jdmoore7

class LogReg():
    """
    Implementation of binary logistic regression
    """
    
    def __init__(self,dataframe,y_column):
        """
        Initialize LogReg class with a pandas dataframe and the name of the dependent variable column, y.
        All other variable will be assumed to be independent variables, x.
        """

jdmoore7

class Node(object):
    def __init__(self,val):
        self.val = val
        self.next = None

    def get_data(self):
        return self.val

    def set_data(self,val):
        self.val = val

jdmoore7

class GeneralTree():
    """
    Parameters:
    @root: root node
    @first_born: leftmost child of root node
    @current_node: initalized as first born
    @current-value: initialized as value of first born (current node)
    @visited: Linked list of all values whichg have previously been visited
    @path: FULL path, traversing siblings, from root to search value (LL)
    @child_path: path, sibling traversal not captured, more closely resembles typical tree design/behavior (LL)

jdmoore7

jdmoore7

jdmoore7

class TweetGrabber():
    
    def __init__(self,myApi,sApi,at,sAt):
        import tweepy
        self.tweepy = tweepy
        auth = tweepy.OAuthHandler(myApi, sApi)
        auth.set_access_token(at, sAt)
        self.api = tweepy.API(auth)
        
        

jdmoore7

class RetweetParser():
    
    def __init__(self,data,user):
        import ast
        self.user = user

        edge_list = []
    
        for idx,row in data.iterrows():
            if len(row[4]) > 5:    

jdmoore7

class TweetGraph():
    
    def __init__(self,edge_list):
        import igraph
        import pandas as pd
        data = pd.read_csv(edge_list).to_records(index=False)
        self.graph = igraph.Graph.TupleList(data, weights=True, directed=False)
        
    def e_centrality(self):
        import operator

jdmoore7

import random

def trial():
  sequence = []
  # dist =  [0.140,0.140,0.140,0.125,0.105,0.090,0.075,0.060,0.045,0.030,0.020,0.015,0.010,0.005]
  dist = [114,113,112,111,99,89,79,69,59,49,39,29,19,9,6,4]
  pool = [idx+1 for idx,elem in enumerate(dist)]
  # for i in range(4):
  for i in range(5):
    selected = random.choices(population=pool, weights=dist)[0]

jdmoore7

# SQL
import sqlite3
conn = sqlite3.connect('acc1819.db')
c = conn.cursor()

# Get columns from game table
game_columns = c.execute("""
PRAGMA table_info(games);
""").fetchall()
g_col = [game_columns[i][1] for i in range(len(game_columns))]