


import json
import sys







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							with open(__lowerCamelCase							,     encoding="utf-8"     ) as f:
														__snake_case		:      Tuple										=       json.load(__lowerCamelCase     )

							__snake_case		:      Optional[int]										=       ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

							for benchmark_name in sorted(__lowerCamelCase     ):
														__snake_case		:      str										=       results[benchmark_name]

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

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

																					__snake_case		:      Union[str, Any]										=       F' {new_val:f}' if isinstance(__lowerCamelCase							,     (int, float)     ) else "None"

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

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

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

							output_md.append("</details>"     )

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


if __name__ == "__main__":
			_snake_case					:		List[Any]      =    sys.argv[1]
			_snake_case					:		Any      =    sys.argv[2]

			format_json_to_md(input_json_file, output_md_file)





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

