numpy.random.logistic

numpy.random.logistic(loc=0.0, scale=1.0, size=None)

Генерация выборок из логистического распределения.

Выборка осуществляется из логистического распределения со заданными параметрами, loc (позиция или среднее значение, также медиана) и scale (>0).

Примечание

Новый код должен использовать метод logistic экземпляра default_rng(), см. random-quick-start.

Параметры

locfloat or array_like of floats, optional

Параметр распределения. По умолчанию равен 0.

scalefloat or array_like of floats, optional

Параметр распределения. Должен быть неотрицательным. По умолчанию равен 1.

sizeint or tuple of ints, optional

Форма выходных данных. Если заданная форма, например, (m, n, k), тогда генерируется m * n * k выборок. Если размер равен None (по умолчанию), возвращается единственное значение, если loc и scale являются скалярными значениями. В противном случае, генерируется np.broadcast(loc, scale).size выборок.

Возвращает

outndarray or scalar

Сгенерированные выборки из заданного логистического распределения.

См. также

scipy.stats.logistic

функция плотности вероятности, распределение или функция кумулятивного распределения и т.д.

Generator.logistic

который следует использовать в новом коде.

Примечания

Плотность вероятности для логистического распределения:

<img alt="P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2}," src="data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0nMS4wJyBlbmNvZGluZz0nVVRGLTgnPz4KPCEtLSBUaGlzIGZpbGUgd2FzIGdlbmVyYXRlZCBieSBkdmlzdmdtIDIuOS4xIC0tPgo8c3ZnIHZlcnNpb249JzEuMScgeG1sbnM9J2h0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnJyB4bWxuczp4bGluaz0naHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluaycgd2lkdGg9JzE3MC4zMDczOTJwdCcgaGVpZ2h0PScyOS41OTMyMzRwdCcgdmlld0JveD0nMTQ3Ljk3MjA4MiA3OC43MDQ4NTcgMTcwLjMwNzM5MiAyOS41OTMyMzQnPgo8ZGVmcz4KPHBhdGggaWQ9J2czLTQwJyBkPSdNMi42NTQwNDcgMS45OTI1MjhDMi43MTc4MDggMS45