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


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


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



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    plt.scatter(A__			,		A__			,		color="red"		)
    plt.plot(A__			,		pol_reg.predict(poly_reg.fit_transform(A__		)		)			,		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



from __future__ import annotations

from decimal import Decimal

from numpy import array



def 						lowercase_      (       A__		)							->							list[list[float]]:
    """simple docstring"""

    snake_case								= Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(A__		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
        # Calculate the determinant of the matrix
        snake_case								= float(
            d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creates a copy of the matrix with swapped positions of the elements
        snake_case								= [[0.0, 0.0], [0.0, 0.0]]
        snake_case					,							snake_case								= matrix[1][1], matrix[0][0]
        snake_case					,							snake_case								= -matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(A__		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(A__		) == 3
        and len(matrix[0]		) == 3
        and len(matrix[1]		) == 3
        and len(matrix[2]		) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        snake_case								= float(
            (
                (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
                + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
                + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
            )
            - (
                (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
                + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
                + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
            )		)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."		)

        # Creating cofactor matrix
        snake_case								= [
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
            [d(0.0		), d(0.0		), d(0.0		)],
        ]
        snake_case								= (d(matrix[1][1]		) * d(matrix[2][2]		)) - (
            d(matrix[1][2]		) * d(matrix[2][1]		)
        )
        snake_case								= -(
            (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[1][0]		) * d(matrix[2][1]		)) - (
            d(matrix[1][1]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[2][2]		)) - (
            d(matrix[0][2]		) * d(matrix[2][0]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
        )
        snake_case								= (d(matrix[0][1]		) * d(matrix[1][2]		)) - (
            d(matrix[0][2]		) * d(matrix[1][1]		)
        )
        snake_case								= -(
            (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
        )
        snake_case								= (d(matrix[0][0]		) * d(matrix[1][1]		)) - (
            d(matrix[0][1]		) * d(matrix[1][0]		)
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                snake_case								= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        snake_case								= array(A__		)
        for i in range(3		):
            for j in range(3		):
                inverse_matrix[i][j] /= d(A__		)

        # Calculate the inverse of the matrix
        return [[float(d(A__		)		) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."		)

