import subprocess
import sys
from argparse import ArgumentParser, Namespace
from typing import Tuple

import numpy as np

from module.bayes import bayes_classification
from module.decision_tree import decision_tree_classification
from module.k_nearest_neighbors import knn_classification
from module.reader import read_gestures_ds, read_heart_ds, read_weather_AUS
from module.support_vector_machine import svm_classification

"""
Sample usage:
    python main.py
    python main.py -s
"""


# MAIN ----------------------------------------------------------------------- #
def main() -> None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header("naive Bayes classifier")
            display_header("Heart data set")
            bayes_classification(read_heart_ds(test_percentage), save_latex, test_percentage)
            display_header("Gestures data set")
            bayes_classification(read_gestures_ds(test_percentage), save_latex, test_percentage)
            display_header("Weather data set")
            bayes_classification(read_weather_AUS(test_percentage), save_latex, test_percentage)
    else:
        display_header("Heart data set")
        process_classifiers(read_heart_ds(default_test_data_size), "heart", save_latex,
                            default_test_data_size)
        display_header("Gestures data set")
        process_classifiers(read_gestures_ds(default_test_data_size), "gestures", save_latex,
                            default_test_data_size)
        display_header("Weather data set")
        process_classifiers(read_weather_AUS(default_test_data_size), "weather", save_latex,
                            default_test_data_size)
    display_finish()


# DEF ------------------------------------------------------------------------ #
def process_classifiers(data_sets: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
                        data_set_name: str, save_latex: bool, test_data_size: float) -> None:
    display_header("k-nearest neighbors classifier")
    knn_classification(data_sets, True, data_set_name, save_latex)
    knn_classification(data_sets, False, data_set_name, save_latex)

    display_header("naive Bayes classifier")
    bayes_classification(data_sets, save_latex, test_data_size)

    display_header("support vector machine classifier")
    svm_classification(data_sets, data_set_name, save_latex)

    display_header("decision trees and random forests classifier")
    decision_tree_classification(data_sets, save_latex)


def display_header(name: str) -> None:
    print("------------------------------------------------------------------------")
    print(name)
    print()


def prepare_args() -> Namespace:
    arg_parser = ArgumentParser()

    arg_parser.add_argument(
        "-s", "--save", default=False, action="store_true",
        help="Create LaTeX source code based on generated data"
    )

    arg_parser.add_argument(
        "-nb", "--bayes", default=False, action="store_true",
        help="Run program with Bayes config"
    )

    return arg_parser.parse_args()


# UTIL ----------------------------------------------------------------------- #
def check_types_check_style() -> None:
    subprocess.call(["mypy", "."])
    subprocess.call(["flake8", "."])


def compile_to_pyc() -> None:
    subprocess.call(["python", "-m", "compileall", "."])


def check_if_exists_in_args(arg: str) -> bool:
    return arg in sys.argv


def display_finish() -> None:
    print("------------------------------------------------------------------------")
    print("FINISHED")
    print("------------------------------------------------------------------------")


# __MAIN__ ------------------------------------------------------------------- #
if __name__ == "__main__":
    if check_if_exists_in_args("-t"):
        check_types_check_style()
    elif check_if_exists_in_args("-b"):
        compile_to_pyc()
    else:
        main()

71711ac2f445b8457107288892ce72f20794edc9
<mask token>


def main() ->None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header('naive Bayes classifier')
            display_header('Heart data set')
            bayes_classification(read_heart_ds(test_percentage), save_latex,
                test_percentage)
            display_header('Gestures data set')
            bayes_classification(read_gestures_ds(test_percentage),
                save_latex, test_percentage)
            display_header('Weather data set')
            bayes_classification(read_weather_AUS(test_percentage),
                save_latex, test_percentage)
    else:
        display_header('Heart data set')
        process_classifiers(read_heart_ds(default_test_data_size), 'heart',
            save_latex, default_test_data_size)
        display_header('Gestures data set')
        process_classifiers(read_gestures_ds(default_test_data_size),
            'gestures', save_latex, default_test_data_size)
        display_header('Weather data set')
        process_classifiers(read_weather_AUS(default_test_data_size),
            'weather', save_latex, default_test_data_size)
    display_finish()


<mask token>


def prepare_args() ->Namespace:
    arg_parser = ArgumentParser()
    arg_parser.add_argument('-s', '--save', default=False, action=
        'store_true', help='Create LaTeX source code based on generated data')
    arg_parser.add_argument('-nb', '--bayes', default=False, action=
        'store_true', help='Run program with Bayes config')
    return arg_parser.parse_args()


