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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "196b8a50-3d29-45c3-82b9-f4a09b49491d"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Семинар 12\n",
"\n",
"# Введение в численные методы оптимизации (Ю. Е. Нестеров Введение в выпуклую оптимизацию, гл. 1 $\\S$ 1.1)\n",
"\n",
" 1. Обзор материала весеннего семестра\n",
" 2. Постановка задачи\n",
" 3. Общая схема решения\n",
" 4. Сравнение методов оптимизации\n",
" 5. Методы одномерной минимизации\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "2a573842-172b-4931-b0dd-9c9d3c47a450"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Обзор материала весеннего семестра\n",
"\n",
"Также на [странице курса](https://github.com/amkatrutsa/MIPT-Opt#Весенний-семестр).\n",
"\n",
"1. Методы решения задач **безусловной** оптимизации\n",
" - Одномерная минимизация (**уже сегодня!**)\n",
" - Градиентный спуск\n",
" - Метод Ньютона\n",
" - Квазиньютоновские методы\n",
" - Метод сопряжённых градиентов \n",
" - Опционально:\n",
" - Решение задачи наименьших квадратов\n",
" - Оптимальные методы и нижние оценки\n",
"2. Методы решения задач **условной** оптимизации\n",
" - Линейное программирование\n",
" - Методы проекции градиента и условного градиента\n",
" - Методы штрафных и барьерных функций\n",
" - Метод модифицированой функции Лагранжа"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Организация работы в семестре\n",
"\n",
"1. Семинар и лекция раз в неделю\n",
"2. Два задания в течение семестра\n",
"3. Midterm в середине семестра\n",
"4. Итоговая контрольная в конце семестра\n",
"5. Экзамен в конце семестра (схема выставлении оценки аналогична осеннему семестру)\n",
"6. Миниконтрольные в начале каждого семинара\n",
"7. Домашнее задание почти каждую неделю: $\\TeX$ или Jupyter Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Постановка задачи\n",
"\n",
"\\begin{equation}\n",
"\\begin{split}\n",
"& \\min_{x \\in S} f_0(x)\\\\\n",
"\\text{s.t. } & f_j(x) = 0, \\; j = 1,\\ldots,m\\\\\n",
"& g_k(x) \\leq 0, \\; k = 1,\\ldots,p\n",
"\\end{split}\n",
"\\end{equation}\n",
"где $S \\subseteq \\mathbb{R}^n$, $f_j: S \\rightarrow \\mathbb{R}, \\; j = 0,\\ldots,m$, $g_k: S \\rightarrow \\mathbb{R}, \\; k=1,\\ldots,p$\n",
"\n",
"Все функции как минимум непрерывны. \n",
"\n",
"Важный факт</span>: задачи **нелинейной** оптимизации \n",
"\n",
"в их самой общей форме являются **численно неразрешимыми**!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Аналитические результаты\n",
"- Необходимое условие первого порядка: \n",
"\n",
"если $x^*$ точка локального минимума дифференцируемой функции $f(x)$, тогда \n",
"$$\n",
"f'(x^*) = 0\n",
"$$\n",
"- Необходимое условие второго порядка \n",
"\n",
"если $x^*$ точка локального минимума дважды дифференцируемой функции $f(x)$, тогда \n",
"\n",
"$$\n",
"f'(x^*) = 0 \\quad \\text{и} \\quad f''(x^*) \\succeq 0\n",
"$$\n",
"- Достаточное условие: \n",
"\n",
"пусть $f(x)$ дважды дифференцируемая функция, и пусть точка $x^*$ удовлетворяет условиям\n",
"\n",
"$$\n",
"f'(x^*) = 0 \\quad f''(x^*) \\succ 0,\n",
"$$\n",
"тогда $x^*$ является точкой строго локального минимума функции $f(x)$.\n",
"\n",
"**Замечание**: убедитесь, что Вы понимаете, как доказывать эти\n",
"\n",
"результаты!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Особенности численного решения\n",
"\n",
"1. Точно решить задачу принципиально невозможно из-за погрешности машинной арифметики\n",
"2. Необходимо задать критерий обнаружения решения\n",
"3. Необходимо определить, какую информацию о задаче использовать"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Общая итеративная схема\n",
"\n",
"Дано: начальное приближение $x$, требуемая точность $\\varepsilon$.\n",
"\n",
"```python\n",
"def GeneralScheme(x, epsilon):\n",
" \n",
" while StopCriterion(x) > epsilon:\n",
" \n",
" OracleResponse = RequestOracle(x)\n",
" \n",
" UpdateInformation(I, x, OracleResponse)\n",
" \n",
" x = NextPoint(I, x)\n",
" \n",
" return x\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Вопросы\n",
"1. Какие критерии остановки могут быть?\n",
"2. Что такое оракул и зачем он нужен?\n",
"3. Что такое информационная модель?\n",
"4. Как вычисляется новая точка?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Критерии остановки\n",
"1. Сходимость по аргументу: \n",
"$$\n",
"\\| x_k - x^* \\|_2 < \\varepsilon\n",
"$$ \n",
"2. Сходимость по функции: \n",
"$$\n",
"\\| f_k - f^* \\|_2 < \\varepsilon\n",
"$$ \n",
"3. Выполнение необходимого условия \n",
"$$\n",
"\\| f'(x_k) \\|_2 < \\varepsilon\n",
"$$\n",
"\n",
"Но ведь $x^*$ неизвестна!\n",
"\n",
"Тогда\n",
"$$\n",
"\\|x_{k+1} - x_k \\| = \\|x_{k+1} - x_k + x^* - x^* \\| \\leq \\|x_{k+1} - x^* \\| + \\| x_k - x^* \\| \\leq 2\\varepsilon\n",
"$$\n",
"\n",
"Аналогично для сходимости по функции...\n",
"\n",
"**Замечание**: лучше использовать относительные изменения этих величин! Например $\\dfrac{\\|x_{k+1} - x_k \\|_2}{\\| x_k \\|_2}$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Что такое оракул?\n",
"**Определение**: оракулом называют некоторое абстрактное \n",
"\n",
"устройство, которое отвечает на последовательные вопросы \n",
"\n",
"метода\n",
"\n",
"Аналогия из ООП: \n",
"\n",
"- оракул - это виртуальный метод базового класса\n",
