

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase__	:   Optional[Any]								=     load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__	:   Optional[Any]								=     train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

UpperCamelCase__	:   Dict								=     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase__	:   Optional[Any]								=     [[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase__	:   Dict								=     knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)





from __future__ import annotations


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    [0] * len(_SCREAMING_SNAKE_CASE							)
    for i in range(1			, len(_SCREAMING_SNAKE_CASE							)							):
        SCREAMING_SNAKE_CASE_						=    duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]			, _SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_SCREAMING_SNAKE_CASE							)
    ]


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       # process id's
       UpperCamelCase__	:   List[str]								=     [1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print("Zero amount of processes")
              raise SystemExit(0)

       # duration time of all processes
       UpperCamelCase__	:   Any								=     [19, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print("Unable to match all id's with their duration time")
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       UpperCamelCase__	:   List[str]								=     calculate_waiting_times(duration_times)
       UpperCamelCase__	:   Union[str, Any]								=     calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       UpperCamelCase__	:   str								=     calculate_average_waiting_time(waiting_times)
       UpperCamelCase__	:   List[Any]								=     calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
       for i, process in enumerate(processes):
              print(
                  F'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
                  F'{turnaround_times[i]}'
              )
       print(F'Average waiting time = {average_waiting_time}')
       print(F'Average turn around time = {average_turnaround_time}')


