


def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  len(lowerCAmelCase__   )
   __a					=  []
   for i in range(len(lowerCAmelCase__   ) - pat_len + 1   ):
      __a					=  True
      for j in range(lowerCAmelCase__   ):
         if s[i + j] != pattern[j]:
            __a					=  False
            break
      if match_found:
         position.append(lowerCAmelCase__   )
   return position


if __name__ == "__main__":
  assert naive_pattern_search('ABCDEFG', 'DE') == [3]
  print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))








"""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