toniesteves

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict, KFold
import xgboost as xgb

toniesteves

def plot(self,run_results,with_alarm = True, positions = None):
        """
        Plot the results of given by the run
        
        Parameters
        ----------
        run_results : dict
            results given by the 'run' method
        with_alarm : bool
		    (default = True) If True, alarms are plotted.

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):

toniesteves

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

toniesteves

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

  return new_list

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

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

lot_of_items = ['coffee'] * 100000

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

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

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

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)))