Documentation of 'jhplot.HBsom' Java class.

Class HBsom

  • public class HBsomextends Object
    The Bayesian self-organizing map (BSOM). This is a method for estimating a probability distribution generating data points on the basis of a Bayesian stochastic model. It is also regarded as a learning method for a kind of neural network. The black dots in the below figure denote artificially generated data points. Based on work of: Akio Utsugi.

    This class is based on: A. Utsugi (1996) ``Topology selection for self-organizing maps", Network: Computation in Neural Systems, vol. 7, no. 4, 727-740.

    A. Utsugi (1997) ``Hyperparameter selection for self-organizing maps", Neural Computation, vol. 9, no. 3, pp. 623-635.

    • Field Detail

      • bsom

        public Bsom bsom
    • Constructor Detail

      • HBsom

        public HBsom()
        Initialize BSOM.
    • Method Detail

      • setNPoints

        public void setNPoints(int units)
        Set number of points for fit
        units - set number of points for fit.
      • setData

        public void setData(P1D p1d)
        Load data to BSOM
        p1d - input data
      • setData

        public void setData(H1D h)
        Load histogram data to BSOM
        p1d - input histogram data
      • doc

        public void doc()
        Show documentation
      • setAlphaBeta

        public void setAlphaBeta(double alpha,                         double beta)
        Set initial alpha and beta parameters. The BSOM model has a pair of hyperparameters: alpha and beta, which represent `the strength of topological constraint' and `the estimate of noise level in data' respectively. You can vary them using the sliders. Observe the variation of the centroid configuration according to the values of the hyperparameters and grasp their meaning. Then try to find the optimal values of the hyperparameters giving the best centroid configuration. Remark that the configuration depends on not only the present values of hyperparameters but also their history. Poor moving of the hyperparameters will lead to a poor local optimal configuration.
        alpha - alpha value
        beta - beta value
      • getResult

        public P1D getResult()
        Get results of training.
        P1D with results.
      • run

        public void run()
        Run the algorithm
      • visible

        public void visible(boolean vis)
        Set frame visible or not
        vis - true if visible
      • setDelta

        public void setDelta(double delta)
        Set calculation precision. Iterations stop if (current(alpha)-previous(alpha) .lt. delta) The value should be very small for best results
        delta - precision
      • getDelta

        public double getDelta()
        Get calculation precision.
      • getNiterations

        public int getNiterations()
        Get number of iterations used for fitting
      • getAlpha

        public double getAlpha()
        Get alpha. The strength of topological constraint
      • getBeta

        public double getBeta()
        Get beta. This is the estimate of noise level in data.
      • visible

        public void visible()
        Set visible frame

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