

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			:					int						=			pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__A			:					Optional[int]						=			dataset.iloc[:, 1:2].values
__A			:					List[Any]						=			dataset.iloc[:, 2].values


__A			, __A			, __A			, __A			:					List[Any]						=			train_test_split(X, y, test_size=0.2, random_state=0)


__A			:					Optional[int]						=			PolynomialFeatures(degree=4)
__A			:					str						=			poly_reg.fit_transform(X)
__A			:					Optional[Any]						=			LinearRegression()
pol_reg.fit(X_poly, y)





def 	__a     (      ):
    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 typing import Any

import numpy as np





def 	__a     (      A__			:  np.ndarray       ):
    return np.array_equal(A__			,  matrix.conjugate().T       )





def 	__a     (      A__			:  np.ndarray			,  A__			:  np.ndarray       ):
    SCREAMING_SNAKE_CASE										=   v.conjugate().T
    SCREAMING_SNAKE_CASE										=   v_star.dot(A__       )
    assert isinstance(A__			,  np.ndarray       )
    return (v_star_dot.dot(A__       )) / (v_star.dot(A__       ))





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
    SCREAMING_SNAKE_CASE										=   np.array([[1], [2], [3]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    print(rayleigh_quotient(A__			,  A__       )       )

    SCREAMING_SNAKE_CASE										=   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    assert rayleigh_quotient(A__			,  A__       ) == float(3       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    tests()