OTI1MjggMi44MTM0NSAxLjk5MjUyOCAyLjgxMzQ1IDEuODk2ODg3QzIuODEzNDUgMS44NjUwMDYgMi44MDU0NzkgMS44NTcwMzYgMi43MDE4NjggMS43NTM0MjVDMS42MDk5NjMgLjcyNTI4IDEuMzM4OTc5LS43NTcxNjEgMS4zMzg5NzktMS45OTI1MjhDMS4zMzg5NzktNC4yODc5MiAyLjI4NzQyMi01LjM2Mzg4NSAyLjY5Mzg5OC01LjczMDUxMUMyLjgwNTQ3OS01LjgzNDEyMiAyLjgxMzQ1LTUuODQyMDkyIDIuODEzNDUtNS44ODE5NDNTMi43ODE1NjktNS45Nzc1ODQgMi43MDE4NjgtNS45Nzc1ODRDMi41NzQzNDYtNS45Nzc1ODQgMi4xNzU4NDEtNS41NzExMDggMi4xMTIwOC01LjQ5OTM3N0MxLjA0NDA4NS00LjM4MzU2MiAuODIwOTIyLTIuOTQ4OTQxIC44MjA5MjItMS45OTI1MjhDLjgyMDkyMi0uMjA3MjIzIDEuNTcwMTEyIDEuMjI3Mzk3IDIuNjU0MDQ3IDEuOTkyNTI4WicvPgo8cGF0aCBpZD0nZzMtNDEnIGQ9J00yLjQ2Mjc2NS0xLjk5MjUyOEMyLjQ2Mjc2NS0yLjc0OTY4OSAyLjMzNTI0My0zLjY1ODI4MSAxLjg0MTA5Ni00LjU5ODc1NUMxLjQ1MDU2LTUuMzMyMDA1IC43MjUyOC01Ljk3NzU4NCAuNTgxODE4LTUuOTc3NTg0Qy41MDIxMTctNS45Nzc1ODQgLjQ3ODIwNy01LjkyMTc5MyAuNDc4MjA3LTUuODgxOTQzQy40NzgyMDctNS44NTAwNjIgLjQ3ODIwNy01LjgzNDEyMiAuNTczODQ4LTUuNzM4NDgxQzEuNjg5NjY0LTQuNjc4NDU2IDEuOTQ0NzA3LTMuMjE5OTI1IDEuOTQ0NzA3LTEuOTkyNTI4QzEuOTQ0NzA3IC4yOTQ4OTQgLjk5NjI2NCAxLjM3ODgyOSAuNTg5Nzg4IDEuNzQ1NDU1Qy40ODYxNzcgMS44NDkwNjYgLjQ3ODIwNyAxLjg1NzAzNiAuNDc4MjA3IDEuODk2ODg3Uy41MDIxMTcgMS45OTI1MjggLjU4MTgxOCAxLjk5MjUyOEMuNzA5MzQgMS45OTI1MjggMS4xMDc4NDYgMS41ODYwNTIgMS4xNzE2MDYgMS41MTQzMjFDMi4yMzk2MDEgLjM5ODUwNiAyLjQ2Mjc2NS0xLjAzNjExNSAyLjQ2Mjc2NS0xLjk5MjUyOFonLz4KPHBhdGggaWQ9J2czLTUwJyBkPSdNMi4yNDc1NzItMS42MjU5MDNDMi4zNzUwOTMtMS43NDU0NTUgMi43MDk4MzgtMi4wMDg0NjggMi44MzczNi0yLjEyMDA1QzMuMzMxNTA3LTIuNTc0MzQ2IDMuODAxNzQzLTMuMDEyNzAyIDMuODAxNzQzLTMuNzM3OTgzQzMuODAxNzQzLTQuNjg2NDI2IDMuMDA0NzMyLTUuMzAwMTI1IDIuMDA4NDY4LTUuMzAwMTI1QzEuMDUyMDU1LTUuMzAwMTI1IC40MjI0MTYtNC41NzQ4NDQgLjQyMjQxNi0zLjg2NTUwNEMuNDIyNDE2LTMuNDc0OTY5IC43MzMyNS0zLjQxOTE3OCAuODQ0ODMyLTMuNDE5MTc4QzEuMDEyMjA0LTMuNDE5MTc4IDEuMjU5Mjc4LTMuNTM4NzMgMS4yNTkyNzgtMy44NDE1OTRDMS4yNTkyNzgtNC4yNTYwNCAuODYwNzcyLTQuMjU2MDQgLjc2NTEzMS00LjI1NjA0Qy45OTYyNjQtNC44Mzc4NTggMS41MzAyNjItNS4wMzcxMTEgMS45MjA3OTctNS4wMzcxMTFDMi42NjIwMTctNS4wMzcxMTEgMy4wNDQ1ODMtNC40MDc0NzIgMy4wNDQ1ODMtMy43Mzc5ODNDMy4wNDQ1ODMtMi45MDkwOTEgMi40NjI3NjUtMi4zMDMzNjIgMS41MjIyOTEtMS4zMzg5NzlMLjUxODA1Ny0uMzAyODY0Qy40MjI0MTYtLjIxNTE5MyAuNDIyNDE2LS4xOTkyNTMgLjQyMjQxNiAwSDMuNTcwNjFMMy44MDE3NDMtMS40MjY2NUgzLjU1NDY3QzMuNTMwNzYtMS4yNjcyNDggMy40NjY5OTktLjg2ODc0MiAzLjM3MTM1Ny0uNzE3MzFDMy4zMjM1MzctLjY1MzU0OSAyLjcxNzgwOC0uNjUzNTQ5IDIuNTkwMjg2LS42NTM1NDlIMS4xNzE2MDZMMi4yNDc1NzItMS42MjU5MDNaJy8+CjxwYXRoIGlkPSdnMC0wJyBkPSdNNS41NzExMDgtMS44MDkyMTVDNS42OTg2My0xLjgwOTIxNSA1Ljg3Mzk3My0xLjgwOTIxNSA1Ljg3Mzk3My0xLjk5MjUyOFM1LjY5ODYzLTIuMTc1ODQxIDUuNTcxMTA4LTIuMTc1ODQxSDEuMDA0MjM0Qy44NzY3MTItMi4xNzU4NDEgLjcwMTM3LTIuMTc1ODQxIC43MDEzNy0xLjk5MjUyOFMuODc2NzEyLTEuODA5MjE1IDEuMDA0MjM0LTEuODA5MjE1SDUuNTcxMTA4WicvPgo8cGF0aCBpZD0nZzEtMjInIGQ9J00xLjkyODc2Ny0yLjgxMzQ1QzEuOTY4NjE4LTIuOTY0ODgyIDIuMDMyMzc5LTMuMjI3ODk1IDIuMDMyMzc5LTMuMjY3NzQ2QzIuMDMyMzc5LTMuNDM1MTE4IDEuOTA0ODU3LTMuNTE0ODE5IDEuNzY5MzY1LTMuNTE0ODE5QzEuNDk4MzgxLTMuNTE0ODE5IDEuNDM0NjItMy4yNTE4MDYgMS40MDI3NC0zLjE0MDIyNEwuMjk0ODk0IDEuMjgzMTg4Qy4yNjMwMTQgMS40MTA3MSAuMjYzMDE0IDEuNDUwNTYgLjI2MzAxNCAxLjQ2NjUwMUMuMjYzMDE0IDEuNjY1NzUzIC40MjI0MTYgMS43MTM1NzQgLjUxODA1NyAxLjcxMzU3NEMuNTU3OTA4IDEuNzEzNTc0IC43NDEyMiAxLjcwNTYwNCAuODQ0ODMyIDEuNDk4MzgxQy44Njg3NDIgMS40MzQ2MiAxLjA5OTg3NSAuNDg2MTc3IDEuMjU5Mjc4LS4xNTE0MzJDMS4zOTQ3Ny0uMDQ3ODIxIDEuNjY1NzUzIC4wNzk3MDEgMi4wNzIyMjkgLjA3OTcwMUMyLjcyNTc3OCAuMDc5NzAxIDMuMTU2MTY0LS40NTQyOTYgMy4xODAwNzUtLjQ4NjE3N0MzLjMyMzUzNyAuMDYzNzYxIDMuODY1NTA0IC4wNzk3MDEgMy45NjExNDYgLjA3OTcwMUM0LjMyNzc3MSAuMDc5NzAxIDQuNTExMDgzLS4yMjMxNjMgNC41NzQ4NDQtLjM1ODY1NUM0LjczNDI0Ny0uNjQ1NTc5IDQuODQ1ODI4LTEuMTA3ODQ2IDQuODQ1ODI4LTEuMTM5NzI2QzQuODQ1ODI4LTEuMTg3NTQ3IDQuODEzOTQ4LTEuMjQzMzM3IDQuNzE4MzA2LTEuMjQzMzM3UzQuNjA2NzI1LTEuMTk1NTE3IDQuNTU4OTA0LS45OTYyNjRDNC40NDczMjMtLjU1NzkwOCA0LjI5NTg5LS4xNDM0NjIgMy45ODUwNTYtLjE0MzQ2MkMzLjgwMTc0My0uMTQzNDYyIDMuNzMwMDEyLS4yOTQ4OTQgMy43MzAwMTItLjUxODA1N0MzLjczMDAxMi0uNjUzNTQ5IDMuODE3Njg0LS45OTYyNjQgMy44NzM0NzQtMS4yMjczOTdMNC4wODA2OTctMi4wNDAzNDlDNC4xMjg1MTgtMi4yNDc1NzIgNC4xNjgzNjktMi40MTQ5NDQgNC4yMzIxMy0yLjY1NDA0N0M0LjI3MTk4LTIuODI5MzkgNC4zNTE2ODEtMy4xNDAyMjQgNC4zNTE2ODEtMy4xODgwNDVDNC4zNTE2ODEtMy4zODcyOTggNC4xOTIyNzktMy40MzUxMTggNC4wOTY2MzgtMy40MzUxMThDMy44MTc2ODQtMy40MzUxMTggMy43Njk4NjMtMy4yMzU4NjYgMy42ODIxOTItMi44NzcyMUwzLjUxNDgxOS0yLjIxNTY5MUwzLjI2Nzc0Ni0xLjIxOTQyN0wzLjE4ODA0NS0uOTAwNjIzQzMuMTcyMTA1LS44NTI4MDIgMi45NTY5MTItLjU0OTkzOCAyLjc3MzU5OS0uMzk4NTA2QzIuNjM4MTA3LS4yOTQ4OTQgMi40MDY5NzQtLjE0MzQ2MiAyLjExMjA4LS4xNDM0NjJDMS43Mzc0ODQtLjE0MzQ2MiAxLjQ4MjQ0MS0uMzQyNzE1IDEuNDgyNDQxLS44MzY4NjJDMS40ODI0NDEtMS4wNDQwODUgMS41NDYyMDItMS4yODMxODggMS41OTQwMjItMS40NzQ0NzFMMS45Mjg3NjctMi44MTM0NVonLz4KPHBhdGggaWQ9J2cxLTYxJyBkPSdNMy43MDYxMDItNS42NDI4MzlDMy43NTM5MjMtNS43NTQ0MjEgMy43NTM5MjMtNS43NzAzNjEgMy43NTM5MjMtNS43OTQyNzFDMy43NTM5MjMtNS44OTc4ODMgMy42NzQyMjItNS45Nzc1ODQgMy41NzA2MS01Ljk3NzU4NEMzLjQ0MzA4OC01Ljk3NzU4NCAzLjQxMTIwOC01Ljg4MTk0MyAzLjM3OTMyOC01LjgwMjI0MkwuNTE4MDU3IDEuNjU3NzgzQy40NzAyMzcgMS43NjkzNjUgLjQ3MDIzNyAxLjc4NTMwNSAuNDcwMjM3IDEuODA5MjE1Qy40NzAyMzcgMS45MTI4MjcgLjU0OTkzOCAxLjk5MjUyOCAuNjUzNTQ5IDEuOTkyNTI4Qy43ODEwNzEgMS45OTI1MjggLjgxMjk1MSAxLjg5Njg4NyAuODQ0ODMyIDEuODE3MTg2TDMuNzA2MTAyLTUuNjQyODM5WicvPgo8cGF0aCBpZD0nZzEtMTE1JyBkPSdNMy4yMTE5NTUtMi45OTY3NjJDMy4wMjg2NDMtMi45NjQ4ODIgMi44NjEyNy0yLjgyMTQyIDIuODYxMjctMi42MjIxNjdDMi44NjEyNy0yLjQ3ODcwNSAyLjk1NjkxMi0yLjM3NTA5MyAzLjEzMjI1NC0yLjM3NTA5M0MzLjI1MTgwNi0yLjM3NTA5MyAzLjQ5ODg3OS0yLjQ2Mjc2NSAzLjQ5ODg3OS0yLjgyMTQyQzMuNDk4ODc5LTMuMzE1NTY3IDIuOTgwODIyLTMuNTE0ODE5IDIuNDg2Njc1LTMuNTE0ODE5QzEuNDE4NjgtMy41MTQ4MTkgMS4wODM5MzUtMi43NTc2NTkgMS4wODM5MzUtMi4zNTExODNDMS4wODM5MzUtMi4yNzE0ODIgMS4wODM5MzUtMS45ODQ1NTggMS4zNzg4MjktMS43NjEzOTVDMS41NjIxNDItMS42MTc5MzMgMS42OTc2MzQtMS41OTQwMjIgMi4xMTIwOC0xLjUxNDMyMUMyLjM5MTAzNC0xLjQ1ODUzMSAyLjg0NTMzLTEuMzc4ODI5IDIuODQ1MzMtLjk2NDM4NEMyLjg0NTMzLS43NTcxNjEgMi42OTM4OTgtLjQ5NDE0NyAyLjQ3MDczNS0uMzQyNzE1QzIuMTc1ODQxLS4xNTE0MzIgMS43ODUzMDUtLjE0MzQ2MiAxLjY1Nzc4My0uMTQzNDYyQzEuNDY2NTAxLS4xNDM0NjIgLjkyNDUzMy0uMTc1MzQyIC43MjUyOC0uNDk0MTQ3QzEuMTMxNzU2LS41MTAwODcgMS4xODc1NDctLjgzNjg2MiAxLjE4NzU0Ny0uOTMyNTAzQzEuMTg3NTQ3LTEuMTcxNjA2IC45NzIzNTQtMS4yMjczOTcgLjg3NjcxMi0xLjIyNzM5N0MuNzQ5MTkxLTEuMjI3Mzk3IC40MjI0MTYtMS4xMzE3NTYgLjQyMjQxNi0uNjkzNEMuNDIyNDE2LS4yMjMxNjMgLjkxNjU2MyAuMDc5NzAxIDEuNjU3NzgzIC4wNzk3MDFDMy4wNDQ1ODMgLjA3OTcwMSAzLjMzOTQ3Ny0uOTAwNjIzIDMuMzM5NDc3LTEuMjM1MzY3QzMuMzM5NDc3LTEuOTUyNjc3IDIuNTU4NDA2LTIuMTA0MTEgMi4yNjM1MTItMi4xNTk5QzEuODgwOTQ2LTIuMjMxNjMxIDEuNTcwMTEyLTIuMjg3NDIyIDEuNTcwMTEyLTIuNjIyMTY3QzEuNTcwMTEyLTIuNzY1NjI5IDEuNzA1NjA0LTMuMjkxNjU2IDIuNDc4NzA1LTMuMjkxNjU2QzIuNzgxNTY5LTMuMjkxNjU2IDMuMDkyNDAzLTMuMjAzOTg1IDMuMjExOTU1LTIuOTk2NzYyWicvPgo8cGF0aCBpZD0nZzEtMTIwJyBkPSdNMy45OTMwMjYtMy4xODAwNzVDMy42NDIzNDEtMy4wOTI0MDMgMy42MjY0MDEtMi43ODE1NjkgMy42MjY0MDEtMi43NDk2ODlDMy42MjY0MDEtMi41NzQzNDYgMy43NjE4OTMtMi40NTQ3OTUgMy45MzcyMzUtMi40NTQ3OTVTNC4zODM1NjItMi41OTAyODYgNC4zODM1NjItMi45MzMwMDFDNC4zODM1NjItMy4zODcyOTggMy44ODE0NDUtMy41MTQ4MTkgMy41ODY1NS0zLjUxNDgxOUMzLjIxMTk1NS0zLjUxNDgxOSAyLjkwOTA5MS0zLjI1MTgwNiAyLjcyNTc3OC0yLjk0MDk3MUMyLjU1MDQzNi0zLjM2MzM4NyAyLjEzNTk5LTMuNTE0ODE5IDEuODA5MjE1LTMuNTE0ODE5Qy45NDA0NzMtMy41MTQ4MTkgLjQ1NDI5Ni0yLjUxODU1NSAuNDU0Mjk2LTIuMjk1MzkyQy40NTQyOTYtMi4yMjM2NjEgLjUxMDA4Ny0yLjE5MTc4MSAuNTczODQ4LTIuMTkxNzgxQy42Njk0ODktMi4xOTE3ODEgLjY4NTQzLTIuMjMxNjMxIC43MDkzNC0yLjMyNzI3M0MuODkyNjUzLTIuOTA5MDkxIDEuMzcwODU5LTMuMjkxNjU2IDEuNzg1MzA1LTMuMjkxNjU2QzIuMDk2MTM5LTMuMjkxNjU2IDIuMjQ3NTcyLTMuMDY4NDkzIDIuMjQ3NTcyLTIuNzgxNTY5QzIuMjQ3NTcyLTIuNjIyMTY3IDIuMTUxOTMtMi4yNTU1NDIgMi4wODgxNjktMi4wMDA0OThDMi4wMzIzNzktMS43NjkzNjUgMS44NTcwMzYtMS4wNjAwMjUgMS44MTcxODYtLjkwODU5M0MxLjcwNTYwNC0uNDc4MjA3IDEuNDE4NjgtLjE0MzQ2MiAxLjA2MDAyNS0uMTQzNDYyQzEuMDI4MTQ0LS4xNDM0NjIgLjgyMDkyMi0uMTQzNDYyIC42NTM1NDktLjI1NTA0NEMxLjAyMDE3NC0uMzQyNzE1IDEuMDIwMTc0LS42Nzc0NiAxLjAyMDE3NC0uNjg1NDNDMS4wMjAxNzQtLjg2ODc0MiAuODc2NzEyLS45ODAzMjQgLjcwMTM3LS45ODAzMjRDLjQ4NjE3Ny0uOTgwMzI0IC4yNTUwNDQtLjc5NzAxMSAuMjU1MDQ0LS40OTQxNDdDLjI1NTA0NC0uMTI3NTIyIC42NDU1NzkgLjA3OTcwMSAxLjA1MjA1NSAuMDc5NzAxQzEuNDc0NDcxIC4wNzk3MDEgMS43NjkzNjUtLjIzOTEwMyAxLjkxMjgyNy0uNDk0MTQ3QzIuMDg4MTY5LS4xMDM2MTEgMi40NTQ3OTUgLjA3OTcwMSAyLjgzNzM2IC4wNzk3MDFDMy43MDYxMDIgLjA3OTcwMSA0LjE4NDMwOS0uOTE2NTYzIDQuMTg0MzA5LTEuMTM5NzI2QzQuMTg0MzA5LTEuMjE5NDI3IDQuMTIwNTQ4LTEuMjQzMzM3IDQuMDY0NzU3LTEuMjQzMzM3QzMuOTY5MTE2LTEuMjQzMzM3IDMuOTUzMTc2LTEuMTg3NTQ3IDMuOTI5MjY1LTEuMTA3ODQ2QzMuNzY5ODYzLS41NzM4NDggMy4zMTU1NjctLjE0MzQ2MiAyLjg1MzMtLjE0MzQ2MkMyLjU5MDI4Ni0uMTQzNDYyIDIuMzk5MDA0LS4zMTg4MDQgMi4zOTkwMDQtLjY1MzU0OUMyLjM5OTAwNC0uODEyOTUxIDIuNDQ2ODI0LS45OTYyNjQgMi41NTg0MDYtMS40NDI1OUMyLjYxNDE5Ny0xLjY4MTY5NCAyLjc4OTUzOS0yLjM4MzA2NCAyLjgyOTM5LTIuNTM0NDk2QzIuOTQwOTcxLTIuOTQ4OTQxIDMuMjE5OTI1LTMuMjkxNjU2IDMuNTc4NTgtMy4yOTE2NTZDMy42MTg0MzEtMy4yOTE2NTYgMy44MjU2NTQtMy4yOTE2NTYgMy45OTMwMjYtMy4xODAwNzVaJy8+CjxwYXRoIGlkPSdnNC00MCcgZD0nTTMuODg1NDMgMi45MDUxMDZDMy44ODU0MyAyLjg2OTI0IDMuODg1NDMgMi44NDUzMyAzLjY4MjE5MiAyLjY0MjA5MkMyLjQ4NjY3NSAxLjQzNDYyIDEuODE3MTg2LS41Mzc5ODMgMS44MTcxODYtMi45NzY4MzdDMS44MTcxODYtNS4yOTYxMzkgMi4zNzkwNzgtNy4yOTI2NTMgMy43NjU4NzgtOC43MDMzNjJDMy44ODU0My04LjgxMDk1OSAzLjg4NTQzLTguODM0ODY5IDMuODg1NDMtOC44NzA3MzVDMy44ODU0My04Ljk0MjQ2NiAzLgyNTY1NC04Ljk2NjM3NiAzLjc3NzgzMy04Ljk2NjM3NkMzLjYyMjQxNi04Ljk2NjM3NiAyLjY0MjA5Mi04LjEwNTYwNCAyLjA1NjI4OS02LjkzMzk5OEMxLjQ0NjU3NS01LjcyNjUyNiAxLjE3MTYwNi00LjQ0NzMyMyAxLjE3MTYwNi0yLjk3NjgzN0MxLjE3MTYwNi0xLjkxMjgyNyAxLjMzODk3OS0uNDkwMTYyIDEuOTYwNjQ4IC43ODkwNDFDMi42NjYwMDIgMi4yMjM2NjEgMy42NDYzMjYgMy4wMDA3NDcgMy43Nzc4MzMgMy4wMDA3NDdDMy44MjU2NTQgMy4wMDA3NDcgMy44ODU0MyAyLjk3NjgzNyAzLjg4NTQzIDIuOTA1MTA2WicvPgo8cGF0aCBpZD0nZzQtNDEnIGQ9J00zLjM3MTM1Ny0yLjk3NjgzN0MzLjM3MTM1Ny0zLjg4NTQzIDMuMjUxODA2LTUuMzY3ODcgMi41ODIzMTYtNi43NTQ2N0MxLjg3Njk2MS04LjE4OTI5IC44OTY2MzgtOC45