public void init(int nplayers, int[] pref) {
numPlayers = nplayers;
prefs = Vectors.castToFloatArray(pref);
prefsInt = pref;
platter = new float[pref.length];
bowlsRemaining = (float) (nplayers - getIndex());
totalNumBowls = bowlsRemaining;
scoreStats = new Stats();
System.out.println(getIndex());
}
public boolean pass(int[] bowl, int bowlId, int round, boolean canPick, boolean mustTake) {
float[] currentBowl = Vectors.castToFloatArray(bowl);
numFruits = Vectors.sum(currentBowl);
if (!canPick) {
log("CANT PICK RETURNING EARLY");
return false;
}
log("|||||||||||||||||||||||||||||||||||||||||");
log("Number of bowls that will pass: "******"Number of bowls remaining: " + bowlsRemaining);
// Initialize the histogram now that we know how many fruit come in a bowl
if (mle == null) {
mle = new MLE((int) numFruits, numPlayers, prefs);
}
mle.addObservation(currentBowl);
// calculate score for the bowl the we get
float score = score(currentBowl);
scoreStats.addData(score);
// get MLE and score it
float[] uniformBowl = new float[currentBowl.length];
for (int i = 0; i < bowl.length; i++) {
uniformBowl[i] = numFruits / bowl.length;
}
float uniformScore = score(uniformBowl);
log("Uniform Score: " + uniformScore);
log("MLE Score: " + score(mle.bowl(round == 0)));
log("Score: " + score);
float[] mleBowl = mle.bowl(round == 0);
Stats bowlGenerationStats = mle.getBowlGenerationStats();
float[] mlePlatter = mle.platter();
float maxScore = maxScore(mlePlatter);
boolean take =
shouldTakeBasedOnScore(
score, score(mleBowl), maxScore, bowlGenerationStats.standardDeviation());
bowlsRemaining--;
return take;
}
private float score(float[] bowl) {
return Vectors.dot(bowl, prefs);
}
public static void main(String[] args) {
// Path de resultados
String pathResults = "results";
String pathToCategories = "values.txt";
String pathToWords = "words.txt";
File file = new File(pathToWords);
HashMap<Double, String> categoriesDict = new HashMap<>();
HashMap<String, String> resultado = new HashMap<>();
FileInputStream fis = null;
try {
fis = new FileInputStream(pathToCategories);
// Construct BufferedReader from InputStreamReader
BufferedReader br = new BufferedReader(new InputStreamReader(fis));
String line = null;
while ((line = br.readLine()) != null) {
String[] words = line.split(" ");
categoriesDict.put(Double.valueOf(words[0]), words[1]);
}
br.close();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
// Path donde estaran las categorias
String pathCategories = "src/main/resources/categoriestest/";
// Configuracion basica de la aplicacion
SparkConf sparkConf = new SparkConf().setAppName("NaiveBayesTest").setMaster("local[*]");
// Creacion del contexto
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
NaiveBayesModel model = NaiveBayesModel.load(jsc.sc(), pathResults);
HashMap<String, String> dictionary = loadDictionary();
JavaRDD<String> fileWords = null;
if (file.exists()) {
JavaRDD<String> input = jsc.textFile(pathToWords);
fileWords =
input.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
});
} else {
System.out.println("Error, there is no words");
System.exit(-1);
}
ArrayList<String> aFileWords = (ArrayList<String>) fileWords.collect();
// Cogemos el fichero en el que se encuentran las categorias
File dir = new File(pathCategories);
for (File f : dir.listFiles()) {
JavaRDD<String> input = jsc.textFile(f.getPath());
JavaRDD<String> words =
input.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
});
JavaPairRDD<String, Double> wordCount = Reducer.parseWords(words, dictionary);
List<Tuple2<String, Double>> total = wordCount.collect();
List<Tuple2<String, Double>> elementsRemoved = new ArrayList<>();
for (Tuple2<String, Double> t : total) {
if (!t._1.equals("")) {
elementsRemoved.add(new Tuple2<>(t._1, t._2 / wordCount.count()));
}
}
ArrayList<Tuple2<String, Double>> freqFinal = new ArrayList<>();
for (String s : aFileWords) {
boolean found = false;
for (Tuple2<String, Double> t : elementsRemoved) {
if (t._1.equals(s)) {
found = true;
freqFinal.add(t);
break;
}
}
if (!found) {
freqFinal.add(new Tuple2<String, Double>(s, 0.0));
}
}
double[] v = new double[freqFinal.size()];
for (int i = 0; i < freqFinal.size(); i++) {
Tuple2<String, Double> t = freqFinal.get(i);
v[i] = t._2;
}
org.apache.spark.mllib.linalg.Vector vector = Vectors.dense(v);
/**/
double d = model.predict(vector);
System.out.println(categoriesDict.get(d));
resultado.put(f.getName(), categoriesDict.get(d));
}
jsc.stop();
try {
Thread.sleep(2000);
} catch (InterruptedException e) {
e.printStackTrace();
}
for (String key : resultado.keySet()) {
System.out.println(key + " - " + resultado.get(key));
}
}