<mask token>


def check_if_exists_in_args(arg: str) ->bool:
    return arg in sys.argv


<mask token>

<mask token>


def main() ->None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header('naive Bayes classifier')
            display_header('Heart data set')
            bayes_classification(read_heart_ds(test_percentage), save_latex,
                test_percentage)
            display_header('Gestures data set')
            bayes_classification(read_gestures_ds(test_percentage),
                save_latex, test_percentage)
            display_header('Weather data set')
            bayes_classification(read_weather_AUS(test_percentage),
                save_latex, test_percentage)
    else:
        display_header('Heart data set')
        process_classifiers(read_heart_ds(default_test_data_size), 'heart',
            save_latex, default_test_data_size)
        display_header('Gestures data set')
        process_classifiers(read_gestures_ds(default_test_data_size),
            'gestures', save_latex, default_test_data_size)
        display_header('Weather data set')
        process_classifiers(read_weather_AUS(default_test_data_size),
            'weather', save_latex, default_test_data_size)
    display_finish()


<mask token>


def prepare_args() ->Namespace:
    arg_parser = ArgumentParser()
    arg_parser.add_argument('-s', '--save', default=False, action=
        'store_true', help='Create LaTeX source code based on generated data')
    arg_parser.add_argument('-nb', '--bayes', default=False, action=
        'store_true', help='Run program with Bayes config')
    return arg_parser.parse_args()


def check_types_check_style() ->None:
    subprocess.call(['mypy', '.'])
    subprocess.call(['flake8', '.'])


<mask token>


def check_if_exists_in_args(arg: str) ->bool:
    return arg in sys.argv


def display_finish() ->None:
    print(
        '------------------------------------------------------------------------'
        )
    print('FINISHED')
    print(
        '------------------------------------------------------------------------'
        )


<mask token>

<mask token>


def main() ->None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header('naive Bayes classifier')
            display_header('Heart data set')
            bayes_classification(read_heart_ds(test_percentage), save_latex,
                test_percentage)
            display_header('Gestures data set')
            bayes_classification(read_gestures_ds(test_percentage),
                save_latex, test_percentage)
            display_header('Weather data set')
            bayes_classification(read_weather_AUS(test_percentage),
                save_latex, test_percentage)
    else:
        display_header('Heart data set')
        process_classifiers(read_heart_ds(default_test_data_size), 'heart',
            save_latex, default_test_data_size)
        display_header('Gestures data set')
        process_classifiers(read_gestures_ds(default_test_data_size),
            'gestures', save_latex, default_test_data_size)
        display_header('Weather data set')
        process_classifiers(read_weather_AUS(default_test_data_size),
            'weather', save_latex, default_test_data_size)
    display_finish()


<mask token>


def display_header(name: str) ->None:
    print(
        '------------------------------------------------------------------------'
        )
    print(name)
    print()


def prepare_args() ->Namespace:
    arg_parser = ArgumentParser()
    arg_parser.add_argument('-s', '--save', default=False, action=
        'store_true', help='Create LaTeX source code based on generated data')
    arg_parser.add_argument('-nb', '--bayes', default=False, action=
        'store_true', help='Run program with Bayes config')
    return arg_parser.parse_args()


def check_types_check_style() ->None:
    subprocess.call(['mypy', '.'])
    subprocess.call(['flake8', '.'])


<mask token>


def check_if_exists_in_args(arg: str) ->bool:
    return arg in sys.argv


def display_finish() ->None:
    print(
        '------------------------------------------------------------------------'
        )
    print('FINISHED')
    print(
        '------------------------------------------------------------------------'
        )


<mask token>

<mask token>


def main() ->None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header('naive Bayes classifier')
            display_header('Heart data set')
            bayes_classification(read_heart_ds(test_percentage), save_latex,
                test_percentage)
            display_header('Gestures data set')
            bayes_classification(read_gestures_ds(test_percentage),
                save_latex, test_percentage)
            display_header('Weather data set')
            bayes_classification(read_weather_AUS(test_percentage),
                save_latex, test_percentage)
    else:
        display_header('Heart data set')
        process_classifiers(read_heart_ds(default_test_data_size), 'heart',
            save_latex, default_test_data_size)
        display_header('Gestures data set')
        process_classifiers(read_gestures_ds(default_test_data_size),
            'gestures', save_latex, default_test_data_size)
        display_header('Weather data set')
        process_classifiers(read_weather_AUS(default_test_data_size),
            'weather', save_latex, default_test_data_size)
    display_finish()


def process_classifiers(data_sets: Tuple[np.ndarray, np.ndarray, np.ndarray,
    np.ndarray], data_set_name: str, save_latex: bool, test_data_size: float
    ) ->None:
    display_header('k-nearest neighbors classifier')
    knn_classification(data_sets, True, data_set_name, save_latex)
    knn_classification(data_sets, False, data_set_name, save_latex)
    display_header('naive Bayes classifier')
    bayes_classification(data_sets, save_latex, test_data_size)
    display_header('support vector machine classifier')
    svm_classification(data_sets, data_set_name, save_latex)
    display_header('decision trees and random forests classifier')
    decision_tree_classification(data_sets, save_latex)


