My toolbox for dynamic programming
#to be documented #Chapter: tf-idf
from anarcute import *
import requests, json
sentence=”Eat more of those french fries and drink cola”
alice=requests.get(”https://gist.githubusercontent.com/phillipj/4944029/raw/75ba2243dd5ec2875f629bf5d79f6c1e4b5a8b46/alice_in_wonderland.txt”).text
print(tf_idf(sentence,alice))
>> {‘eat’: 168.7962962962963, ‘more’: 62.006802721088434, ‘of’: 5.9111543450064845, ‘those’: 303.8333333333333, ‘french’: 759.5833333333333, ‘and’: 3.4843272171253816, ‘drink’: 434.047619047619}
#If text is too big it’s frequencies can be pre-cached.
filename=”alice.json”
vector=vectorize(alice)
open(filename,”w+”).write(json.dumps(vector))
vector=json.load(open(filename,”r+”))
print(tf_idf(sentence,vector))
>>{‘eat’: 168.7962962962902, ‘more’: 62.00680272108618, ‘of’: 5.91115434500627, ‘those’: 303.8333333333223, ‘french’: 759.5833333333056, ‘and’: 3.484327217125255, ‘drink’: 434.0476190476033}
#we can sort by value
print(sort_by_value(tf_idf(sentence,vector)))
>>{‘french’: 759.5833333332979, ‘drink’: 434.04761904759886, ‘those’: 303.8333333333192, ‘eat’: 168.7962962962885, ‘more’: 62.006802721085556, ‘of’: 5.911154345006209, ‘and’: 3.4843272171252204}
#Chapter: Google
#We have Google Translate and Google Custom Search Engine now
key=”MY_GOOGLE_KEY”
gt=GT(key)
gt.translate(“pl”,”en”,”Jeszcze Polska nie zginęła, Kiedy my żyjemy. Co nam obca przemoc wzięła, Szablą odbierzemy.”)
>> {‘data’: {‘translations’: [{‘translatedText’: ‘Poland is not dead yet, When we live. What foreign violence has taken from us, we will take away the Saber.’}]}}
cx=”MY_CUSTOM_SEARCH_ENGINE_KEY”
gs=GS(cx,key)
gs.search(“krakauer sausage recipe”)
>> dict with search result, up to 10 items
gs.items(“krakauer sausage recipe””)
>> array of results, up to 100 items
#Chapter: Multithreading
#based on multithreading_on_dill library
#let’s reverse every string of Alice in Wonderland
alice=requests.get(url).text
alice_reversed=mapp(lambda s: str(s[::-1]),alice.split(’n’))
#as you see we have no problem with lambda
#by default the number of processes equals to cpu number, but you can make it bigger for highly async tasks or smaller to prevent overload
alice_reversed=mapp(lambda s: str(s[::-1]),alice.split(’n’),processes=2)
#decorator @timeit also included in the library
@timeit
def test(p=None):
r=mapp(lambda s: math.factorial(150*len(s)),alice.split(’n’),processes=p)
return None
test()
>> ‘test’ 2563.11 ms
test(1)
>> ‘test’ 5287.27 ms
#multithreading filter
alice_special=filterp(lambda s: “alice” in s.lower(),alice.split(’n’))
#run one async function
run(print,[“A B C”])
#you can wait for it’s result when you need to catch up
p=run(lambda x: request.get(x).text,url)
some_other_stuff()
p.join()
#apply - function that executes functions. Used to run few different functions in one multithreading process
r=mapp(apply,[lambda:requests.get(“https://gist.githubusercontent.com/phillipj/4944029/raw/75ba2243dd5ec2875f629bf5d79f6c1e4b5a8b46/alice_in_wonderland.txt”).text,lambda: math.factorial(9000)])
#Chapter predicates
#in_or(a,b) - returns if at least one element of array is in array/string b
a=[“Some”,”important”,”array”]
b=[“Another”,”array”]
in_or(a,b)
>> True
c=[“Something”, “Else”]
in_or(a,c)
>> False
d=”Some string”
in_or(a,d)
>> True
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