nivdul

I am attesting that this GitHub handle nivdul is linked to the Tezos account tz1VbDP9Mytydszm7o1EGx9v1mPyL68BeNiW for tzprofiles

sig:edsigtuSptFpj31FdpCxtt9Rii9PdGcrVu3CTTh3JCnFbubXRpN2qiaoEBGeaQnfNgDUAAYJ1ZS3wNPdt9zSatC1mM4yHUrEU66

nivdul

public class BubbleSortSwap {

    /**
     * Swap two elements indexed by a and b in an array
     */
    static void swap(int[] arr, int a, int b) {

        int temp = arr[a];
        arr[a] = arr[b];
        arr[b] = temp;

nivdul

public class BubbleSortExample {

    static int[] bubbleSort(int[] arr) {

        int n = arr.length;
        int temp = 0;

        for(int i=0; i < n; i++){

            for(int j=1; j < (n-i); j++){

nivdul

{"id":"572692378957430785",
 "user":"Srkian_nishu :)",
 "text":"@always_nidhi @YouTube no i dnt understand bt i loved the music nd their dance awesome all the song of this mve is rocking",
 "place":"Orissa", 
 "country":"India"}

nivdul

// create the Spark configuration and context
SparkConf conf = new SparkConf().setAppName("Wordcount").setMaster("local[*]");

JavaSparkContext sc = new JavaSparkContext(conf);

// load data and create an RDD of string
JavaRDD<String> tweets = sc.textFile("path_To_File")

JavaPairRDD<String, Integer> wordcount = tweets.flatMap(line -> Arrays.asList(line.toString().split(" ")))
                                               // mapper step

nivdul

Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
int numClasses = 4;
String impurity = "gini";
int maxDepth = 9;
int maxBins = 32;

// create model
final DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, impurity, maxDepth, maxBins);

// Evaluate model on training instances and compute training error

nivdul

// Split data into 2 sets : training (60%) and test (40%).
JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.6, 0.4});
JavaRDD<LabeledPoint> trainingData = splits[0].cache();
JavaRDD<LabeledPoint> testData = splits[1];

nivdul

public Double computeAvgTimeBetweenPeak(JavaRDD<long[]> data) {
    // define the maximum using the max function from MLlib
    double[] max = this.summary.max().toArray();

    // keep the timestamp of data point for which the value is greater than 0.9 * max
    // and sort it !
    JavaRDD<Long> filtered_y = data.filter(record -> record[1] > 0.9 * max[1])
                                   .map(record -> record[0])
                                   .sortBy(time -> time, true, 1);

nivdul

/**
 * @return Double resultant = 1/n * ∑ √(x² + y² + z²)
 */
public static double computeResultantAcc(JavaRDD<double[]> data) {
  // first let's compute the square of each value and the sum
  // compute then the root square: √(x² + y² + z²)
  // to finish apply a mean function: 1/n * sum [√(x² + y² + z²)]
  JavaRDD<Vector> squared = data.map(record -> Math.pow(record[0], 2)
                                             + Math.pow(record[1], 2)
                                             + Math.pow(record[2], 2))

nivdul

/**
 * @return Vector [ (1 / n ) * ∑ |b - mean_b|, for b in {x,y,z} ]
 */
public static double[] computeAvgAbsDifference(JavaRDD<double[]> data, double[] mean) {

  // then for each point x compute x - mean
  // then apply an absolute value: |x - mean|
  JavaRDD<Vector> abs = data.map(record -> new double[]{Math.abs(record[0] - mean[0]),
                                                        Math.abs(record[1] - mean[1]),
                                                        Math.abs(record[2] - mean[2])})