"- каждая задача - производный класс\n",
"- оракул определяется для каждой задачи отдельно согласно общему определению в базовом классе\n",
"\n",
"**Концепция чёрного ящика**\n",
"1. Единственной информацией, получаемой в ходе работы итеративного метода, являются ответы оракула\n",
"2. Ответы оракула являются *локальными*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Информация о задаче\n",
"1. Каждый ответ оракула даёт локальную информацию о поведении функции в точке\n",
"2. Агрегируя все полученные ответы оракула, обновляем информацию о глобальном виде целевой функции:\n",
" - кривизна\n",
" - направление убывания\n",
" - etc"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Вычисление следующей точки\n",
"\n",
"$$\n",
"x_{k+1} = x_{k} + \\alpha_k h_k\n",
"$$\n",
"\n",
"- **Линейный поиск**: фиксируется направление $h_k$ и производится поиск по этому направлению \"оптимального\" значения $\\alpha_k$\n",
"- **Метод доверительных областей**: фиксируется допустимый размер *области* по некоторой норме $\\| \\cdot \\| \\leq \\alpha$ и *модель* целевой функции, которая хорошо её аппроксимирует в выбранной области. \n",
" Далее производится поиск направления $h_k$, минимизирующего модель целевой функции и не выводящего точку $x_k + h_k$ за пределы доверительной области\n",
"\n",
"Вопросы:\n",
"1. Как выбрать $\\alpha_k$?\n",
"2. Как выбрать $h_k$?\n",
"3. Как выбрать модель?\n",
"4. Как выбрать область?\n",
"5. Как выбрать размер области? \n",
"\n",
"<span style=\"color:red\">\n",
" В курсе рассматривается только линейный поиск!</span> \n",
" \n",
"Однако несколько раз копцепция метода доверительных областей \n",
"\n",
"будет использована."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Как сравнивать методы оптимизации?\n",
"Для заданного класса задач сравнивают следующие величины:\n",
"1. Сложность\n",
" - аналитическая: число обращений к оракулу для решения задачи с точностью $\\varepsilon$\n",
" - арифметическая: общее число всех вычислений, необходимых для решения задачи с точностью $\\varepsilon$\n",
"2. Скорость сходимости\n",
"3. Эксперименты"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Скорости сходимости \n",
"1. Сублинейная\n",
"$$\n",
"\\| x_{k+1} - x^* \\|_2 \\leq C k^{\\alpha},\n",
"$$\n",
"где $\\alpha < 0$ и $ 0 < C < \\infty$\n",
"2. Линейная (геометрическая прогрессия)\n",
"$$\n",
"\\| x_{k+1} - x^* \\|_2 \\leq Cq^k, \n",
"$$\n",
"где $q \\in (0, 1)$ и $ 0 < C < \\infty$\n",
"\n",
"3. Сверхлинейная \n",
"$$\n",
"\\| x_{k+1} - x^* \\|_2 \\leq Cq^{k^2}, \n",
"$$\n",
"где $q \\in (0, 1)$ и $ 0 < C < \\infty$\n",
"4. Квадратичная\n",
"$$\n",
"\\| x_{k+1} - x^* \\|_2 \\leq C\\| x_k - x^* \\|^2_2, \\qquad \\text{или} \\qquad \\| x_{k+1} - x^* \\|_2 \\leq C q^{2^k}\n",
"$$\n",
"где $q \\in (0, 1)$ и $ 0 < C < \\infty$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Оптимальные методы: can we do better?\n",
"- Доказывают нижние оценки скоростей сходимости для класса задач и методов фиксированного порядка\n",
"- Предлагают методы, на которых эти нижние оценки достигаются $\\Rightarrow$ доказана оптимальность \n",
"- Ниже про значение теорем сходимости\n",
"\n",
"Оптимальным методам и нижним оценкам будет, возможно, \n",
"\n",
"посвящён отдельный семинар."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"outputs": [
{
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f2c5fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"C = 10\n",
"alpha = -0.5\n",
"q = 0.9\n",
"num_iter = 7\n",
"sublinear = np.array([C * k**alpha for k in range(1, num_iter + 1)])\n",
"linear = np.array([C * q**k for k in range(1, num_iter + 1)])\n",
"superlinear = np.array([C * q**(k**2) for k in range(1, num_iter + 1)])\n",
"quadratic = np.array([C * q**(2**k) for k in range(1, num_iter + 1)])\n",
"plt.figure(figsize=(12,8))\n",
"plt.semilogy(np.arange(1, num_iter+1), sublinear, \n",
" label=r\"Sublinear, $\\alpha = -0.5$\")\n",
"plt.semilogy(np.arange(1, num_iter+1), superlinear, \n",
" label=r\"Superlinear, $q = 0.5$\")\n",
"plt.semilogy(np.arange(1, num_iter+1), linear, \n",
" label=r\"Linear, $q = 0.5$\")\n",
"plt.semilogy(np.arange(1, num_iter+1), quadratic, \n",
" label=r\"Quadratic, $q = 0.5$\")\n",
"plt.xlabel(\"Number of iteration, $k$\", fontsize=24)\n",
"plt.ylabel(\"Error rate upper bound\", fontsize=24)\n",
"plt.legend(loc=\"best\", fontsize=20)\n",
"plt.xticks(fontsize = 20)\n",
"_ = plt.yticks(fontsize = 20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Значение теорем сходимости (Б.Т. Поляк Введение в оптимизацию, гл. 1, $\\S$ 6)\n",
"1. Что дают теоремы сходимости\n",
" - класс задач, для которых можно рассчитывать на применимость метода (важно не завышать условия!)\n",
" - выпуклость\n",
" - гладкость\n",
" - качественное поведение метода\n",
" - существенно ли начальное приближение\n",
" - по какому функционалу есть сходимость\n",
" - оценку скорости сходимости\n",
" - теоретическая оценка поведения метода без проведения экспериментов\n",
" - определение факторов, которые влияют на сходимость (обусловленность, размерность, etc)\n",
" - иногда заранее можно выбрать число итераций для достижения заданной точности \n",
"\n",
"2. Что **НЕ** дают теоремы сходимости\n",
" - сходимость метода **ничего не говорит** о целесообразности его применения\n",
