dkohlsdorf

                                     ,~~+++===~:,,,,
                                 ,,:=+==~~~=~========::,
                               ,:++?=~::~~~~~~~~~~=~====:,
                           ,::=?+=~~~~~~~~::::::::~~~~~~~~==~:
                       ,,:=?+?=~~~~~:~~~~~~::::::::::::::~====~:,
                      :++???=~~~::::~:~~~::,,,,,,,,,:::,::::~~=++~,
                     ,~+++===~~:::::::::::,,,,,,,,,,,,,,,,:::~~==?=,
                  , ,+?+==~~::::::::::,,,,,,,..........,,,,,,~~~==?=,
                  =~?+???=~::::::,,,,,,,,,,,,..............,,~~=+??+=:
                 +++=+====~:::::,,,,,,,,,,,,,...............,,,,~++===+:

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class Alignment[A](
  init: (Int, Int) => Double,
  rank: (Double, Double, Double, A, A) => Double
) {

  def score(x: Array[A], y: Array[A]): Double = {  
    val n  = x.size
    val m  = y.size
    val dp = Array.ofDim[Double](n + 1, m + 1)

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public class UnionFind {

    private int id[];

    private int count;

    public UnionFind(int N) {
        count = N;
        id = new int[N];
        for(int i = 0; i < N; i++) {

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import numpy as np
import scipy.stats as stats

def generate_split(n_char, mu = 0.0, std = 1.0):    
    return stats.norm.ppf(np.arange(1, n_char) / n_char, mu, std)

def convert(x, splits, cardinality):
    n = len(x)
    chars = np.zeros(n, dtype=np.int)
    for i in range(0, n):

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package processing.signals;

import java.util.ArrayList;

/**
 * Extract all local features from current spectrogram
 * 
 * @author Daniel Kohlsdorf
 */
public class LocalFeatureExtractor {	

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public class BitSet {

	private int set = 0;
	
	public void add(int position) {
		int mask = 1 << position;
		set = set | mask;
	}

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import java.util.Arrays;

/**
 * Implements a size limited heap
 * 
 * @author Daniel Kohlsdorf
 */
public class Heap {

	private double heap[];

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import java.util.Arrays;

/**
 * Merge as in MergeSort
 * 
 * @author Daniel Kohlsdorf
 */
public class Merge {

	public static int[] merge(int x[], int y[]) {

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import java.util.Random;


public class KMeans {

	public static double distance(double x[], double y[]) {
		int D = x.length;
		double distance = 0;
		for(int i = 0; i < D; i++) {
			distance += Math.pow(x[i] - y[i], 2);