danielbarkhorn

# Tasked at work to clean user typed company name data (over 5k 'unique' companies), I needed to programmaticaly combine
# entries like 'E Trade', 'E*Trade', and 'E*Trade Financial'. Initial thought was to use Levenshtein ratios and other computer
# sciency heuristics.
# On second thought, the cleaned data was needed quickly, so I found an API that would autocomplete company names and leveraged it.

import requests
import csv
import Levenshtein
from tqdm import tqdm
import pickle

danielbarkhorn

import numpy as np
import consts

RHO_MATRICES = {tuple(np.linalg.matrix_power(np.array([[0,1],[2,3]]), t).dot(np.array([1,0])) % 5): t for t in range(0,24)}
RHO_MATRICES[(0,0)] = -1

def arrTransform1(arr):
    output = [[[0 for _ in range(64)] for _ in range(5)] for _ in range(5)]
    for i in range(5):
        for j in range(5):

danielbarkhorn

class Trie:
    """
    Trie data structure implemented using dictionary
    
    Parameters
    ----------
    dictioanry : array
    An array of all the strings to be included in the trie
    
    Attributes