def main():
# Чтение данных тестов
n, mu, sigm, alpha, precision, histogram_run, charts_run = ff.read_tests_settings(
INPUT_PATH + TESTS_FILENAME)
a, b = alg.get_bounds(mu, sigm)
# Формирование последовательности
sequence, density, count, modeling_time = alg.make_sequence(
n, a, b, mu, sigm)
# Формирование интервалов
intervals, intervals_width = alg.get_intervals(sequence)
hits, v = alg.interval_hits(sequence, intervals)
# Тест критерия Хи-квадрат
chi2_r, chi2_S, chi2_PSS, chi2_passed = alg.chi2_test(
sequence, intervals, hits, mu, sigm, alpha)
# Тест критерия Смирнова
sm_S, sm_PSS, sm_passed = alg.smirnov_test(sequence, mu, sigm, alpha)
ff.write_tests_results(OUTPUT_PATH + TESTS_RESULT, precision, mu, sigm,
alpha, a, b, sequence, density, count, intervals,
hits, modeling_time, chi2_r, chi2_S, chi2_PSS,
chi2_passed, sm_S, sm_PSS, sm_passed)
if histogram_run:
alg.make_histogram(OUTPUT_PATH + HISTOGRAM, intervals, intervals_width,
v, mu, sigm)
if charts_run:
alg.make_charts(OUTPUT_PATH + DENSITY_CHART,
OUTPUT_PATH + DISTRIBUTION_CHART, a, b, mu, sigm)
def main():
# Чтение данных тестов
n_list, correction_n, sigm, alpha, precision, histogram_run, chart_run = read_tests_settings(INPUT_PATH + TESTS_FILENAME)
# Инициализация параметров для выполнения нескольких распределений
DISTR_COUNT = 2
DISTR_TITLE = ["Рэлея", "Нормального"]
DISTR_FILENAME = ["rayleigh", "norm"]
EMPIRIC_DISTR = [distr2.rayleigh_distribution, distr2.normal_distribution]
THEOR_DISTR = [distr2.rayleigh.cdf, distr2.norm.cdf]
# Неиспользуемый параметр loc для распределения Рэлея
ARGS_LIST = [[0, sigm, correction_n], [correction_n]]
ARGS_LABEL = [["_", "sigm", "correction_n"], ["correction_n"]]
for i in range(DISTR_COUNT):
for j in range(len(n_list)):
# Формирование последовательности
sequence, modeling_time = alg.make_sequence(n_list[j], EMPIRIC_DISTR[i], *ARGS_LIST[i])
# Формирование интервалов
intervals, intervals_width = alg.get_intervals(sequence)
hits, v = alg.interval_hits(sequence, intervals)
# Тест критерия Хи-квадрат
chi2_r, chi2_S, chi2_PSS, chi2_passed = alg.chi2_test(n_list[j], intervals, hits, alpha, THEOR_DISTR[i], *ARGS_LIST[i][:-1])
# Тест критерия Крамера-Мизеса-Смирнов
cms_S, cms_PSS, cms_passed = alg.cms_test(sequence, alpha, THEOR_DISTR[i], *ARGS_LIST[i][:-1])
ff.write_tests_results(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], TESTS_RESULT, n_list[j]), precision,
ARGS_LIST[i][1:], ARGS_LABEL[i][1:], alpha, sequence, intervals, hits, modeling_time,
chi2_r, chi2_S, chi2_PSS, chi2_passed, cms_S, cms_PSS, cms_passed)
if histogram_run:
alg.make_histogram(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], HISTOGRAM, n_list[j]),
HISTOGRAM_TITLE + DISTR_TITLE[i], intervals, intervals_width, v, THEOR_DISTR[i], *ARGS_LIST[i][:-1])
if chart_run:
alg.make_chart(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], CHART),
CHART_TITLE + DISTR_TITLE[i], THEOR_DISTR[i], *ARGS_LIST[i][:-1])
def main():
# Чтение данных тестов
n_list, mu, nu, k, alpha, precision, histogram_run, chart_run = read_tests_settings(INPUT_PATH + TESTS_FILENAME)
# Инициализация параметров для выполнения нескольких распределений
DISTR_COUNT = 3
DISTR_TITLE = ["Фишера", "Хи-квадрат", "Нормального"]
DISTR_FILENAME = ["fisher", "chi2", "norm"]
EMPIRIC_DISTR = [distr1.fisher_distribution, distr1.chi2_distribution, distr1.normal_distribution]
THEOR_DISTR = [distr1.f.cdf, distr1.chi2.cdf, distr1.norm.cdf]
ARGS_LIST = [[mu, nu], [k], []]
ARGS_LABEL = [["mu", "nu"], ["k"], []]
for i in range(DISTR_COUNT):
for j in range(len(n_list)):
# Формирование последовательности
sequence, modeling_time = alg.make_sequence(n_list[j], EMPIRIC_DISTR[i], *ARGS_LIST[i])
# Формирование интервалов
intervals, intervals_width = alg.get_intervals(sequence)
hits, v = alg.interval_hits(sequence, intervals)
# Тест критерия Хи-квадрат
chi2_r, chi2_S, chi2_PSS, chi2_passed = alg.chi2_test(n_list[j], intervals, hits, alpha, THEOR_DISTR[i], *ARGS_LIST[i])
# Тест критерия Крамера-Мизеса-Смирнов
cms_S, cms_PSS, cms_passed = alg.cms_test(sequence, alpha, THEOR_DISTR[i], *ARGS_LIST[i])
ff.write_tests_results(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], TESTS_RESULT, n_list[j]), precision,
ARGS_LIST[i], ARGS_LABEL[i], alpha, sequence, intervals, hits, modeling_time,
chi2_r, chi2_S, chi2_PSS, chi2_passed, cms_S, cms_PSS, cms_passed)
if histogram_run:
alg.make_histogram(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], HISTOGRAM, n_list[j]),
HISTOGRAM_TITLE + DISTR_TITLE[i], intervals, intervals_width, v, THEOR_DISTR[i], *ARGS_LIST[i])
if chart_run:
alg.make_chart(OUTPUT_PATH + ff.get_filename(DISTR_FILENAME[i], CHART),
CHART_TITLE + DISTR_TITLE[i], THEOR_DISTR[i], *ARGS_LIST[i])
def main():
# Чтение данных тестов
n, sigm, alpha, precision, histogram_run, charts_run = ff.read_tests_settings(INPUT_PATH + TESTS_FILENAME)
# Формирование последовательности
sequence, modeling_time = alg.make_sequence(n, sigm)
# Формирование интервалов
intervals = alg.get_intervals(sequence)
hits, v = alg.interval_hits(sequence, intervals)
# Тест критерия типа Хи-квадрат
chi2_r, chi2_S, chi2_PSS, chi2_passed = alg.chi2_test(sequence, intervals, hits, sigm, alpha)
# Тест критерия типа Омега-квадрат Андерса-Дарлинга
ad_S, ad_PSS, ad_passed = alg.anderson_darling_test(sequence, sigm, alpha)
ff.write_tests_results(OUTPUT_PATH + TESTS_RESULT, precision, sigm, alpha, sequence, intervals, hits, modeling_time,
chi2_r, chi2_S, chi2_PSS, chi2_passed, ad_S, ad_PSS, ad_passed)
if histogram_run:
alg.make_histogram(OUTPUT_PATH + HISTOGRAM, intervals, v, sigm)
if charts_run:
alg.make_charts(OUTPUT_PATH + DENSITY_CHART, OUTPUT_PATH + DISTRIBUTION_CHART, sigm)