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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
from sklearn.metrics import mean_squared_error

# Assuming df_list[100]['y_lag'] is your time series data
# Ensure it's a pandas Series for compatibility with SimpleExpSmoothing
data = pd.Series(df_list[100]['y_lag'])

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import requests
import pandas as pd



number_of_pages = 100
#number_of_ads = number_of_pages * per_page

job_title = ["'Data Analyst' and 'data scientist'"]
for job in job_title:

fuwiak

class Vector2D:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __str__(self):
        return f"Vector2D({self.x}, {self.y})"

    def __add__(self, other):
        if isinstance(other, Vector2D):

fuwiak

import os
import sys

# Pobierz aktualną ścieżkę do bieżącego pliku
current_path = os.path.dirname(os.path.abspath(__file__))

# Dodaj dwa poziomy wyżej do ścieżki
two_levels_up = os.path.abspath(os.path.join(current_path, "../../"))

# Dodaj nową ścieżkę do sys.path, aby Python mógł znaleźć moduł

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def compare_algorithms2df(MLA, X_train, X_test, y_train, y_test, sorted_by_measure='accuracy'):
  #show grid with compared results - accuracy, recall, ppv, f1-measure, mcc
  
  MLA_columns = []
  MLA_compare = pd.DataFrame(columns = MLA_columns)


  row_index = 0
  for alg in MLA:

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import pyspark
#import udf
from pyspark.sql.functions import udf
from pyspark.sql.types import BooleanType
from shapely.geometry import Point, Polygon


# Create a SparkContext
sc = pyspark.SparkContext()

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class IP:
    def __init__(self, ip_address, filename):
        self.ip_address = ip_address
        self.filename = filename


    def random_ip_nonlocal(self):
        lista_ip_non_local = []
        for i in range(self.ip_address):
            ip = ""

fuwiak

p.generate_ips()
(['127.247.9.229'], ['124.72.36.132'])

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function p = predict(theta, X)
%PREDICT Predict whether the label is 0 or 1 using learned logistic
%regression parameters theta
%   p = PREDICT(theta, X) computes the predictions for X using a
%   threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)

m = size(X, 1); % Number of training examples

% You need to return the following variables correctly
p = zeros(m, 1);

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function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
%   parameter for logistic regression and the gradient of the cost
%   w.r.t. to the parameters.

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly