Package opennlp.tools.ml.maxent.quasinewton

Klasse QNMinimizer

java.lang.Object
opennlp.tools.ml.maxent.quasinewton.QNMinimizer
public class QNMinimizer extends Object
Implementation of L-BFGS which supports L1-, L2-regularization and Elastic Net for solving convex optimization problems.

Usage example: 

  // Quadratic function f(x) = (x-1)^2 + 10
  // f obtains its minimum value 10 at x = 1
  Function f = new Function() {

    @Override
    public int getDimension() {
      return 1;
    }

    @Override
    public double valueAt(double[] x) {
      return StrictMath.pow(x[0]-1, 2) + 10;
    }

    @Override
    public double[] gradientAt(double[] x) {
      return new double[] { 2*(x[0]-1) };
    }

  };

  QNMinimizer minimizer = new QNMinimizer();
  double[] x = minimizer.minimize(f);
  double min = f.valueAt(x);

  • Felddetails

  • Konstruktordetails

    • QNMinimizer

      public QNMinimizer()
      Initializes a QNMinimizer with default parameters.

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost)
      Initializes a QNMinimizer.
      Parameter:
      l1Cost - The L1-regularization cost.
      l2Cost - The L2-regularization cost.

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost, int iterations)
      Initializes a QNMinimizer.
      Parameter:
      l1Cost - The L1-regularization cost.
      l2Cost - The L2-regularization cost.
      iterations - The maximum number of iterations.

    • QNMinimizer

      public QNMinimizer(double l1Cost, double l2Cost, int iterations, int m, int maxFctEval)
      Initializes a QNMinimizer.
      Parameter:
      l1Cost - The L1-regularization cost.
      l2Cost - The L2-regularization cost.
      iterations - The maximum number of iterations.
      m - The number of Hessian updates to store.
      maxFctEval - The maximum number of function evaluations.

  • Methodendetails

    • getEvaluator

      public QNMinimizer.Evaluator getEvaluator()

    • setEvaluator

      public void setEvaluator(QNMinimizer.Evaluator evaluator)

    • minimize

      public double[] minimize(Function function)
      Finds the parameters that minimize the objective function.
      Parameter:
      function - The objective Function.
      Gibt zurück:
      The minimizing parameters.