Adaptive non maximal suppression for keypoints distribution Java?

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Hi, I'm sorry to ask but I'm new to OpenCV for Android, and I've tried to change this code on the last answer to Java. The questions are:

1. I don't understand sort function. Is that from library? If it's not how should I make it?
2. Is the numeric_limits I set myself? If yes, what number should I set it to?
3. Core.norm( keypoints.get(i).pt - keypoints.get(j).pt ) Java can't substract Point with Point. I've tried Point temp = new Point(keypoints.get(i).pt.x - keypoints.get(j).pt.x, keypoints.get(i).pt.y - keypoints.get(j).pt.y) but the Core.norm need Mat parameter.
4. swap function on the bottom?

The code that I change

public void adaptiveNonMaximalSuppresion( List<KeyPoint> keypoints,
                                   const int numToKeep )
{
    if( keypoints.size() < numToKeep ) { return; }

    //
    // Sort by response
    //

    //sort( keypoints.begin(), keypoints.end(), 
    //[&]( const cv::KeyPoint& lhs, const cv::KeyPoint& rhs )
    //{
    //    return lhs.response > rhs.response;
    //} );

    List<KeyPoint> anmsPts = new ArrayList<KeyPoint>();

    List<Double> radii = new ArrayList<Double>();
    //radii.resize( keypoints.size() );
    List<Double> radiiSorted = new ArrayList<Double>();
    //radiiSorted.resize( keypoints.size() );

    Float robustCoeff = 1.11f; // see paper

    for( int i = 0; i < keypoints.size(); ++i )
    {
        Float response = keypoints.get(i).response * robustCoeff;
        Double radius = numeric_limits<Double>::max();
        for( int j = 0; j < i && keypoints[j].response > response; ++j )
        {
            radius = Math.min( radius, Core.norm( keypoints.get(i).pt - keypoints.get(j).pt ) );
        }
        radii.set(i,radius);
        radiiSorted.set(i,radius);
    }

    //std::sort( radiiSorted.begin(), radiiSorted.end(),
    //[&]( const double& lhs, const double& rhs )
    //{
    //    return lhs > rhs;
    //} );

    Double decisionRadius = radiiSorted.get(numToKeep);
    for( int i = 0; i < radii.size(); ++i )
    {
        if( radii.get(i) >= decisionRadius )
        {
            anmsPts.add( keypoints.get(i) );
        }
    }

    anmsPts.swap( keypoints );
}