toniesteves

import numpy as np 
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np

from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler

toniesteves

items = ['Andrew Ng', 'François Chollet', 'Josh Stamer', 
         'Kent Beck', 'Erick Evans', 'Soumith Chintala'];

def find_all_pairs(items):
  for item_a in items:
    for item_b in items:
      print(item_a + ' - ' + item_b)

find_all_pairs(items)

toniesteves

from itertools import chain, combinations

authors = ['Andrew Ng', 'François Chollet', 'Josh Stamer', 'Kent Beck', 'Erick Evans', 'Soumith Chintala'];

def subsets(iterable):
  s = list(iterable)
  
  return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

print(list(subsets(authors)))

toniesteves

items = [1, 2, 3, 4, 5, 6 , 7, 8, 9, 10]

def binary_search(alist, item):
  first = 0
  last = len(alist)-1
  found = False

  while first <= last and not found:

    midpoint = (first + last)//2

toniesteves

import time

lot_of_items = ['coffee'] * 100000

def find_coffee(items):
  start = time.time()

  for item in items:
    if(item == 'coffe'):
      print('Found Coffee')

toniesteves

authors = ['Andrew Ng', 'Josh Stamer', 'Kent Beck', 
	   'Martin Fowler', 'Erick Evans', 'Erich Gamma'];

def first_two_authors(authors):
  print(authors[0]) # O(1)
  print(authors[1]) # O(1)

first_two_authors(authors) # O(2)

toniesteves

import time

items = ['coffee'] * 10;
lot_of_items = ['coffee'] * 100000

def find_coffee(items):
  start = time.time()

  for item in items:
    if(item == 'coffe'):

toniesteves

def double_and_copy_list_values(items):
  new_list = []
	
  for item in items:
    new_list.append(item * 2)

  return new_list

toniesteves

def double_list_values(my_list, idx):
  my_list[idx] = my_list[idx] * 2

toniesteves

import numpy as np
import pandas as pd
from collections import defaultdict
from scipy.stats import hmean
from scipy.spatial.distance import cdist
from scipy import stats
import numbers


def weighted_hamming(data):