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NumericalGradientForward.process
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How to use
process
method
in
org.ddogleg.optimization.derivative.NumericalGradientForward

Best Java code snippets using org.ddogleg.optimization.derivative.NumericalGradientForward.process (Showing top 5 results out of 315)

  • Common ways to obtain NumericalGradientForward
private void myMethod () {
NumericalGradientForward n =
  • Codota IconFunctionNtoS function;new NumericalGradientForward(function)
  • Smart code suggestions by Codota
}
origin: org.ddogleg/ddogleg

numerical.process(param,expected);
origin: lessthanoptimal/ddogleg

numerical.process(param,expected);
origin: org.ddogleg/ddogleg

public static boolean gradient( FunctionNtoS func , FunctionNtoN gradient ,
                double param[] , double tol ,  double differenceScale )
{
  NumericalGradientForward numerical = new NumericalGradientForward(func,differenceScale);
  if( numerical.getN() != gradient.getN() )
    throw new RuntimeException("N is not equal: "+numerical.getN()
        +"  "+gradient.getN());
  int N = numerical.getN();
  double[] found = new double[N];
  double[] expected = new double[N];
  gradient.process(param, found);
  numerical.process(param,expected);
  for (int i = 0; i < N; i++) {
    if(Math.abs(found[i]-expected[i]) > tol)
      return false;
  }
  return true;
}
origin: lessthanoptimal/ddogleg

public static boolean gradient( FunctionNtoS func , FunctionNtoN gradient ,
                double param[] , double tol ,  double differenceScale )
{
  NumericalGradientForward numerical = new NumericalGradientForward(func,differenceScale);
  if( numerical.getN() != gradient.getN() )
    throw new RuntimeException("N is not equal: "+numerical.getN()
        +"  "+gradient.getN());
  int N = numerical.getN();
  double[] found = new double[N];
  double[] expected = new double[N];
  gradient.process(param, found);
  numerical.process(param,expected);
  for (int i = 0; i < N; i++) {
    if(Math.abs(found[i]-expected[i]) > tol)
      return false;
  }
  return true;
}
origin: lessthanoptimal/ddogleg

@Test
public void simple() {
  // give it a function where one variable does not effect the output
  // to make the test more interesting
  SimpleFunction f = new SimpleFunction();
  NumericalGradientForward alg = new NumericalGradientForward(f);
  
  double output[] = new double[]{1,1,1};
  alg.process(new double[]{2,3,7},output);
  
  assertEquals(3, output[0], 1e-5);
  assertEquals(-36, output[1], 1e-5);
  assertEquals(0, output[2], 1e-5);
}

org.ddogleg.optimization.derivativeNumericalGradientForwardprocess

Popular methods of NumericalGradientForward

  • <init>
  • getN

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