Package de.jungblut.math.minimize

Class GradientDescent

  • All Implemented Interfaces:
    Minimizer
    public final class GradientDescent
extends AbstractMinimizer
    Gradient descent implementation with some neat features like momentum, divergence detection, delta breaks and bold driver and scheduled annealing adaptive learning rates. For more sophisticated configuration use the GradientDescent.GradientDescentBuilder.
    Author:
    thomas.jungblut

    • Constructor Summary

      Constructors 
      Constructor Description
      GradientDescent​(double alpha, double limit)  

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      de.jungblut.math.DoubleVector minimize​(CostFunction f, de.jungblut.math.DoubleVector pInput, int maxIterations, boolean verbose)
      Minimizes the given costfunction with the starting parameter theta.
      static de.jungblut.math.DoubleVector minimizeFunction​(CostFunction f, de.jungblut.math.DoubleVector pInput, double alpha, double limit, int length, boolean verbose)
      Minimize a given cost function f with the initial parameters pInput (also called theta) with a learning rate alpha and a fixed number of iterations.

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

      • GradientDescent

        public GradientDescent​(double alpha,
                       double limit)
        Parameters:
        alpha - the learning rate.
        limit - the delta in cost to archieve to break the iterations.

    • Method Detail

      • minimize

        public final de.jungblut.math.DoubleVector minimize​(CostFunction f,
                                                    de.jungblut.math.DoubleVector pInput,
                                                    int maxIterations,
                                                    boolean verbose)
        Description copied from interface: Minimizer
        Minimizes the given costfunction with the starting parameter theta.
        Parameters:
        f - the costfunction to minimize.
        pInput - the starting parameters.
        maxIterations - the number of iterations to do.
        verbose - if TRUE it will print progress.
        Returns:
        the optimized theta parameters.

      • minimizeFunction

        public static de.jungblut.math.DoubleVector minimizeFunction​(CostFunction f,
                                                             de.jungblut.math.DoubleVector pInput,
                                                             double alpha,
                                                             double limit,
                                                             int length,
                                                             boolean verbose)
        Minimize a given cost function f with the initial parameters pInput (also called theta) with a learning rate alpha and a fixed number of iterations. The loop can break earlier if costs converge below the limit. If the same cost was archieved three times in a row, it will also break the iterations.
        Parameters:
        f - the function to minimize.
        pInput - the starting parameters.
        alpha - the learning rate.
        limit - the cost to archieve to break the iterations.
        length - the number of iterations.
        verbose - if true prints progress to STDOUT.
        Returns:
        the learned minimal parameters.