mesejo

{
  "learner": {
    "attributes": {
      "scikit_learn": "{\"_estimator_type\": \"classifier\"}"
    },
    "feature_names": [],
    "feature_types": [],
    "gradient_booster": {
      "model": {
        "gbtree_model_param": {

mesejo

from collections import defaultdict
from itertools import groupby, cycle
from operator import itemgetter as ig

import random
import perfplot
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

mesejo

import perfplot
import re
import random
from string import ascii_lowercase

pattern = r"(?=(n(?:e(?:c(?:essary|k)|ed|xt|ighborly)|o(?:tebook|ndescript|rmal|is[ey])|a(?:ppy|me|useating|t(?:ion|ural))|i(?:fty|ne|ce|ppy)|u(?:mber(?:less)?|t))|k(?:ey|i(?:ck|nd|ll)|no(?:ck|t))|z(?:oo|any|i(?:nc|p)|ephyr)|v(?:o(?:ice|latile)|e(?:in|rsed?)|ulgar|a(?:nish|cuous|se)|iolet)|y(?:oung|a(?:rd|k)|ummy)|g(?:a(?:ze|rrulous)|e(?:ese|ntle)|l(?:ass|eaming|ue)|iant|o(?:od|ld|vernment)|u(?:itar|llible|ar(?:antee|ded)|sty)|host|r(?:ieving|ubby|e(?:en|a(?:sy|t))|o(?:u(?:chy|nd)|an|ovy)|a(?:ndfather|pe|ceful|t(?:is|e)|b)))|j(?:u(?:m(?:bled|p)|icy)|e(?:wel|lly)|a(?:il|zzy)|o(?:yous|lly|g))|u(?:mbrella|gliest|tter|sed?|n(?:becoming|ruly|fasten|kempt|used|wieldy|derstood|pack|a(?:ccountable|ble)|i(?:que|nterested|t)|equaled|s(?:ightly|uitable)|cle)|p)|i(?:tchy|cy|gnore|m(?:mense|p(?:erfect|o(?:rtant|lite|ssible)))|ll(?:\-informed|ustrious)|n(?:ject|v(?:incible|ention)|c(?:re(?:ase|dible)|ompetent)|nate|t(?:roduce|e(?:lligent|rest(?:ing

mesejo

from operator import eq
from functools import partial
import perfplot
import random


def nested_list_comprehension(names, needle="Matt"):
    return [[name for name in lst if needle == name] for lst in names]

mesejo

from collections import defaultdict
from random import choices

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

sns.set_theme(style="whitegrid")

mesejo

import random
import perfplot
from collections import defaultdict, Counter
from itertools import chain
from pyroaring import BitMap


def setup(k):
    lss = [f"list{i}" for i in range(1, k + 1)]
    labels = [f"label{i}" for i in range(1, 101)]

mesejo

import random
import numpy as np

from numpy.random import default_rng
from itertools import islice


def reservoir_sampling(pop, k):
    reservoir = []
    stream = iter(pop)

mesejo

question
for the last years of his life galileo was under house arrest for espousing this mans theory
no 1912 olympian football star at carlisle indian school mlb seasons with the reds giants braves
the city of yuma in this state has record average of 4055 hours of sunshine each year
in 1963 live on the art linkletter show this company served its billionth burger
signer of the dec of indep framer of the constitution of mass second president of the united states
in the title of an aesop fable this insect shared billing with grasshopper
built in 312 bc to link rome the south of italy its still in use today
no 30 steals for the birmingham barons 2306 steals for the bulls
in the winter of 1971 72 record 1122 inches of snow fell at rainier paradise ranger station in this state

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import pandas as pd
import numpy as np

arr = pd.read_csv("test.txt", delim_whitespace=True).values
print(arr)


class Cube:

    def __init__(self, row_index, col_index, data):

mesejo

import pandas as pd
from collections import defaultdict

df = pd.DataFrame(
    {
        'unit_0': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'],
        'unit_1': ['B', 'C', 'C', 'C', 'D', 'D', 'E', 'E'],
        'unit_2': ['F', 'G', 'G', 'H', 'I', 'I', 'J', 'I']
    }
)