" - оценки сходимости зависят от неизвестных констант - неконструктивный характер\n",
" - учёт ошибок округления и точности решения вспомогательных задач\n",
" \n",
"**Мораль**: нужно проявлять разумную осторожность \n",
"\n",
"и здравый смысл!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Классификация задач\n",
"1. Безусловная оптимизация\n",
" - целевая функция липшицева\n",
" - градиент целевой функции липшицев\n",
"2. Условная оптимизация\n",
" - многогранник\n",
" - множество простой структуры\n",
" - общего вида"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Классификация методов\n",
"1. Методы нулевого порядка: оракул возвращает только значение функции $f(x)$\n",
"\n",
"2. Методы первого порядка: оракул возвращает значение функции $f(x)$ и её градиент $f'(x)$\n",
"\n",
"3. Методы второго порядка: оракул возвращает значение функции $f(x)$, её градиент $f'(x)$ и гессиан $f''(x)$.\n",
"\n",
"**Вопрос**: существуют ли методы более высокого порядка?\n",
"\n",
"1. Одношаговые методы \n",
"$$\n",
"x_{k+1} = \\Phi(x_k)\n",
"$$\n",
"2. Многошаговые методы\n",
"$$\n",
"x_{k+1} = \\Phi(x_k, x_{k-1}, ...)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Одномерная минимизация\n",
"**Определение**. Функция $f(x)$ называется унимодальной на $[a, b]$, если существует такая точка $x^* \\in [a, b]$, что \n",
"- $f(x_1) > f(x_2)$ для любых $a \\leq x_1 < x_2 < x^*$, \n",
"\n",
"и \n",
"- $f(x_1) < f(x_2)$ для любых $x^* < x_1 < x_2 \\leq b$.\n",
"\n",
"**Вопрос**: какая геометрия унимодальных функций?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Метод дихотомии\n",
"\n",
"Идея из информатики первого семестра: делим отрезок $[a,b]$ на две равные части пока не найдём минимум унимодальной функции.\n",
"\n",
"Пусть $N$ - число вычислений функции $f$, тогда можно выполнить $K = \\frac{N - 1}{2}$ итераций и справедлива следующая оценка\n",
"$$\n",
"|x_{K+1} - x^*| \\leq \\frac{b_{K+1} - a_{K+1}}{2} = \\left( \\frac{1}{2} \\right)^{\\frac{N-1}{2}} (b - a) \\approx 0.5^{K} (b - a) \n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def binary_search(f, a, b, epsilon, callback=None):\n",
" c = (a + b) / 2.0\n",
" while abs(b - a) > epsilon:\n",
"# Check left subsegment\n",
" y = (a + c) / 2.0\n",
" if f(y) <= f(c):\n",
" b = c\n",
" c = y\n",
" else:\n",
"# Check right subsegment\n",
" z = (b + c) / 2.0\n",
" if f(c) <= f(z):\n",
" a = y\n",
" b = z\n",
" else:\n",
" a = c\n",
" c = z\n",
" if callback is not None:\n",
" callback(a, b)\n",
" return c"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def my_callback(a, b, left_bound, right_bound, approximation):\n",
" left_bound.append(a)\n",
" right_bound.append(b)\n",
" approximation.append((a + b) / 2.0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.313225746154785e-10\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5,1,'Objective function')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a918588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# %matplotlib inline\n",
"import numpy as np\n",
"# import matplotlib.pyplot as plt\n",
"\n",
"left_boud_bs = []\n",
"right_bound_bs = []\n",
"approximation_bs = []\n",
"\n",
"callback_bs = lambda a, b: my_callback(a, b, \n",
" left_boud_bs, right_bound_bs, approximation_bs)\n",
"\n",
"# Target unimodal function on given segment\n",
"f = lambda x: (x - 2) * x * (x + 2)**2 # np.power(x+2, 2)\n",
"# f = lambda x: -np.sin(x)\n",
"x_true = -2\n",
"# x_true = np.pi / 2.0\n",
"a = -3\n",
"b = -1.5\n",
"epsilon = 1e-8\n",
"x_opt = binary_search(f, a, b, epsilon, callback_bs)\n",
"print(np.abs(x_opt - x_true))\n",
"plt.figure(figsize=(10,6))\n",
"plt.plot(np.linspace(a,b), f(np.linspace(a,b)))\n",
"plt.title(\"Objective function\", fontsize=18)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Метод золотого сечения\n",
"Идея: делить отрезок $[a,b]$ не на две равные насти, а в пропорции \"золотого сечения\".\n",
"\n",
"Оценим скорость сходимости аналогично методу дихотомии:\n",
"$$\n",
"|x_{K+1} - x^*| \\leq b_{K+1} - a_{K+1} = \\left( \\frac{1}{\\tau} \\right)^{N-1} (b - a) \\approx 0.618^K(b-a),\n",
"$$\n",
"где $\\tau = \\frac{\\sqrt{5} + 1}{2}$.\n",
"\n",
"- Константа геометрической прогрессии **больше**, чем у метода дихотомии\n",
"- Количество вызовов функции **меньше**, чем у метода дихотомии"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def golden_search(f, a, b, tol=1e-5, callback=None):\n",
" tau = (np.sqrt(5) + 1) / 2.0\n",
" y = a + (b - a) / tau**2\n",
" z = a + (b - a) / tau\n",
" while b - a > tol:\n",
" if f(y) <= f(z):\n",
" b = z\n",
" z = y\n",
" y = a + (b - a) / tau**2\n",
" else:\n",
" a = y\n",
" y = z\n",
" z = a + (b - a) / tau\n",
" if callback is not None:\n",
" callback(a, b)\n",
" return (a + b) / 2.0"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.93889390875399e-18\n",
"9.549014390504221e-18\n",
"9.313225746154785e-10\n"
]
}
],
"source": [
"left_boud_gs = []\n",
"right_bound_gs = []\n",
"approximation_gs = []\n",
"\n",
"cb_gs = lambda a, b: my_callback(a, b, left_boud_gs, right_bound_gs, approximation_gs)\n",
"x_gs = golden_search(f, a, b, epsilon, cb_gs)\n",
"\n",
"print(f(x_opt))\n",
"print(f(x_gs))\n",
