(Network network, double[][] xData, double[][] yData)
      Trains the neural network using supplied training patterns.

      Each row of xData and yData contains a training pattern. The number of rows in these two arrays must be at least equal to the number of weights in the network.

      Specified by:
      train in interface Trainer
      Parameters:
      network - The Network to be trained.
      xData - An input double matrix containing training patterns. The number of columns in xData must equal the number of nodes in the input layer.
      yData - An output double matrix containing output training patterns. The number of columns in yData must equal the number of perceptrons in the output layer.

    • getErrorValue

      public double getErrorValue()
      Returns the final value of the error function.
      Specified by:
      getErrorValue in interface Trainer
      Returns:
      A double representing the final value of the error function from the last training. Before training, NaN is returned.

    • getErrorGradient

      public double[] getErrorGradient()
      Returns the value of the gradient of the error function with respect to the weights.
      Specified by:
      getErrorGradient in interface Trainer
      Returns:
      A double array whose length is equal to the number of network weights, containing the value of the gradient of the error function with respect to the weights. Before training, null is returned.

    • getLogger

      public static Logger getLogger()
      Returns the Logger object. This is the Logger used to trace this class. It is named com.imsl.datamining.neural.QuasiNewtonTrainer.
      Returns:
      The Logger object, if present, or null.

    • getFormatter

      public static Formatter getFormatter()
      Returns the logging formatter object. Logger support requires JDK1.4. Use with earlier versions returns null.

      The returned Formatter is used as input to Handler.setFormatter(java.util.logging.Formatter) to format the output log.

      Returns:
      The Formatter object, if present, or null.

    • getError

      public QuasiNewtonTrainer.Error getError()
      Returns the function used to compute the error to be minimized.
      Returns:
      The Error object containing the function to be minimized.

    • setError

      public void setError(QuasiNewtonTrainer.Error error)
      Sets the function used to compute the network error.
      Parameters:
      error - The Error object containing the function to be used to compute the network error. The default is to compute the sum of squares error, SUM_OF_SQUARES.