




from statistics import mean

import numpy as np







def      _a				(						SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    int					):
					__lowerCAmelCase           =	0
					# Number of processes finished
					__lowerCAmelCase           =	0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					__lowerCAmelCase           =	[0] * no_of_process
					# List to include calculation results
					__lowerCAmelCase           =	[0] * no_of_process

					# Sort by arrival time.
					__lowerCAmelCase           =	[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE_					)]
					__lowerCAmelCase           =	[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE_					)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										__lowerCAmelCase           =	0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															__lowerCAmelCase           =	arrival_time[i]

										__lowerCAmelCase           =	0
										# Index showing the location of the process being performed
										__lowerCAmelCase           =	0
										# Saves the current response ratio.
										__lowerCAmelCase           =	0
										for i in range(0				,				SCREAMING_SNAKE_CASE_					):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				__lowerCAmelCase           =	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				__lowerCAmelCase           =	temp
																				__lowerCAmelCase           =	i

        # Calculate the turn around time
										__lowerCAmelCase           =	current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										__lowerCAmelCase           =	1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time







def      _a				(						SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    list				,				SCREAMING_SNAKE_CASE_							:    int					):
					__lowerCAmelCase           =	[0] * no_of_process
					for i in range(0				,				SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
					UpperCamelCase__         =      5
					UpperCamelCase__         =      ["""A""", """B""", """C""", """D""", """E"""]
					UpperCamelCase__         =      [1, 2, 3, 4, 5]
					UpperCamelCase__         =      [1, 2, 3, 4, 5]

					UpperCamelCase__         =      calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					UpperCamelCase__         =      calculate_waiting_time(
					    process_name, turn_around_time, burst_time, no_of_process
					)

					print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
					for i in range(0, no_of_process):
										print(
										    f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
										    f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
										)

					print(f'''average waiting time : {mean(waiting_time):.5f}''')
					print(f'''average turn around time : {mean(turn_around_time):.5f}''')










from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__         =      """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

UpperCamelCase__         =      """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


UpperCamelCase__         =      """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,    _KWARGS_DESCRIPTION				)
class       a__   ( datasets.Metric				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return datasets.MetricInfo(
											    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(self._get_feature_types()      )		,      reference_urls=[
											        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
											    ]		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value("float"      )      ),
																    "references": datasets.Sequence(datasets.Value("float"      )      ),
																}
											else:
																return {
																    "predictions": datasets.Value("float"      ),
																    "references": datasets.Value("float"      ),
																}






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=None		,      _A="uniform_average"		,      _A=True      ):




											"""simple docstring"""



											__lowerCAmelCase           =	mean_squared_error(
											    _A		,      _A		,      sample_weight=_A		,      multioutput=_A		,      squared=_A      )

											return {"mse": mse}




