



"""simple docstring"""






import json
import sys


def      _lowercase							(    __snake_case   ,__snake_case      )		->  Optional[int]:
			with open(__snake_case   ,encoding="utf-8"      ) as f:
						__lowerCAmelCase :    Optional[int]				=			json.load(__snake_case      )

			__lowerCAmelCase :    Any				=			["<details>", "<summary>Show updated benchmarks!</summary>", " "]

			for benchmark_name in sorted(__snake_case      ):
						__lowerCAmelCase :    List[Any]				=			results[benchmark_name]

						__lowerCAmelCase :    Any				=			benchmark_name.split("/"      )[-1]
						output_md.append(F"""### Benchmark: {benchmark_file_name}"""      )

						__lowerCAmelCase :    List[Any]				=			"| metric |"
						__lowerCAmelCase :    str				=			"|--------|"
						__lowerCAmelCase :    Any				=			"| new / old (diff) |"
						for metric_name in sorted(__snake_case      ):
									__lowerCAmelCase :    List[Any]				=			benchmark_res[metric_name]
									__lowerCAmelCase :    Optional[Any]				=			metric_vals["new"]
									__lowerCAmelCase :    Optional[int]				=			metric_vals.get("old"   ,__snake_case      )
									__lowerCAmelCase :    Tuple				=			metric_vals.get("diff"   ,__snake_case      )

									__lowerCAmelCase :    Tuple				=			F""" {new_val:f}""" if isinstance(__snake_case   ,(int, float)      ) else "None"

									if old_val is not None:
												val_str += F""" / {old_val:f}""" if isinstance(__snake_case   ,(int, float)      ) else "None"
									if dif_val is not None:
												val_str += F""" ({dif_val:f})""" if isinstance(__snake_case   ,(int, float)      ) else "None"

									title += " " + metric_name + " |"
									lines += "---|"
									value += val_str + " |"

						output_md += [title, lines, value, " "]

			output_md.append("</details>"      )

			with open(__snake_case   ,"w"   ,encoding="utf-8"      ) as f:
						f.writelines("\n".join(__snake_case      )      )


if __name__ == "__main__":
	__snake_case				:  Tuple =       sys.argv[1]
	__snake_case				:  str =       sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)




"""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