def display_header(name: str) ->None:
    print(
        '------------------------------------------------------------------------'
        )
    print(name)
    print()


def prepare_args() ->Namespace:
    arg_parser = ArgumentParser()
    arg_parser.add_argument('-s', '--save', default=False, action=
        'store_true', help='Create LaTeX source code based on generated data')
    arg_parser.add_argument('-nb', '--bayes', default=False, action=
        'store_true', help='Run program with Bayes config')
    return arg_parser.parse_args()


def check_types_check_style() ->None:
    subprocess.call(['mypy', '.'])
    subprocess.call(['flake8', '.'])


def compile_to_pyc() ->None:
    subprocess.call(['python', '-m', 'compileall', '.'])


def check_if_exists_in_args(arg: str) ->bool:
    return arg in sys.argv


def display_finish() ->None:
    print(
        '------------------------------------------------------------------------'
        )
    print('FINISHED')
    print(
        '------------------------------------------------------------------------'
        )


<mask token>

import subprocess
import sys
from argparse import ArgumentParser, Namespace
from typing import Tuple

import numpy as np

from module.bayes import bayes_classification
from module.decision_tree import decision_tree_classification
from module.k_nearest_neighbors import knn_classification
from module.reader import read_gestures_ds, read_heart_ds, read_weather_AUS
from module.support_vector_machine import svm_classification

"""
Sample usage:
    python main.py
    python main.py -s
"""


# MAIN ----------------------------------------------------------------------- #
def main() -> None:
    test_data_percentage = np.arange(0.3, 0.95, 0.05)
    default_test_data_size = 0.3
    args = prepare_args()
    save_latex: bool = args.save
    bayes_config: bool = args.bayes
    if bayes_config:
        for test_percentage in test_data_percentage:
            display_header("naive Bayes classifier")
            display_header("Heart data set")
            bayes_classification(read_heart_ds(test_percentage), save_latex, test_percentage)
            display_header("Gestures data set")
            bayes_classification(read_gestures_ds(test_percentage), save_latex, test_percentage)
            display_header("Weather data set")
            bayes_classification(read_weather_AUS(test_percentage), save_latex, test_percentage)
    else:
        display_header("Heart data set")
        process_classifiers(read_heart_ds(default_test_data_size), "heart", save_latex,
                            default_test_data_size)
        display_header("Gestures data set")
        process_classifiers(read_gestures_ds(default_test_data_size), "gestures", save_latex,
                            default_test_data_size)
        display_header("Weather data set")
        process_classifiers(read_weather_AUS(default_test_data_size), "weather", save_latex,
                            default_test_data_size)
    display_finish()


# DEF ------------------------------------------------------------------------ #
def process_classifiers(data_sets: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
                        data_set_name: str, save_latex: bool, test_data_size: float) -> None:
    display_header("k-nearest neighbors classifier")
    knn_classification(data_sets, True, data_set_name, save_latex)
    knn_classification(data_sets, False, data_set_name, save_latex)

    display_header("naive Bayes classifier")
    bayes_classification(data_sets, save_latex, test_data_size)

    display_header("support vector machine classifier")
    svm_classification(data_sets, data_set_name, save_latex)

    display_header("decision trees and random forests classifier")
    decision_tree_classification(data_sets, save_latex)


def display_header(name: str) -> None:
    print("------------------------------------------------------------------------")
    print(name)
    print()


def prepare_args() -> Namespace:
    arg_parser = ArgumentParser()

    arg_parser.add_argument(
        "-s", "--save", default=False, action="store_true",
        help="Create LaTeX source code based on generated data"
    )

    arg_parser.add_argument(
        "-nb", "--bayes", default=False, action="store_true",
        help="Run program with Bayes config"
    )

    return arg_parser.parse_args()


# UTIL ----------------------------------------------------------------------- #
def check_types_check_style() -> None:
    subprocess.call(["mypy", "."])
    subprocess.call(["flake8", "."])


def compile_to_pyc() -> None:
    subprocess.call(["python", "-m", "compileall", "."])


def check_if_exists_in_args(arg: str) -> bool:
    return arg in sys.argv


def display_finish() -> None:
    print("------------------------------------------------------------------------")
    print("FINISHED")
    print("------------------------------------------------------------------------")


# __MAIN__ ------------------------------------------------------------------- #
if __name__ == "__main__":
    if check_if_exists_in_args("-t"):
        check_types_check_style()
    elif check_if_exists_in_args("-b"):
        compile_to_pyc()
    else:
        main()
