



"""simple docstring"""






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def      _lowercase							(    __snake_case      )		->  None:
			__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			analyze_text(__snake_case      )
			__lowerCAmelCase :    str				=			list(" " + ascii_lowercase      )
			# what is our total sum of probabilities.
			__lowerCAmelCase :    Tuple				=			sum(single_char_strings.values()      )

			# one length string
			__lowerCAmelCase :    Union[str, Any]				=			0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									__lowerCAmelCase :    List[str]				=			single_char_strings[ch]
									__lowerCAmelCase :    Union[str, Any]				=			my_str / all_sum
									my_fir_sum += prob * math.loga(__snake_case      )  # entropy formula.

    # print entropy
			print(F"""{round(-1 * my_fir_sum      ):.1f}"""      )

			# two len string
			__lowerCAmelCase :    List[Any]				=			sum(two_char_strings.values()      )
			__lowerCAmelCase :    str				=			0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									__lowerCAmelCase :    Union[str, Any]				=			cha + cha
									if sequence in two_char_strings:
												__lowerCAmelCase :    List[str]				=			two_char_strings[sequence]
												__lowerCAmelCase :    Tuple				=			int(__snake_case      ) / all_sum
												my_sec_sum += prob * math.loga(__snake_case      )

    # print second entropy
			print(F"""{round(-1 * my_sec_sum      ):.1f}"""      )

			# print the difference between them
			print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)      ):.1f}"""      )


def      _lowercase							(    __snake_case      )		->  tuple[dict, dict]:
			__lowerCAmelCase :    Optional[int]				=			Counter()  # type: ignore
			__lowerCAmelCase :    Optional[Any]				=			Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0   ,len(__snake_case      ) - 1      ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings


def      _lowercase							(    )		->  Union[str, Any]:
			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
	main()




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


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			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