Package com.imsl.datamining.neural

Class LeastSquaresTrainer

java.lang.Object
com.imsl.datamining.neural.LeastSquaresTrainer
All Implemented Interfaces:
Trainer, Serializable
public class LeastSquaresTrainer extends Object implements Trainer, Serializable
Trains a FeedForwardNetwork using a Levenberg-Marquardt algorithm for minimizing a sum of squares error.
See Also:

  • Constructor Details

    • LeastSquaresTrainer

      public LeastSquaresTrainer()
      Creates a LeastSquaresTrainer.

  • Method Details

    • 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 stepsize in the optimizer. Default: \(10^3 || w ||_2\), where w are the values of the weights in the network when training starts.
      See Also:

    • setInitialTrustRegion

      public void setInitialTrustRegion(double initialTrustRegion)
      Sets the intial trust region.
      Parameters:
      initialTrustRegion - A double which specifies the initial trust region radius. Default: unlimited trust region.
      See Also:

    • setMaximumTrainingIterations

      public void setMaximumTrainingIterations(int maximumSolverIterations)
      Sets the maximum number of iterations used by the nonlinear least squares solver.
      Parameters:
      maximumSolverIterations - An int which specifies the maximum number of iterations to be used by the nonlinear least squares solver. Its default value is 1000.
      See Also:

    • setRelativeTolerance

      public void setRelativeTolerance(double relativeTolerance)
      Sets the relative tolerance.
      Parameters:
      relativeTolerance - A double which specifies the relative error tolerance. It must be in the interval [0,1]. Its default value is 1.0e-20.
      See Also:

    • setFalseConvergenceTolerance

      public void setFalseConvergenceTolerance(double falseConvergenceTolerance)
      Set the false convergence tolerance.
      Parameters:
      falseConvergenceTolerance - a double specifying the false convergence tolerance. Default: 1.0e-14.
      See Also:

    • setGradientTolerance

      public void setGradientTolerance(double gradientTolerance)
      Set the gradient tolerance.
      Parameters:
      gradientTolerance - A double specifying the gradient tolerance. Default: 2.0e-5.
      See Also:

    • setStepTolerance

      public void setStepTolerance(double stepTolerance)
      Set the step tolerance used to step between weights.
      Parameters:
      stepTolerance - A double which specifies the scaled step tolerance to use when changing the weights. Default: 1.0e-5.
      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. These number of rows in two arrays must be equal.

      Specified by:
      train in interface Trainer
      Parameters:
      network - The Network to be trained.
      xData - A double matrix which contains the input training patterns. The number of columns in xData must equal the number of Nodes in the InputLayer.
      yData - A double matrix which contains the output training patterns. The number of columns in yData must equal the number of Perceptrons in the OutputLayer.

    • getErrorValue

      public double getErrorValue()
      Returns the final value of the error function.
      Specified by:
      getErrorValue in interface Trainer
      Returns:
      A double containing 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.

    • getErrorStatus

      public int getErrorStatus()
      Returns the error status from the trainer.
      Specified by:
      getErrorStatus in interface Trainer
      Returns:
      An int which contains the error status. 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.

      ValueMeaning
      0All convergence tests were met.
      1Scaled step tolerance was 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 StepTolerance is too big.
      2Scaled actual and predicted reductions in the function are less than or equal to the relative function convergence tolerance RelativeTolerance.
      3Iterates appear to be converging to a noncritical point. Incorrect gradient information, a discontinuous function, or stopping tolerances being too tight may be the cause.
      4Five consecutive steps with the maximum stepsize have been taken. Either the function is unbounded below, or has a finite asymptote in some direction, or the maximum stepsize is too small.
      5Too many iterations required