allen1881996

public class QuickSelect {

    public int quickSelect(int[] arr, int k) {
        return quickSelect(arr, k, 0, arr.length - 1);
    }

    private int quickSelect(int[] arr, int k, int start, int end){
        if (k > arr.length) throw new IllegalArgumentException();
        if (arr.length == 0 || arr == null) return Integer.MIN_VALUE;
        var pivotIndex = randomlySelect(arr);

allen1881996

package com.allenhuang;

import java.util.ArrayList;
import java.util.Arrays;

public class DeterministicSelect {
    // store the median of each group
    private ArrayList<Integer> medians = new ArrayList<>();

    public int select(int[] arr, int k) {

allen1881996

package com.allenhuang;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class Graph {
    private Map<String, Node> nodes = new HashMap<>();
    private Map<Node, List<Node>> adjacencyList = new HashMap<>();

allen1881996

package com.allenhuang;

import java.util.*;

public class Graph2 {
    private Map<Node,Integer> map = new HashMap();
    private List<LinkedList<Node>> adjacencyList = new ArrayList();
    private int index = 0;

    private class Node {

allen1881996

package com.allenhuang;

import java.util.*;

public class Graph {
    private Map<String, Node> nodes = new HashMap<>();
    private Map<Node, List<Node>> adjacencyList = new HashMap<>();

    private class Node {
        private String label;

allen1881996

package com.allenhuang;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class WeightedGraph {
    private Map<String, Node> nodes = new HashMap<>();
    private Map<Node, List<Edge>> adjacencyList = new HashMap<>();

allen1881996

text = []
like = []
date = []
reply_to = []
text = []
name = [] 

def is_break_point(astring:str):
    if 'React' in astring:
        return True

allen1881996

def main():
  df['polarity'] = df['text_prep'].parallel_apply(lambda x: round(TextBlob(x).polarity,3))
  df['subjectivity'] = df['text_prep'].parallel_apply(lambda x: round(TextBlob(x).subjectivity,3))
  df = subjectivity_labeling(df)
  df = polarity_labeling(df)

# labeling subjectivity
def subjectivity_labeling(df,threshold=0.5):
    if threshold == 0.5:
        df.loc[df.subjectivity > 0.5,"subjectivity_label"] = 'subjective'