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SimilarityRanking.java
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SimilarityRanking.java
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import java.util.*;
/**
* This class is used to compare players to a given hall of fame player model through cosine
* similarity. It can also return an array list of players sorted by decreasing cosine similarity.
*/
public class SimilarityRanking {
int numInRanking;
/**
* Constructor that instantiates the class with the number of players the user wants to rank
* @param numInRanking the length of the returned list of players
*/
public SimilarityRanking(int numInRanking) {
this.numInRanking = numInRanking;
}
/**
* Given two PlayerModels, one of the average hall of fame player during their college years
* and the other of a college player we want to compare to the hall of fame player model,
* create 2 vectors and return the cosine similarity of the 2 vectors.
* @param hofP the PlayerModel for the average hall of fame player during their college years
* @param colP the PlayerModel for the player we want to compare to hofP
* @return the cosine similarity between the vectors created from hofP and colP
*/
public double cosSimilarity(PlayerModel hofP, PlayerModel colP) {
double[] hallOfFameVector = {hofP.getAssists(), hofP.getFieldGoalPercent(),
hofP.getFreeThrowPercent(), hofP.getPoints(), hofP.getRebounds()};
double[] collegeVector = {colP.getAssists(), colP.getFieldGoalPercent(),
colP.getFreeThrowPercent(), colP.getPoints(), colP.getRebounds()};
double dotProd = 0.0;
double hofVectorMag = 0.0;
double colVectorMag = 0.0;
for (int i = 0; i < 5; i++) {
dotProd += (hallOfFameVector[i] * collegeVector[i]);
hofVectorMag += (hallOfFameVector[i] * hallOfFameVector[i]);
colVectorMag += (collegeVector[i] * collegeVector[i]);
}
if (colVectorMag == 0 || hofVectorMag == 0) {
return 0;
}
hofVectorMag = Math.sqrt(hofVectorMag);
colVectorMag = Math.sqrt(colVectorMag);
return dotProd / (hofVectorMag * colVectorMag);
}
/**
* Given an ArrayList of PlayerModels and the model for the average hall of fame
* player during their college years rankedPlayers will determine the cosine similarity
* between all PlayerModels in allPlayers and hofP, organizing all this data in an array
* list of tuples that contain both a players name and the calculated cosine similarity.
* Then, the method will sort this array list by decreasing cosine similarity, then
* return the names of the top players in an array list.
* @param hofP the PlayerModel for the average hall of fame player during their college years
* @param allPlayers the ArrayList containing all players we want to rank based on cosine
* similarity to hofP
* @return an ArrayList containing the names of the most highly cosine similar players
*/
public ArrayList<String> rankedPlayers(ArrayList<PlayerModel> allPlayers, PlayerModel hofP) {
ArrayList<Entry<String, Double>> allCosSim = new ArrayList<>();
for (PlayerModel player : allPlayers) {
allCosSim.add(new Entry<>(player.getName(), cosSimilarity(hofP, player)));
}
allCosSim.sort(new Comparator<Entry<String, Double>>() {
@Override
public int compare(Entry<String, Double> o1, Entry<String, Double> o2) {
if ((o1.value - o2.value) > 0) {
return -1;
} else if ((o1.value - o2.value) < 0) {
return 1;
}
return 0;
}
});
ArrayList<String> rankedNames = new ArrayList<>();
for (int i = 0; i < Math.min(numInRanking, allCosSim.size()); i++) {
rankedNames.add(allCosSim.get(i).key);
}
return rankedNames;
}
/**
* This subclass serves the purpose of creating a map entry that allows for
* the storing of a value (in our case cosine similarity value) keyed by player name
*/
class Entry<K, V> {
K key;
V value;
public Entry(K key, V value) {
this.key = key;
this.value = value;
}
}
}