






"""simple docstring"""







from copy import deepcopy







class  lowerCamelCase__ :






    def __init__(			self     ,A = None     ,A = None ):

          if arr is None and size is not None:
                UpperCAmelCase          =	size
                UpperCAmelCase          =	[0] * size
          elif arr is not None:
                self.init(A )
          else:
                raise ValueError("""Either arr or size must be specified""" )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	len(A )
          UpperCAmelCase          =	deepcopy(A )
          for i in range(1     ,self.size ):
                UpperCAmelCase          =	self.next_(A )
                if j < self.size:
                      self.tree[j] += self.tree[i]






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.tree[:]
          for i in range(self.size - 1     ,0     ,-1 ):
                UpperCAmelCase          =	self.next_(A )
                if j < self.size:
                      arr[j] -= arr[i]
          return arr






    @staticmethod
    def     _UpperCamelCase							(			A ):
          return index + (index & (-index))






    @staticmethod
    def     _UpperCamelCase							(			A ):
          return index - (index & (-index))






    def     _UpperCamelCase							(			self     ,A     ,A ):
          if index == 0:
                self.tree[0] += value
                return
          while index < self.size:
                self.tree[index] += value
                UpperCAmelCase          =	self.next_(A )






    def     _UpperCamelCase							(			self     ,A     ,A ):
          self.add(A     ,value - self.get(A ) )






    def     _UpperCamelCase							(			self     ,A ):
          if right == 0:
                return 0
          UpperCAmelCase          =	self.tree[0]
          right -= 1  # make right inclusive
          while right > 0:
                result += self.tree[right]
                UpperCAmelCase          =	self.prev(A )
          return result






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return self.prefix(A ) - self.prefix(A )






    def     _UpperCamelCase							(			self     ,A ):
          return self.query(A     ,index + 1 )






    def     _UpperCamelCase							(			self     ,A ):
          value -= self.tree[0]
          if value < 0:
                return -1

          UpperCAmelCase          =	1  # Largest power of 2 <= size
          while j * 2 < self.size:
                j *= 2

          UpperCAmelCase          =	0

          while j > 0:
                if i + j < self.size and self.tree[i + j] <= value:
                      value -= self.tree[i + j]
                      i += j
                j //= 2
          return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()











"""simple docstring"""







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_UpperCamelCase					      =  pd.read_csv(
    """https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
    """position_salaries.csv"""
)
_UpperCamelCase					      =  dataset.iloc[:, 1:2].values
_UpperCamelCase					      =  dataset.iloc[:, 2].values


_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase					      =  train_test_split(X, y, test_size=0.2, random_state=0)


_UpperCamelCase					      =  PolynomialFeatures(degree=4)
_UpperCamelCase					      =  poly_reg.fit_transform(X)
_UpperCamelCase					      =  LinearRegression()
pol_reg.fit(X_poly, y)

def _a			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_snake_case							)							)				,	color="""blue"""							)
      plt.title("""Truth or Bluff (Linear Regression)"""							)
      plt.xlabel("""Position level"""							)
      plt.ylabel("""Salary"""							)
      plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003



