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        Word2VecModel model_stemmed = ModelUtil.loadWord2VecModel("/Users/mueller/Coding/Word2Vectors/webbase10p/model_word2vec_stemmed.ser");
        Word2VecModel model_unstemmed = ModelUtil.loadWord2VecModel("/Users/mueller/Coding/Word2Vectors/webbase10p/model_word2vec.ser");
        System.out.println("Stemmed example");
        System.out.println("#############################################");

        String term1= "scholar";
        String term2 ="student";

        //To Stem terms the Porter Stemmer from Apache Lucene is used
        double result = Word2VecSim.cousineSimilarityBetweenTerms(model_stemmed,ModelUtil.porter_stem(term1),ModelUtil.porter_stem(term2));

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# Perform Dataset preparation and the comparison
import numpy as np
import random
import time
from collections import defaultdict
from sklearn.neighbors import BallTree
from sklearn.neighbors import DistanceMetric
n_samples_min = int(1e3)
n_samples_max = int(1e7)

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RADIANT_TO_KM_CONSTANT = 6367
import proximitypyhash as ppyh
import pygeohash as pgh
from sklearn.neighbors import DistanceMetric
import numpy as np
from collections import defaultdict


class GeoHashIndexer:
    def __init__(self, precision, lat_longs):

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from sklearn.neighbors import BallTree
import numpy as np
RADIANT_TO_KM_CONSTANT = 6367
class BallTreeIndex:
    def __init__(self,lat_longs):
        self.lat_longs = np.radians(lat_longs)
        self.ball_tree_index =BallTree(self.lat_longs, metric='haversine')
        
    def query_radius(self,query,radius):
        radius_km = radius/1e3

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from sklearn.neighbors import DistanceMetric
import numpy as np
RADIANT_TO_KM_CONSTANT = 6367
class BruteForce:
    def __init__(self,lat_longs):
        self.haversine =DistanceMetric.get_metric('haversine')
        self.lat_longs = np.radians(lat_longs)
        
    def query_radius(self,query,radius):
        radius_km = radius/1e3

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import numpy as np
import random
n_samples_min = int(1e3)
n_samples_max = int(5e7)
lat_longs = np.array([[random.uniform(50, 52),
                       random.uniform(8, 15) ] 
                      for i in range(n_samples_max)])

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## Install scala 2.11.8

export scalaVer="2.11.8"
sudo apt-get remove scala-library scala
wget www.scala-lang.org/files/archive/scala-"$scalaVer".deb
sudo dpkg -i scala-"$scalaVer".deb
sudo apt-get -y --force-yes update
sudo apt-get -y --force-yes install scala

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ssh-keygen -t rsa -b 4096 -C "your_email@example.com"