NjYzNzYgLjc2NTEzMS04Ljk2NjM3NkMuNzE3MzEtOC45NjYzNzYgLjY1NzUzNC04Ljk0MjQ2NiAuNjU3NTM0LTguODcwNzM1Qy42NTc1MzQtOC44MzQ4NjkgLjY1NzUzNC04LjgxMDk1OSAuODYwNzcyLTguNjA3NzIxQzIuMDU2Mjg5LTcuNDAwMjQ5IDIuNzI1Nzc4LTUuNDI3NjQ2IDIuNzI1Nzc4LTIuOTg4NzkyQzIuNzI1Nzc4LS42Njk0ODkgMi4xNjM4ODUgMS4zMjcwMjQgLjc3NzA4NiAyLjczNzczM0MuNjU3NTM0IDIuODQ1MzMgLjY1NzUzNCAyLjg2OTI0IC42NTc1MzQgMi45MDUxMDZDLjY1NzUzNCAyLjk3NjgzNyAuNzE3MzEgMy4wMDA3NDcgLjc2NTEzMSAzLjAwMDc0N0MuOTIwNTQ4IDMuMDAwNzQ3IDEuOTAwODcyIDIuMTM5OTc1IDIuNDg2Njc1IC45NjgzNjlDMy4wOTYzODktLjI1MTA1OSAzLjM3MTM1Ny0xLjU0MjIxNyAzLjM3MTM1Ny0yLjk3NjgzN1onLz4KPHBhdGggaWQ9J2c0LTQzJyBkPSdNNC43NzAxMTItMi43NjE2NDRIOC4wNjk3MzhDOC4yMzcxMTEtMi43NjE2NDQgOC40NTIzMDQtMi43NjE2NDQgOC40NTIzMDQtMi45NzY4MzdDOC40NTIzMDQtMy4yMDM5ODUgOC4yNDkwNjYtMy4yMDM5ODUgOC4wNjk3MzgtMy4yMDM5ODVINC43NzAxMTJWLTYuNTAzNjExQzQuNzcwMTEyLTYuNjcwOTg0IDQuNzcwMTEyLTYuODg2MTc3IDQuNTU0OTE5LTYuODg2MTc3QzQuMzI3NzcxLTYuODg2MTc3IDQuMzI3NzcxLTYuNjgyOTM5IDQuMzI3NzcxLTYuNTAzNjExVi0zLjIwMzk4NUgxLjAyODE0NEMuODYwNzcyLTMuMjAzOTg1IC42NDU1NzktMy4yMDM5ODUgLjY0NTU3OS0yLjk4ODc5MkMuNjQ1NTc5LTIuNzYxNjQ0IC44NDg4MTctMi43NjE2NDQgMS4wMjgxNDQtMi43NjE2NDRINC4zMjc3NzFWLjUzNzk4M0M0LjMyNzc3MSAuNzA1MzU1IDQuMzI3NzcxIC45MjA1NDggNC41NDI5NjQgLjkyMDU0OEM0Ljc3MDExMiAuOTIwNTQ4IDQuNzcwMTEyIC43MTczMSA0Ljc3MDExMiAuNTM3OTgzVi0yLjc2MTY0NFonLz4KPHBhdGggaWQ9J2c0LTQ5JyBkPSdNMy40NDMwODgtNy42NjMyNjNDMy40NDMwODgtNy45MzgyMzIgMy40NDMwODgtNy45NTAxODcgMy4yMDM5ODUtNy45NTAxODdDMi45MTcwNjEtNy4yMjczOTcgMi4zMTkzMDMtNy4xODUwNTYgMS4wODc5Mi03LjE4NTA1NlYtNi44MzgzNTZDMS4zNjI4ODktNi44MzgzNTYgMS45NjA2NDgtNi44MzgzNTYgMi42MTgxODItNy4xNDkxOTFWLS45MjA1NDhDMi42MTgxODItLjQ5MDE2MiAyLjU4MjMxNi0uMzQ2NyAxLjUzMDI2Mi0uMzQ2N0gxLjE1OTY1MVYwQzEuNDgyNDQxLS4wMjM5MSAyLjY0MjA5Mi0uMDIzOTEgMy4wMzY2MTMtLjAyMzkxUzQuNTc4ODI5LS4wMjM5MSA0Ljk5MTYxOSAwVi0uMzQ2N0g0LjUzMTAwOUMzLjQ3ODk1NC0uMzQ2NyAzLjQ0MzA4OC0uNDkwMTYyIDMuNDQzMDg4LS45MjA1NDhWLTcuNjYzMjYzWicvPgo8cGF0aCBpZD0nZzQtNjEnIGQ9J004LjA2OTczOC0zLjg3MzQ3NEM4LjIzNzExMS0zLjg3MzQ3NCA4LjQ1MjMwNC0zLjg3MzQ3NCA4LjQ1MjMwNC00LjA4ODY2N0M4LjQ1MjMwNC00LjMxNTgxNiA4LjI0OTA2Ni00LjMxNTgxNiA4LjA2OTczOC00LjMxNTgxNkgxLjAyODE0NEMuODYwNzcyLTQuMzE1ODE2IC42NDU1NzktNC4zMTU4MTYgLjY0NTU3OS00LjEwMDYyM0MuNjQ1NTc5LTMuODczNDc0IC44NDg4MTctMy44NzM0NzQgMS4wMjgxNDQtMy44NzM0NzRIOC4wNjk3MzhaTTguMDY5NzM4LTEuNjQ5ODEzQzguMjM3MTExLTEuNjQ5ODEzIDguNDUyMzA0LTEuNjQ5ODEzIDguNDUyMzA0LTEuODY1MDA2QzguNDUyMzA0LTIuMDkyMTU0IDguMjQ5MDY2LTIuMDkyMTU0IDguMDY5NzM4LTIuMDkyMTU0SDEuMDI4MTQ0Qy44NjA3NzItMi4wOTIxNTQgLjY0NTU3OS0yLjA5MjE1NCAuNjQ1NTc5LTEuODc2OTYxQy42NDU1NzktMS42NDk4MTMgLjg0ODgxNy0xLjY0OTgxMyAxLjAyODE0NC0xLjY0OTgxM0g4LjA2OTczOFonLz4KPHBhdGggaWQ9J2cyLTU5JyBkPSdNMi

где = местоположение и = масштаб.

Распределение логистики используется в задачах экстремальных значений, где оно может выступать в качестве смеси распределений Гумбеля, в эпидемиологии и в системе рейтинга Эло Всемирной шахматной федерации (ФИДЕ), предполагая, что производительность каждого игрока является случайной величиной с распределением логистики.

Ссылки

1

Реис, Р.-Д. и Томас М. (2001), «Статистический анализ экстремальных значений, от страхования, финансов, гидрологии и других областей», Birkhauser Verlag, Базель, стр. 132-133.

2

Вейстейн, Эрик В. «Логистическое распределение». Из MathWorld – Wolfram Web Resource. http://mathworld.wolfram.com/LogisticDistribution.html

3

Википедия, «Логистическое распределение», https://en.wikipedia.org/wiki/Logistic_distribution

Примеры

Генерация выборок из распределения:

>>> loc, scale = 10, 1
>>> s = np.random.logistic(loc, scale, 10000)
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, bins=50)

# построение графика по распределению

>>> def logist(x, loc, scale):
...     return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
>>> lgst_val = logist(bins, loc, scale)
>>> plt.plot(bins, lgst_val * count.max() / lgst_val.max())
>>> plt.show()