"print(np.abs(x_opt - x_true))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Сравнение методов одномерной минимизации"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b8c9630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.semilogy(np.arange(1, len(approximation_bs) + 1), np.abs(x_true - np.array(approximation_bs, dtype=np.float64)), label=\"Binary search\")\n",
"plt.semilogy(np.arange(1, len(approximation_gs) + 1), np.abs(x_true - np.array(approximation_gs, dtype=np.float64)), label=\"Golden search\")\n",
"plt.xlabel(r\"Number of iteration, $k$\", fontsize=20)\n",
"plt.ylabel(\"Error rate upper bound\", fontsize=18)\n",
"plt.legend(loc=\"best\", fontsize=20)\n",
"plt.xticks(fontsize = 20)\n",
"_ = plt.yticks(fontsize = 20)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22.6 µs ± 1.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"84.1 µs ± 6.64 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"source": [
"%timeit binary_search(f, a, b, epsilon)\n",
"%timeit golden_search(f, a, b, epsilon)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Пример иного поведения методов\n",
"\n",
"$$\n",
"f(x) = \\sin(\\sin(\\sin(\\sqrt{x}))), \\; x \\in [2, 60]\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"f = lambda x: np.sin(np.sin(np.sin(np.sqrt(x))))\n",
"x_true = (3 * np.pi / 2)**2\n",
"a = 2\n",
"b = 60\n",
"epsilon = 1e-8\n",
"# plt.plot(np.linspace(a,b), f(np.linspace(a,b)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Сравнение скорости сходимости и времени работы методов"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Метод дихотомии"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.1968899233115735e-07\n"
]
}
],
"source": [
"left_boud_bs = []\n",
"right_bound_bs = []\n",
"approximation_bs = []\n",
"\n",
"callback_bs = lambda a, b: my_callback(a, b, \n",
" left_boud_bs, right_bound_bs, approximation_bs)\n",
"\n",
"x_opt = binary_search(f, a, b, epsilon, callback_bs)\n",
"print(np.abs(x_opt - x_true))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Метод золотого сечения"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.1968899233115735e-07\n"
]
}
],
"source": [
"left_boud_gs = []\n",
"right_bound_gs = []\n",
"approximation_gs = []\n",
"\n",
"cb_gs = lambda a, b: my_callback(a, b, left_boud_gs, right_bound_gs, approximation_gs)\n",
"x_gs = golden_search(f, a, b, epsilon, cb_gs)\n",
"\n",
"print(np.abs(x_opt - x_true))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Сходимость"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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VV5/VburUqfTv359atWoxYcIEJk6cyJEjR+jVqxfbthUeBt6wYQN33313wTMTEhIYMWIEv/32W9l+IypAyxW9xZhTGyoVOu0MJ8z76XMIOlDyMEK+OinQ7nJY9Bb0vBdqxXooaBGpMb4e4/09WeLbwUX/LNMt+/btIyMjg+Tk5LOuZWdnF/o6ICCAt99+m/Xr1zNt2jT69u0LwEUXXURaWhoPP/wwN954I/7+/mc9a+3atUycOJEnnniCMWPGADBw4ECOHj3K66+/Xqjt3XffTZ8+ffj8888Lzp1//vmkpKTw7LPP8sILLxSc37t3LzNmzKBZs2YAdOrUiYSEBD7++GMefPDBMv1elJd6DLypQRc4tBUO7yx0+pL2iQz0W0S2XxA06efas3r9yZmPMPdlDwQqIlI12GJ2rt29ezeBgYGFPtnZ2cyYMYOkpKSCpCDftddeS3p6OqtWrSryefPnzyc3N5crrrii0Pkrr7yy0Nfr169nw4YNXHPNNWRnZxd8wsLC6NatGzNmzCjUvlmzZgVJAUBcXBxxcXFs3VrGjfcqQD0G3pTcxTlumw9tLi043TQ2nFpBv/JrQEfODa7l2rNimzvPWPgm9Lgbwup4IGARqTHK+JO6r4iJiSEkJOSsf0hjYmJYuHAhAG+88Qb/+c9/ANi/fz8JCQlnPSc+Pr7gelHylzvWq1ev0Pkzv96zZw8AN954IzfeeONZzzmzZ6NOnbP/7g4ODiYjI6PIODxBiYE3xbeHgFBnOOG0xIC0FcTnpvHcsaHE7z9Ogzphrj2v9/2w8jOnGuKgJzwTs4iIDwsICKB37958//33ZGVlFcwzCAgIIDU1FYApU6YUtK9Tpw5r16496zm7dzuTuevWrVvke/KTibS0NFJSUgrOp6WlFWqXf/9TTz3FgAEDznqOK/MgKpuGErzJPxCSOp9V6Ig1U7EYfsrpxJRlZSjCUa8NdLreGU5Y9rF7YxURqSIeeOAB9u7dy1/+8pdS2/bp04ft27cze/bsQucnTpxIXFwcrVq1KvK+Ll264Ofnx8cfF/679sMPPyz0dYsWLWjUqBErV64kNTX1rE/79kUUr/My9Rh4W3IXmPWCU8woKNw5t2YqpkEXGmQ25MulO7m1bxPXnzf4Gdi3ET6/HaLqQ8MSljqKiFRD/fv355///Cdjxoxh2bJlXH/99TRu3JiMjAzWrVvHhx9+SHh4OMYYRo8ezYsvvshll13GE088Qf369Xn//ff5/vvvGTduXJETD4GCFQh/+9vfyM3N5dxzz+X777/nq6++KtTOGMMrr7zCsGHDyMrK4oorriAmJoa0tDTmzJlDcnIy9913X2X8trhMPQbe1qAr2BzY8Yvz9cGtsHsZtLyYIe0TWbXrML/tOer68wKCYeR7znLID6+BfRs8E7eIiA974IEHmDlzJnXr1uXBBx9kwIABjBgxgnfeeYeRI0eyfv16/P39CQ8PZ/r06QwcOJAxY8YwbNgwli5dynvvvcfNN99c4jvGjRvHjTfeyDPPPMOll17KmjVrmDhx4lntBg8ezIwZMzh27Bg33XQTgwYN4oEHHmD37t1069bNU78F5WaKm8FZnaWmptpFixZ5OwzH8f3wr8bQ72Ho/WeY9zp88xe48xfSApPo+tSP3N2/GfcMaF625+7bAG8OgNDacNMPmowoIkVavXp1sd3lUrWU9mdpjFlsrU0t7TnqMfC2sDoQ2/LUTotrpjhf121CvcgQujSuw5dLdxa7BKdYdZvAVR/AoW1Oz0F2pvtjFxGRakeJgS9o0MVZsnhsH2yZU6io0dAOSWxIP8bqXUfK/tzkrjD8Ndg6B764E2pg75CIiJSNEgNf0KALZByCOS858w1OSwwubBtPgJ85teNiWbUbAec/DMs+gulF1w0XERHJp8TAFyR3dY7zX3f2T0g4p+BSnfAgejaLKd9wQr7e90OHq+Hnp2DpR24IWEREqislBr6gTgqExTgljVsMBr/CfyxD2iey/cAJlmw7WL7nGwNDXoRGveCLO2Dz7NLvERGRGkmJgS/I31AJitw06YI29QgK8OPLpWUodnSmgKC8ZYwN4aNrYPmncCSt9PtEpNqriavTqht3/hkqMfAVrYdBXGvnp/ozRIYEcn6LWKYs20lObgX+8ENrwzUfO2WY/3cjPNsc/t0ZvrjLqZR4qOh9xEWk+goMDOTEiRPeDkMq6MSJEwQGBrrlWap86Cs6jHQ+xRjSIZFvV6axYNN+ujUpuna3S+qkwD3LYNdS2DLbGVZYORl+ece5Ht0QGvWExn2crZz9lDuKVGdxcXHs2LGDpKQkQkNDMcZ4OyQpA2stJ06cYMeOHWdt4FReSgyqiH4t4wgL8uflaetZufNQiW3Pa1yH9vWji2/gHwj1U51Pj7shNwfSVjhJwpbZsPZrWPI+HNsD3e9083ciIr4kMjISgJ07d3Ly5EkvRyPlERgYSL169Qr+LCtKlQ+rkL9+tpwPFpS+J7cxcH3Xhtw/qAURIeXoWsrNhYmXw/aFcNcSVU0UEakGXK18qMSgCrHWcjQzu8Q2GSdzeWXab7wzdzP1IkL4+7A2DGwTX/aXpa2E13pA9ztg4OPlC1hERHyGSiJXQ8YYIkICS/zERgQzdmgbPru1O9Fhgdz83mL++N5idh/KKNvL6rWBjtfA/HFwYItnviEREfE5SgyqqXOSa/PlnT154MIWTFu7hwuem857czeTW5ZVDec/CMYPflKPgYhITaHEoBoL9Pfjtr5N+e7e3nRoEM0jn69kxOtzWJfm4r4LUUnQ9TZY/jHsXOLZYEVExCcoMagBGtYN570bz+O5Kzqwae8xBr84kx9WuVjcqOc9EFYXvn9EmzCJiNQAVTYxMMb0NsZ8YYzZYYyxxpjR3o7JlxljuKxTfX78U1+S64Tx75/Wu3ZjSBT0+QtsmgG//eDZIEVExOuqbGIA1AJWAHcDKtvlojrhQVzfrSFLtx9i+faS6yEU6HwD1G4M3//NqXkgIiLVVpVNDKy1X1lrH7TWfgrkejuequSyzvUJDfRnwjwXVxsEBMGAR2HPKlj6gWeDExERr3I5MTDGjDDG/NsYM9MYcziv+35CKffUN8a8ZYzZaYzJNMZsNsa8YIypXfHQpbwiQwIZ1jGRz5fu4NAJFyudtR4OSanOCoWs46W3zzgMXz0An91csWBFRKRSlaXH4GHgDqAjsKO0xsaYJsBi4AZgAfA8sBGn63+uMaYCBf+loq7t2pCMk7l89ouLGycZAwP/AUd2wbxXS2675it4pQssGAfLPoLj+ysesIiIVIqyJAb3As2BSOBWF9q/CsQBd1lrh1trx1hr++EkCC2AJ05vbIx5PK8XoqRP3zLEKyVomxRFhwbRvD9/q+vbdTbsDi0uhlkvwLG9Z18/kgYfj4IPr3J2cuz3iHM+bYX7AhcREY9yOTGw1k6z1q63LvwrYoxJAQYCm4FXzrj8KHAMuM4YE37a+ReAVqV8Frgar5Tu2i7J/LbnKPM2luEn+gFj4eRxmP70qXPWwi/vwivnOhsw9XsEbpkOna53ru9WYiAiUlV4avJhv7zjd9baQhMDrbVHgNlAGND1tPN7rbVrSvm4MLgtrhrSIZGo0EAmzC9DyePY5tB5FCx6C/ZtcD7vDIEv7oR6beHW2dD7fmcHx1pxUKueegxERKoQTyUGLfKO64q5nr+Ivnl5X2CMqWWM6WiM6YjzfSTnfZ1c3mfWNCGB/ozoXJ9vV+xmz5Ey7KXQZwz4B8OH18Br3WHXMhjyIoyaAjHNCret1xZ2L3Nv4CIi4jGeSgyi8o7FLZTPPx9dgXekAr/mfUKBx/J+/feiGhtjbjbGLDLGLEpPT6/Aa6uXa7okk51r+XjhNtdviqjnVERMXw3NBsIdC6DzaPAr4j+n+HaQvhays9wWs4iIeI636hiYvGO5a+xaa3+21poiPqOLaf+GtTbVWpsaGxtb3tdWOymxtejRtC4fLNhGTlk2WOp1P9y+AEa+BxElbOsc3w5ysmBvcZ1HLtgwDfZvKv/9IiLiMk8lBvk9AlHFXI88o5140bVdGrLj4Ammrdnj+k1+fhDbovR29do6x/LOM8g5CR9cBR9fD7mqYyUi4mmeSgzW5h2Lm0OQPxBdgR8jxV0GtK5HXERw2SYhuqpuUwgIgd3Ly3d/2grIPuHMU1j2oXtjExGRs3gqMZiWdxxojCn0DmNMBNADZ3+DeR56v5RBoL8fV56XzPR16Wzb7+aFH/4BENeq/InBtoXOsU4T+PHvkHXMfbGJiMhZPJIYWGs3AN8BjYDbz7j8GBAOvGut1d/yPuKq8xrgZwzvz9/q/ofXa+v85F+ebZu3L4Ra8TD8Vafq4pyX3R+fiIgUKMteCcONMeONMeOBMXmnu+WfM8Y8c8YttwF7gJeMMZONMU8ZY37CqaC4DnjIDfGLmyREhdK/ZRwfL9pGZrabd1CMbw/H98GR3WW/d/tCqJ8KyV2h9TCY/QIc3uXe+EREpEBZegw6AqPyPoPyzqWcdm7E6Y3zeg1SgfFAF+BPQBPgJaCbtXZfRQIX97u2a0P2H8vimxXl+Ae8JPF5ExDLOpxwbC8c2AQNznO+HjDWmYw47XF3RiciIqcpS0nkscUsD8z/NCrinm3W2hustQnW2iBrbUNr7d3WWu2q44N6No2hYd0w17djdlW9Ns4xrYyJwfZFzrH+uc6xTgp0uQV+fb/8cxZERKRE3qpjID7Iz89wTZdkFm4+wJrdh9334JAoiG5Y9n/Mty8E4w8JHU+d630/hEbDtw+Vb86CiIiUSImBFHJ55wYEBfjx/jw3T0KMb1f2zZS2L3SGIYLCTp0Lre2UZN40HdZ/594YRUSEAG8HIL6ldngQl7RLYNKvO+jQILqgRGVREqND6dakrmsPjm8Ha6Y6yw2Dwktvn5sDO36BDiPPvnbujbDwP/Ddw9Ckn7Nhk4iIuIUSAznLqO6NmLRkB/d/srTUtm/fcC7nt4gr/aH12gIW9qx2VhmUJn0tZB05Nb/gdP6BcMHf4cOrYfF4OO8PpT9PRERcosRAztKhQTQLHhzAiazily3mWsuotxfw9Ndr6N0sFn+/kvoWKLwywZXEYPsC51hUYgDQYjA06gU/PwXtr3DmMYiISIVpjoEUKTYimOS6YcV+GsWEc//AFqzZfYTPftle+gOjG0JwpOsTELcvdOYT1Ekp+roxMPBxOL4fZj7r+jcmIiIlUmIg5XZxuwQ61I/iue/XkXGylKJIxpyqgOiK7Yuc3gJTQk9EYkfocBXMew0ObHY5bhERKZ4SAyk3Pz/DmItasetQBuPnbC79hvi2kLay9F0STxyE9DXFDyOcrv8jzpLGHx5zKWYRESmZEgOpkG5N6nJ+i1hemfYbB45lldw4vh1kHXWqGZZk5y/O0ZXEIDIRetwFKz87VRBJRETKTYmBVNhfLmrJ0cxsXpn2W8kN6+VNQCxtOGH7IsBAUifXAuh+lzN/YdFbrrUXEZFiKTGQCmsZH8mITvV5d+6WkrdtjmsFxq/0QkfbF0JsS9dXGgTXgtZDYdUXkOXmbaNFRGoYJQbiFvcNbI4x8Nz364pvFBgKMc1LXplg7akdFcui/Uin7sG6r8t2n4iIFKLEQNwiISqUG3o0ZvKSHazYcaj4hqWtTNi/EU4ccG1+weka9oTIJFj2cdnuExGRQpQYiNvc2rcJUaGBPP3NmuIbxbeDQ9uc+gNF2VZKYaPi+PlBuxHw2w/Ods0iIlIuSgzEbaJCA7nj/KbMXL+XmevTi26UXwExbWXR17cvhKAIiG1R9gDaj4TcbFjxWdnvFRERQImBuNl13RpSv3Yo//x6Dbm5RWyLXK+dcyxuOGH7Qmc1gp9/2V9er40zVLHso7LfKyIigBIDcbPgAH/uH9iClTsP88XSnWc3iKgH4XFFT0DMOub0JJR1GOF07UfCjkWwb0P5nyEiUoMpMRC3G9ohkTaJkfzft2uLLpUc37boxGDnErA5FUsM2o0ATPkmIW74CSZeCdmlFGoSEanGlBiI2/n5Gf56USt2HDzB69M3sDH9aKHPwcgW2D1r2Lj7ABvTj55KHrYvdI4VSQwiE6Fxb2c4wRYxlFGckxnw5T3OcseNP5f//SIiVZxhuj/sAAAgAElEQVS2XRaP6Nkshl7NYnjhh/W88MP6QteG+vnxUlAWt774IWttMv1bxvHf0ec6iUGdFAivW7GXtx8Jn9/mVFBs4GKSsWAcHNwC/kGwchI0H1ixGEREqiglBuIxr13bmZ/W7MGe8ZN7xOFw+OkVnuwOT++ow7o9R04VNkrpW/EXtxoCU+9zeg1cSQyOpsOMZ6DZIAiPgdVTIDsTAoIrHouISBWjxEA8plZwAEM7JJ59IaceTA+mc9B2OjU8n//OOkDuwW34HU2r2DBCvpBIaDEYVvwPLnwK/ANLbv/zU87Ex4H/gIPbYMn7znyDFhdVPBYRkSpGcwyk8vkHOPsmpK0gMTqEkzmWI7/Nca6VtRRycdqPhBP7nYJHJdmzGha/Defe6NROSOkDobVVC0FEaiwlBuId8W1h9woSIkMAyNqyAAJCTu3AWFFN+0NY3dJrGnz7kFNQqc8Y52v/QGcoYu1XcPKEe2IREalClBiId8S3h+N7aRDo7KsQuHMxJJ5Tere/q/wDoe3vYO3XkFHM3g3rf4ANP0KfBwpPeGxzKWQdLb23QUSkGlJiIN6R1zOQmLGBIE4ScWCl+4YR8rUfCdkZsPrLs6/lZMN3D0HtxnDeHwpfa9Tb6W1YOcm98YiIVAFKDMQ76rUBIOLgajoEbMXfnnTPxMPTJXV2lj8WNZzwy3hIX+NMODxz9YF/ALQaCmu/gazj7o1JRMTHKTEQ7wiNhuhkTNoKeodtds65OzEwxuk12DQTDu04df7EQZj2pLNVc8tLir63zaVw8his/869MYmI+DglBuI99dpB2go6+f9Gul+sU7XQ3dpdDlhY8empczOfdbZ9HvSEkzwUpVFPCI/VcIKI1DhKDMR74tvCvt9oe3IFy2jmmXfUbeL0RCzNG07Yvwnmvw4dr4bEjsXf5+cPrYfBum8h86hnYhMR8UFKDMR74tuBzSUqex/zslLIKWqbZndoPxL2rITdK+D7v4FfAPR7pPT72lwG2Sdg/beeiUtExAcpMRDvOa1mweKcpuw9mumZ97S5zEkGvhkDq7+AHvdAZELp9yV3hVrxKnYkIjWKEgPxnuiGEBRBrl8gK20jdh70UEGh8LrQ9ALYPBMiEqH7Ha7d5+cPbYbD+u8h84jr7ztxoGztRUR8iBID8R4/P0jqREZsRzIJYtehDM+9q+NVznHAoxAU7vp9bS6FnEynUJIrDu2AV7vBpD+WPUYRER+gxEC863f/5eTv3gbwXI8BOHUJbpnhzDcoi/rnQWSSa6sTso7BB1fCkV3OEsncnPLFKiLiRUoMxLtqxRIZW5/QQH92e7LHwBhI6FD88sTi+PlB6+FOeeQTB4tvl5sLk26BtBXQ7grIPAR7VlUsZhERL1BiIF5njCEhKsSzQwkV0fYyyMkqeTjhp384pZcHPQn9HnLObZ1XOfGJiLiREgPxCQnRIew85KO7GSZ1hqjk4ocTlkyEWc9B5xugyx+dSZURibBlTuXGKSLiBkoMxCckRIWy66CP9hgY46xO2PCTs+LgdFvmwhd3QeM+MPj/nLbGQMNusHUuWA/VZhAR8RAlBuITEqNC2HMkg+ycXG+HUrQ2l0LuSVgz9dS5/Zvgo2ugdkO44p3CW0Ynd3MmIR7YXOmhiohUhBID8QnxUaHkWthzxENFjioq8Ryo3ehUsaOMQ84KhNwcuPpjCK1duH3D7s5R8wxEpIpRYiA+ISE6BIBdvjrPwBin12Djz3B0D3z6e9j3G4x8z9mP4UyxrSAkCrZqnoGIVC1KDMQnJEaFArDTV+cZgFNa2ebAO0Oc5YsXPweNexfd1s8PGnR15iCIiFQhSgzEJ+T3GHi0lkFFxbeDOk0gfQ10uwM6jyq5fcNusG89HE0v+7uyjsEXd2qOgohUOiUG4hMiggMID/L33SWL4Awn9P+bkxRc8PfS2yfnzzMoR6/Bmqnwy7sw7cmy3ysiUgFKDMQnGGNIiPbhJYv52gyHQU84GyyVJrEjBISUbwLiysnOcfmn6jUQkUqlxEB8hlP90Id7DMoqINgpjlTWCYgZh505DK2HOQnI7Jc8E5+ISBGUGIjPSIwK9d2yyOWV3A12LYPMo67fs/47Z0fHLrdCh6vg1wlwJM1zMYqInEaJgfiM+KgQ0o9mkpXto0WOyqNhN2clw/YFrt+zchLUiocGXaDH3U5hpXmvei5GEZHTKDEQn5EYHYK1kHa4GvUa1D8PjJ/ryxYzj+YNIwx1ljzWbeLUT1j435J3dxQRcZMqmxgYY243xiwzxhzO+8w1xlzs7bik/BLyahlUq+GEkEhnmaOrKxPWfQPZGc5Wz/l63gtZR2DhfzwTo4jIaapsYgBsB/4CdAJSgZ+AycaY9l6NSsot0derH5ZXcnfYvgiys0pvu+pzqFUPkrueOhffDpoNhHmvQdZxz8UpIkIVTgystZ9ba7+21v5mrV1nrX0IOAJ083ZsUj7x1bHHAJx/5LNPwK6lJbfLOgbrv4dWQ89eDtnrT3B8n1PbQETEg1xODIwxI4wx/zbGzMzrurfGmAml3FPfGPOWMWanMSbTGLPZGPOCMaZ2SfeVlTHG3xhzJVALUHH6KqpWcAARIQHsOljNegwKNlQq5T/Ndd86CUTrYWdfS+7q9DzM+bdrPQ8iIuVUlh6Dh4E7gI7AjtIaG2OaAIuBG4AFwPPARuBuYK4xpm6Zoz37He2MMUeBTOB14FJr7fKKPle8JzEqlJ3VrcegVpxTSrm0CYirJkN43KlE4ky97oPD22H5x+6PUUQkT1kSg3uB5kAkcKsL7V8F4oC7rLXDrbVjrLX9cBKEFsATpzc2xjye1wtR0qfvGe9Yi5OodAVeA94xxrQtw/ckPiYhOsS390sor4bdYNs8yC1mKWbBMMKQ4qsqNh3gzDeY9YKz3bOIiAe4nBhYa6dZa9dba21pbY0xKcBAYDPwyhmXHwWOAdcZY8JPO/8C0KqUT6HF4NbarLw5BoustX8FluAkMFJFVbvqh/mSu8OJA7B3bdHX138PJ487JZeLYwz0vM/ZmGnNFM/EKSI1XoCHntsv7/idtbbQj0jW2iPGmNk4iUNX4Me883uBvRV8rx8QXMFniBclRIWy92gWmdk5BAe4sB9BVZG/ymDLHIhrdfb1VZMhLObUxkvFaT3MGZaY+awzSdEY98cqIjWap1YltMg7rivm+vq8Y/PyvsAY809jTC9jTKO8uQZPAX2B94tpf7MxZpExZlF6ejm2wZVKkRBVBbZfLo86Kc4yxKLqGWQdh3XfOcMI/qXk6n7+0PMeZ4XDhp88E6uI1GieSgyi8o6Hirmefz66Au+IBybgzDP4ETgXuMha+3VRja21b1hrU621qbGxsRV4rXhSYnQ1XbJojLNvQlETEH/7AU4eK3kY4XTtr4SIRJj1vHtjFBHBe3UM8vs/S52vUBxr7WhrbUNrbbC1Ns5aO8Ba+62b4hMviY+qpkWOwFltcHg7HNxW+PyqyRBWFxr2dO05AUHQ/Q7YPBO2lWEPBhERF3gqMcjvEYgq5nrkGe1EAGe5IsDOg9WsxwCcHgMoPJxw8gSs/QZaXlL6MMLpOo2C0Now8zn3xigiNZ6nEoP8qdfFzSFolncsbg6C1FChQf5EhwVWzx6Dem0gONKZgJjvtx/LNoyQL7iWsy3zuq/h1yKn1YiIlIunEoNpeceBxphC7zDGRAA9gBPAPA+9X6qwhKjQ6jf5EJyJgw3OK9xjsGoyhNaBRr3L/rzud0LK+fD5bTD7RffFKSI1mkcSA2vtBuA7oBFw+xmXHwPCgXettcc88X6p2hKjQqrnUAI4wwnpa+D4fjiZ4QwjtCrjMEK+oDC4+mNocxl8/zf49qHiCyiJiLjI5b+NjDHDgfz+zvi8YzdjzPi8X++11t5/2i234exb8JIxpj+wGugCnI8zhPBQBeKWaiw+KoRfth7wdhieUbBvwjzAOtspF7U3gqsCguB3/4XwGJj7MhzbC8NeBv9At4QrIjVPWX5M6QiMOuNcSt4HYAtQkBhYazcYY1KBvwMXAoOBXcBLwGPW2v3lDVqqt8ToUA4cP8mJrBxCg6pRkSOAxE7gH+RsqHR0jzOBsHGfij3Tzw8u+pezz8K0x+HEfrh8PASFl3qriMiZXE4MrLVjgbFlebi1dhvOJkoiLisocnQ4g8Yx1ewft8AQSOoMG3+GA1ug9VD3/HRvDPT5s9NzMPU+eHeYM8wQVqfizxaRGsVbdQxEipWQt2Sx2m2/nC+5K+xeDpmHofWl7n126g1w+Tuwaxm8fREcKnUjVBGRQpQYiM/J7zGodtsv58vfDyEkChqXYzVCaVoPhWv/B4d3wn8HQnoxGzeJiBRBiYH4nILqh9W1x6DBeWD8naJGAUGeeUfjXjB6KuRkwZsXwKK3tGJBRFyixEB8TkigP3XDg9h1uJr2GIRGw3WfwYCxnn1PQnu46XtI7ABT7oW3BkHaKs++U0SqPCUG4pMSokOqb48BQEpfqBXn+ffUbgTXfwHDX4d9v8G4XvDDWGdHRxGRIigxEJ8UHxla/XZY9BZjoONVcMciZ2fGWc/Da92cXR1FRM6gxEB8UmJ0CDurc4+BN4TXheGvwKgp4BcAE34Hn94IR9K8HZmI+BAlBuKTEqJCOZyRzbHMbG+HUv007gW3zoG+f4XVX8Ar58Kyj70dlYj4CCUG4pMSo/NWJmg4wTMCgqHvGCdBiGsNn/0Bln/q7ahExAcoMRCfFB+ZnxhoOMGjYprBdZOgYQ+YdIvmHYiIEgPxTYnR+dUP1WPgcYGhcNUHENsKProeti/ydkQi4kVKDMQn1YsMwRgNJVSakCinWmKtWHj/clVLFKnBlBiITwoK8COmVrCGEipTRD1nWMEvAN67DA5t93ZEIuIFSgzEZyVEhVTf/RJ8VZ0UuPZTZ4On9y6D49odXaSmUWIgPishqppXP/RVCR3gyolwYLMzrJB1zNsRiUglUmIgPishKpTd6jHwjsa9YMR/Yecv8NF1kJ3l7YhEpJIoMRCflRgdwpHMbI5knPR2KDVTqyFwyQuw4Uf4/DbtzihSQwR4OwCR4sRH5S1ZPJRBREigl6OpoTqPguN74ce/w5Hd0PFqZ7vokEhvRyYiHqIeA/FZiVFOkSPtmeBlPe+DQU/CwS0w+Vb4v6bO8MKqz+GkhnpEqhv1GIjPSsgrcqR5Bl5mDHS7HbreBtsXOqWTV37m7LMQHOn0ILQbAY37gL/+ShGp6vR/sfisuIhg/AxasugrjIEG5zmfQU/C5hlOkrD6S1g6EcJj4bL/QJPzvR2piFSAhhLEZwX6+xEbEawli77IPwCa9IPhr8L962HkBKd64ud3QOZRb0cnIhWgxEB8WkJUqMoi+7rAEGcFw7BX4fB2+Pkpb0ckIhWgxEB8WmJ0CDtVFrlqSO4CnW+Aea/BrmXejkZEykmJgfi0/CJH1lpvhyKuGPAohNWBKfdAbo63oxGRclBiID4tISqE41k5HD6R7e1QxBWhtWHQU7BjMSx6y9vRiEg5KDEQn5aQV+RIwwlVSLsRkNLXKYp0eJe3oxGRMlJiID4tIdopcqTtl6sQY+Di5yA7E779q7ejEZEyUmIgPi3xtLLIUoXUbQK974eVk2D9996ORkTKQAWOxKfFRgTj72f4YMFWlm075O1w3MbPDy5pn0iPpjHeDsVzetwNyz+BqffBbfMhKMzbEYmIC5QYiE/z9zNc2DaexZsPMP1IurfDcZtjWdl8sGAbN/ZszJ8HtSAk0N/bIblfQDBc8jyMvxhm/AsGjPV2RCLiAiUG4vNeubqTt0NwuxNZOTz51Wr+O2sTs3/by0tXnUPzehHeDsv9GvWEjtfAnH9DuyugXuui21kLu5fBso/hwGb43X+dwkkiUuk0x0DEC0KD/PnH8Lb8d1Qq6UcyGfLvWYyfval61mu44B/OZktT7oXc3MLXDm2Hmc/Bq91gXG+Y+wqsmQJbZnknVhFRYiDiTf1b1eObe3rTrUldxn65ihvGLyT9SKa3w3Kv8Low8B+wbR78+h5kHIJf3oPxl8DzbeHHx5x9Fi5+Du5bBf7B8NtP3o5apMYy1fInlFKkpqbaRYsWeTsMkQLWWt6du4Unv1pNreAA/jWiPf1b1fN2WO5jrTPXYOevYHMhOwPqNIEOV0K7y6FO41Nt37sUDu+E2+d7L16RasgYs9ham1paO/UYiPgAYwyjujfiyzt7EhsRzI3vLOLhycvJys4t/eaqwBgY8iLENINO18NNP8Gdi6HPA4WTAoAm/SF9jTPMICKVTomBiA9pXi+Cz+/owU09GzNh3lbenr3J2yG5T0wzuGUGDP4/qN/ZSRaK0rS/c/ztx8qLTUQKKDEQ8THBAf48fElrujepy9uzN1efXgNXxbaEiETYoMRAxBuUGIj4qD/0TmH34QymLNvp7VAqlzFOr8HGnyFHm2eJVDYlBiI+qm/zWJrF1eKNGRur5zLGkjTt76xe2LG4fPcv/xRW/M+9MYnUEEoMRHyUMYY/9E5hze4jzFy/19vhVK6UvmD8yjeccPIETLkPvrwHMo+4OzKRak+JgYgPG9YxkbiIYP4zc6O3Q6lcobUhqXP5JiCumQqZhyDzMPw6wf2xiVRzSgxEfFhwgD+jezRi5vq9rNp52NvhVK6mA5yhhOP7y3bfkokQWR/qnwfzXoPcHM/EJ1JNKTEQ8XHXnNeQsCB/3qxpvQZN+gMWNk5z/Z7DO532Ha+C7nfAwS1OD4KIuEyJgYiPiwoLZOS5Dfhi6U52HTrh7XAqT1InCIkuW3nkZR85lRU7XAUtL4HoZJj3qudiFKmGlBiIVAG/79EYC4yfvdnboVQeP39nEuKGH52SyqWx1hlGSO4GdZs493e5FbbOLf/qBpEaSImBSBXQoE4Yg9slMHH+Vo5knPR2OJWnaX84sgv2rCq97Y7FsHcddLz61LlzroWgCJirXgMRVykxEKki/tCrMUcys/lwwTZvh1J5mpShPPKS9yEgFFoPP3UuJBI6j4JVk7X3goiLlBiIVBHt60fTNaUOb83exMmcGlImOSoJYluVXs/gZAYs/x+0HuokA6c772Zn3sGCNzwXp0g1UmUTA2PMWGOMPeOz29txiXjSzb1T2HUog6nLdnk7lMrTtD9smQNZx4pvszavdsHpwwj5ajeEVkNh8XjIPOqxMEWqiyqbGORZCySc9mnn3XBEPKtv87iaVya5ST/IyYLNs4tvk1+7oFHvoq93u90psbxkomdiFKlGqnpikG2t3X3aJ93bAYl4kp+f4Q+9Uli16zBzNuzzdjiVo2EPZ+5AccMJh3fBhp+c2gV+xfyV1uA8SEqF+Sp4JFIalxMDY8wIY8y/jTEzjTGH87ruS6w3aoypb4x5yxiz0xiTaYzZbIx5wRhTu+KhA5BijNlhjNlkjPnQGJPipueK+Kxh5yQSUyuYN2bUkIJHgSHQqEfxExBPr11Qkm63w/6NsO4b98coUo2UpcfgYeAOoCOwo7TGxpgmwGLgBmAB8DywEbgbmGuMqVvmaAubD4wGLgL+AMQDc9zwXBGfFhzgzw09GjF9XTprd9eQTYKa9Id96+HAlsLnz6xdUJJWQyGqgZYuipSiLInBvUBzIBK41YX2rwJxwF3W2uHW2jHW2n44CUIL4InTGxtjHi9iMuGZn7757a21X1trP7bWLrPW/gBckvf9jCrD9yRSJV3TJZmwIP+a02vQNG/Z4pnDCTt+gb1rS+8tAPAPgC63wJZZsHOJ+2MUqSYCXG1orS0oWG6MKbFtXpf+QGAz8MoZlx8FbgauM8b8yVqbP9X4BaC0rdC2lhDfUWPMSqBZKc8QqfKiw4K4IrUB787dzPR1JU+tSYkN54M/dMXfr+T/b31aTHPnp/3ffoTU3586n1+7oM3w4u89Xafr4ed/OmWSL9PyRZGiuJwYlFG/vON31tpCC66ttUeMMbNxEoeuwI955/cC5d503hgTArQEyrDjikjVdfv5TQHIKqGmwd4jmXy3Ko25G/bRs1lMZYXmfsY4qxNWToKck+Af6NQuWPEptBoCIVGuPSckCs65Dhb+BwaMhchET0YtUiV5KjFokXdcV8z19TiJQXPyEoOyMsY8A3yJ04sQBzwChAPvlOd5IlVNbEQwY4e2KbFNxskczn3iBz77dXvVTgzAGU745R3YvhAadoe1XzlLEIuqXVCSLrfA/NdhwX9gwKOeiVWkCvPUcsX89P1QMdfzz0dX4B31gQ9wahl8BmQCXa21W4pqbIy52RizyBizKD1dqxqlZggJ9Ofidgl8u2I3x7OyvR1OxTTuA8b/1OqE/NoFjYupXVCcOo2h1SWw+O2SiyaJ1FDeqmOQP9hZ7got1torrbWJ1toga22StfZ31tpid1qx1r5hrU211qbGxsaW97UiVc7wc5I4lpXD96vSvB1KxYRGQ/1znQmIR3Y7xw5XOrsollXX2+HEAfjqgbNXOojUcJ5KDPJ7BIob+Is8o52IeMh5jeqQFB3KZ7+UusrY9zXt76womPuKU7ugrMMI+ZK7QufRsHQivNgB3r8C1n2n4kcieC4xWJt3bF7M9fyVA8XNQRARN/HzMww/J5GZ69NJP5Lp7XAqpkl/wDqJQYOupdcuKI4xMORFuGc59P4z7FoCEy+Hl86BWc/DsXLPgxap8jyVGOSvDBhojCn0DmNMBNADOAHM89D7ReQ0l56TRK6FL5bu9HYoFZPYEUJrg80pf2/B6aLqQ7+H4N6VcPl4iE6GH8bCc63gs5th24KKv0OkivFIYmCt3QB8BzQCbj/j8mM4qwfePa2GgYh4UNO4CNolRTHp1+3eDqVi/PydXoOy1C5whX8gtLkURk+B2+ZD5xtg7dfw3wtg3uvue49IFWBc3aHNGDMcyP8/MR4YhFPieGbeub3W2vtPa98EmIOzlPBzYDXQBTgfZwihu7XWK7vApKam2kWLFnnj1SJe89asTfx9yiq+v7c3zepFeDuc8juSBod3QFInz74n8yh8+nvYNB3+OAtiVDtNqjZjzGJrbWpp7crSY9ARp9zwKJykACDltHMjTm+c12uQCozHSQj+BDQBXgK6eSspEKmphnRIxN/PMOnXKj4JMaKe55MCgOBaMPTfEBgKk2/VxESpMVxODKy1Y621poRPoyLu2WatvcFam5C3rLChtfZua+1+t34XIlKq2IhgejWL4fMlO8nNLfdK4Zoloh4MfsYpqjTn396ORqRSeKuOgYh4waXnJLHj4AkWbFZu7rK2v3N2Zpz2BOxZ7e1oRDxOiYFIDTKwdTzhQf5Mqg41DSqLMXDxcxAcAZP+6OzVIFKNKTEQqUFCg/y5sG0CXy3fRcZJjZm7rFYsXPK8U+9g1gvejkbEo5QYiNQwl3VK4khmNj+u3uPtUKqW1sOcYYXpT8Pu5d6ORsRjlBiI1DBdU+pSLzK46tc08IbBzzgFlibdCtlZ3o5GxCOUGIjUMP5+huEdk/h5bTr7j+kftzIJq+OUUk5bDjOf8XY0Ih6hxECkBhp+ThLZuZYpy6p4iWRvaDkYOlwFM56Bnb96OxoRt1NiIFIDtUqIpGV8RPXYcdEbLnwKasXlDSkUsTFV1jHYOh/mvwGTb4PPb4fc3MqPU6QcArwdgIh4x2WdknjyqzVs2nuMxjHh3g6nagmt7VRFfH8E/Ph3aDHYWbGwa6nz2bvO2RYaIKgWZB2FjtdCw27ejVvEBeoxEKmhhnZIwhiqfolkb2l2AZxzHcx9GcYPhm8fhE0zoHZj6P0AXPkB3LsK/rQWAkJg5SRvRyziEvUYiNRQ8VEh9GgSw+Rfd3DvgGYYY7wdUtVz4T8hvj3UaewcI+oV3a7ZQFj1uTME4edfuTGKlJF6DERqsEvPSWLr/uP8svWAt0OpmoJrQZebnd6D4pICcLZ0Probts6rvNhEykk9BiI12KC28Tw0eTn//HoN5zaqU2LbC1rX45zk2pUUWTXTfBAEhMLKz6BRD29HI1IiJQYiNVit4ACuOi+ZCfO2sGTbwWLb5eRa3py1idev7US/liX8ZCxFCwp3koNVn8NF/9Jwgvg0Y23N2341NTXVLlq0yNthiFQZ+49lMeqtBazedZgXruzIJe0TvR1S1bNyMnwyCkZ9CY17ezsaqYGMMYuttamltdMcAxEpVZ3wIN7/QxfOSY7mrg9+5eOF27wdUtXTbCAEhpV/dUJOtmohSKVQYiAiLokMCeTd33ehR9MYHvjfMt6evcnbIVUtQWHQ/EJY9YXzj3xZ5ObAm/3hizs9E5vIaZQYiIjLQoP8eXNUKoPa1OOxL1fx8k/rqYnDkeXW5lI4vhe2zCrbfSsnOQWUln8CJ4qfCyLiDkoMRKRMggP8eeXqTlx2ThLPfLeOf36zRsmBq5pdAIHhZRtOyM2B6f+CsBjIyYRVkz0XnwhKDESkHAL8/Xjm8g5c0yWZcdM38sjnK8jNVXJQqsBQaHFR2YYTVk6CvWth8L8gpgUs+cCzMUqNp8RARMrFz8/w+PC23NI7hQnztnL/J0vJztHkuFK1uRRO7IfNM0pvm5sD05+G2FbQ+lLocCVsmwf7N3o+TqmxlBiISLkZYxhzUUvuH9icz37dwTPfrfN2SL6v6QBnYyVXhhNWTnI2ZOr7F/Dzg/YjAQNLP/J4mFJzKTEQkQoxxnBHv2Zc3rk+/5m5kdW7Dns7JN8WGOLsxrj6S8g5WXy703sLWg1zzkUlQUofWPoBaF6HeIgSAxFxiwcHtyIqNJC/frZc8w1K0+ZSOHEANk0vvs2Kzwr3FuTrcBUc3AJb53o+TqmRlBiIiFvUDg/ikUtasWTbQd6fv8Xb4fi2Jv0gOLL44YT83oK41qd6C/K1vMRZ2bBUkxDFM5QYiIjbDO+YRI+mdfnXN2tJO5zh7XB8V8FwwhTIzjr7+orPYN966HNGbwE4Ozq2HuaUWD55onLilRpFiYGIuI0xhieGtyMzJ5fHvlsOCAgAABgHSURBVFzp7XB8W5tLIePg2cMJBb0FbaDV0KLv7XAlZB6GtV95Pk6pcZQYiIhbNYoJ565+Tflq+W5+XJ3m7XB8V5PzITjq7OGEFf9zegvOnFtwuka9ILK+ahqIRygxEBG3u7l3E5rF1eJvn6/kWGYZ9wWoKQKCoeXFhYcT8nsL6rWFlkOKv9fPD9pfARt+hCNKvsS9lBiIiNsFBfjx5GXt2HHwBC/8oNoGxWpzKWQego3TnK+Xfwr7fit6bsGZOlwFNtfZP0HEjZQYiIhHnNuoDledl8xbszezYschb4fjm1L6QkjecEJONsz4V15vwSWl3xvbHJI6w9IPPR2l1DBKDETEY8Zc2JLaYUE8OGk5OaptcLaAIGfIYM1UWDrR6S3oO6b03oJ8Ha6CtOWwe7ln45QaRYmBiHhMVFggfxvSmmXbD/Hu3M3eDsc3tbnUWWHw1QNQrx20uLgM914GfoHqNRC3UmIgIh41pH0CfZrH8sy3a9l1SOvuz5LSB0KiIftE2XoLAMLrQvNBsOxj13ZrzDwCv7wH2Znlj1eqPSUGIuJRxji7MOZYy6Ofq7bBWfwDIfUGaNzbWaVQVh2ugmN7Tk1gLM72xfB6T/jiDqc4kkgxlBiIiMc1qBPGPQOa892qNIa+PIu3Z29i71H91FpgwFgY9SUYU/Z7mw2E0NqwZGLR13NzYeZz8NZAp1fB+Dt1EkSKocRARCrFTT0bM3ZIa3JyLY99uYouT/7I78cv5MulO8k4mePt8KqugCBoO8KZwPj/7d17eFx1ncfx9zf3NmnSkkuvtGmbNAXkXqC2RQJouTwgFZEVFYGlIiCK7rJeFlnQ9QIPstwRkUVWca3K4qIryLUF2tLSCl1we7/SK0ma3tI2SdP89o/fmUkaMsnM5DIzmc/rec5zMmfOmfnm23Tyze/8Lgd3H/nc3u3wq1nwyvd8a8QN82HYOKhTYSCRZSU6ABFJD1mZGVw9fTxXTx/P6g/28czbW3l22VZeXVnDkNwsLjh+BJeeMobTy48iIyOOv5zT2YlXwJKfw/Jn4dSr/LFVz8N/3wgtjXDxA3DKF32LRHEF7FyX2HglqakwEJF+N2n4EL59wWT+6bwqFq3fyTNvb+XP727nd0u3MHnEEJ65cRqDc/TxFLXRp0BxpR+dcMLl8NK/wFuPwYjj4dNP+DkPQoorYcMb/hZDLB0dJW3op0JEEiYzw5heUcI9l5/Iku9+nFsvPIaVO/Yxf01dokNLLWZw0hXw/kL42cd8UTD1Rpj9ypFFAUDxRD8CYt+2xMQqSU+FgYgkhcE5WVw1rZz8nEzmra5NdDip54S/A8uAA/Xwud/D+T/26zF0VFLp9zvXxv9ezfth16b4r5ekpsJARJJGTlYG0ypKeG1VLc5ppsSYFI2Ba1+GGxfBpJmRzyuu8PuedEB8/W54+HSo3xD/a0jSUmEgIkmluqqUrbsPsramIdGhpJ4xp0JBadfnDBkJ2fk964C4bZnv1PjCrfG/hiQtFQYiklSqq8oAmLuqJsGRDFBmvp9BT+YyqFkBOUNg1Z9hzUu9F5skBRUGIpJURg8dxKThBcxbpX4Gfaa4Iv4+BgfqoWEHzLjZv87z34p/iuWDuzQ9cxJSYSAiSae6qowlG+tpaIpi/n+JXUkl7H4/vl/KtSv9fuTJcMFdUL8O3nw49tepXw8PnAIPnQbrupnOWfqVBgqLSNKpnlTKY6+vZ+HaOmYeNyLR4fSK5pZWGppa2N/Uwr7GFvY3t9DQ2EJDk98ONB/m0OFWmltaw/vm0D44dvyYoVw7Y3zPgymuANfqOw+WTY7t2prlfl92DBSNhskXwes/8aMiikZH9xqNe+E3V/gYMrP97IwnfQHO+4Gf3jkWh1v80tPFFZA7JLZro9V8wK9H0VDrWzma9kJzg1+UKrztbfvaMiArz2/ZeW1fhx5n5oI77GNvPQSHm+HwIWhtOfLrc26LPqe9SIWBiCSdKeVHhYctJnthcLjVUbuvie17DrJjTyPb9zSyfc9Btu9pDD+ubWiiuaU16tc0g5zMDHKyMsL77MwMigZl907QoZEJO9fEURisgNwiKBzlH5/3Q3j4DHjpNrjsie6vb22FZ67zoyKufAaOngqv3QUL7oc1L8KFd8Oxl3S/bsT+Ovjrk7D0Cdi71a8BMepkKJ8B5WfC2DOiKxSaGqButY9n9yZo+AAaamB/bfB1LTTv6+IFzL9P7hDIKYDcAnDOt8a0HPT7QwfbHrtOfg4ysn2BlJnd9nVGti8+EkCFgYgknY7DFi2exYX6yP6mFpZu2sXi9TtZvKGed7fs5tDhI4dW5mZlMLIoj5FFgzhj/FGUFuZSmJdNfk4mBXnZFORmkp+bRUFoy8tiUHZmuBDIzLC+/Z7DhUEc/QxqVvjWglB8w8phxjdg3o/h1Gtg/JldXz/3B7D6ebjgbphQ7Y99/HY47lN+5cffX+VbIS78CRSO/PD125b5CZzeexoON8H4s+DsW/2tiY3z4c2HYMF9vlAYfUpQKMyA0smwayPUrvKFQO0qXwzs3XLk6w8aBvllUFDmC438Mj/So2C4/3rQMMgrbCsGsvOjn0HSOd8S0NLo48vMhoys+BbP6kMqDEQkKZ1dVcZLyz9gTU0Dk4b3URNxFPY2HmLpxnoWr69n0YZ6/rZ1D4dbHZkZxgljirh6Wjlji/MZVZTHiKI8RhUNYujg7KQqZj4kr9D/oquLsTBwzt9KOPaSI49PvxmW/Rqe/yZ8+XX/C68z7z0Nb9zj1204/UtHPjfyBJj9Kix6GOb+yLdCzPw+nHKV/2W64o+w+DHYvAiyB8PJn4fTr/NFSnvN+2HzYl8kbJwPCx+E+fceeU52vu9nUT4dSib5rbTKFzmdTQrVW8zaWgaSWMoWBma2ERjXyVPPOefiWNRcRJJJdZUfjz9vVU3MhcH9L6/hxeU7ehxDc0sr62obaHWQnWmcOGYo1581gTPGF3PquGHk56bsR2h8IxMaPvD32MuOPfJ49iA4/06Y8zlY8jhMveHD1257B569CcZ+FC68p/O/kjOzfJEx+SL4081+e+cp2LMF9m33v7jP+xGc9HkYNLTzGHPyYeI5fgN/q2DzYt+icNR4KKmCwtFaJ6ILKfxTzWlAZrvHI4G/Ar9LTDgi0ptGtRu2eN3HJkZ93ZZdB3jg1TVUlhUwZtigHsVgZlxw/Eimjj+Kk8cOY1BOZvcXpYriibDyudiuad/xsKOqC6Hi4/6v/Y982jfFh+z7AOZ8HgYXw+W/8ktFdxfbF/8I7/wS5t3l3++i+6DyE5AR479BbgFUnAucG9t1aSxlCwPn3BGDnM3sWmAv8PvERCQiva26qoxfLNhAQ1MLBVH+df74GxvIMPjFNacxsqhnhcGAVlwJB+p8C0C0IwFqgqGKHVsMwLcAnH8XPDIVXr4DZj3ij7c0wW+/4Oc/uPbF7mdmDMnIgFOv9pv0q6jbUszsMjN70MzeMLO9ZubM7KlurhljZk+Y2TYzazKzjWZ2n5nFOB6l29gMuBZ4yjl3oDdfW0QSp3pSKYcOOxaujW61xZ0NTcxZ8j6zThqtoqA74Q6IMUyNXLMc8kshv6Tz50sqYNpNvr/B5rd8n4T/+QfY8hZ86qe+H4EkvVhusnwXuAk4Cdja3clmNhHftH8N8BZwL7AeuBl408yKY442sk8A44HHe/E1RSTB2g9bjMaTCzfS1NLKl8+K/tZD2gqtshjLYkqhEQldOfMWGDIKnrvFT3y07Cn42Df9qANJCbEUBt8AJgGFQCc9Sz7kEaAM+JpzbpZz7tvOuXPwBUIV8MP2J5vZD4JWiK626gjv9SVgiXNuWQzfj4gkuZysDKZXlDBvZU23qy02NLXwHws3ct6xI6goK+inCFPY0HF+yFy0HRBbW/2sh53dRmgvt8BPVLT9f+HFW31Hwurv9Dxe6TdRFwbOubnOuTUuirVQzWwCMBPYCHScK/N2YD9wpZnltzt+H3BMN9tbnbxXGXAJ8PNovxcRSR3VVWVs29PImm5WW/zN4vfZ29jC9dVqLYhKVo7v5R/tYkp7NvsJd0qjmBDpuEuhciaMPBE+9ahGAKSYvup8GIwT4UXnjpzmyTm3z8wW4AuHqcArwfE6ILobiUe6GmgC5sQdrYgkrWiGLTa1HObx+euZNrGYk46OMIxNPqy4Ivo+BjUr/L67FgPwHRGvmAOYioIU1Ff/YlXBfnWE50Ml6qSevEnQ6XA2MMc519WclSKSokZFsdriH97eygd7m7ixuqIfIxsASip9YdAaxXTN4aGKUU6hnJGpoiBF9dW/WlGw3xPh+dDxnpb21UAlUdxGMLPrzGypmS2trdVyriKppKvVFg+3On72+nqOH13E9Ire7NOcBoon+vn793bbn9z3LygcA3lF3Z8rKS1R5Vxoyqtu+yt0Jej3YM65D/U96OTcx5xzU5xzU0pLoxxHKyJJoboq8rDFF/5vBxvq9nND9cTknoY4GbVfTKk7Ncu7H5EgA0JfFQahFoFIpWVhh/NERCKaMs4PW5zb4XaCc46fzlvHhJJ8zkvyVRiTUnEwZLG7fgaHW6B2tQqDNNFXhcGqYB+pD0Hw0xixD4KISFho2OJrq44ctrhg7U7e27qHL581gcwMtRbEbMgIv1Rwd0MWd23wKxmqMEgLfVUYzA32M83siPcwsyHAdOAgsKiP3l9EBpjOhi0+Mm8twwtzmXXy6ARGlsLMfD+D7iY56mqNBBlw+qQwcM6tA14EyoGvdHj6e0A+8Evn3P6+eH8RGXjaD1sEWLZ5NwvX7WT2jAnkZg2gxY36WzSrLNasAMyvTCgDXtTzGJjZLGBW8DB0M++jZvZk8HWdc+6WdpfcCCwEHjCzc4EVwBnA2fhbCLf2IG4RSTMdV1t8dN46CvOyuOKMsYkOLbUVV8LfnoFDjZCd1/k5Ncv9ksU5g/s3NkmIWCY4Ogm4qsOxCcEGsAkIFwbOuXVmNgX4PnA+cCGwHXgA+J5zrj7eoEUkPZ1dVcYTCzbw7pbdvLB8BzedXRH1qosSQXEF4Hw/gki3CmpWRDexkQwIsUyJfEcwNDDSVt7JNZudc9c450Y653Kcc+OcczerKBCReJwVDFu86T/fITcrg6unlSc6pNRXEgxZjNTPoKXJj1pQ/4K0oWmpRCRlhIYtvl9/gM+eNpbigtxEh5T6jgrWlojUz6BuDbjDKgzSiAoDEUkZOVkZzKgsITPDmH3m+ESHMzDkFULB8MiFQWiNhFIVBulCN+dEJKV854Jj+OzpYxkzTB3hek1xZReFwXLIyGqbJVEGPBUGIpJSykvyKS/J7/5EiV7xRFjxp86fq1nhC4esnP6NSRJGtxJERNJdSSUcrIcDnfQL1xoJaUeFgYhIugsvptRhzYSmBti9SUMV04wKAxGRdBdeTKnDkMW6YNkbtRikFRUGIiLpbtg438GwYwfE0IgEFQZpRYWBiEi6y8yGYeUfnuSoZgVk5fnnJG2oMBARkWAxpQ59DGqWQ2kVZGiRqnSiwkBERHxhUL8OWlvbjmmNhLSkwkBERHxh0NIIe7f4xwfqYd929S9IQyoMRESkbchiqJ9B7Uq/V4tB2lFhICIifpIjaOtnoBEJaUuFgYiI+IWUcgra5jKoWQG5hVA4OrFxSb9TYSAiImAWjEwI5jKoWQGlk/1xSSsqDERExCuugLq14JzWSEhjKgxERMQrqYQ9m/36CAfr1fEwTakwEBERr7gCcLDyOf9YLQZpSYWBiIh4oSGLK/7o92oxSEsqDERExCue6PfvL4LBJVBQmth4JCFUGIiIiJc7BApGAE63EdKYCgMREWkTmuhIhUHaUmEgIiJtQrcTVBikLRUGIiLSpjjUYqCOh+kqK9EBiIhIEjn2k372w1EnJzoSSRAVBiIi0mboWLj4vkRHIQmkWwkiIiISpsJAREREwlQYiIiISJgKAxEREQlTYSAiIiJhKgxEREQkTIWBiIiIhKkwEBERkTAVBiIiIhKmwkBERETCVBiIiIhImAoDERERCVNhICIiImHmnEt0DP3OzGqBTb38siVAXS+/prRRfvuW8tu3lN++pfxGZ5xzrrS7k9KyMOgLZrbUOTcl0XEMVMpv31J++5by27eU396lWwkiIiISpsJAREREwlQY9J7HEh3AAKf89i3lt28pv31L+e1F6mMgIiIiYWoxEBERkTAVBiIiIhKmwqAHzGyMmT1hZtvMrMnMNprZfWY2LNGxpQIzu8zMHjSzN8xsr5k5M3uqm2ummdlzZlZvZgfM7F0z+7qZZfZX3KnCzIrNbLaZ/cHM1prZQTPbY2bzzexaM+v0/79yHD0zu8vMXjGzzUF+683sHTO73cyKI1yj/PaAmV0ZfFY4M5sd4ZyLzGxe8PPeYGaLzeyq/o41VamPQZzMbCKwECgDngVWAqcDZwOrgOnOuZ2JizD5mdky4ESgAdgCTAZ+7Zz7QoTzLwH+C2gEfgvUAxcDVcDTzrnP9EfcqcLMrgd+CmwH5gLvA8OBS4EifC4/49p9CCjHsTGzZuBtYDlQA+QDU4EpwDZgqnNuc7vzld8eMLOjgfeATKAA+JJz7vEO59wEPAjsxOe4GbgMGAPc45y7pV+DTkXOOW1xbMALgAO+2uH4vwXHH010jMm+4YuoSsCA6iBvT0U4txD/wdsETGl3PA9foDngs4n+npJpA87B/9LJ6HB8BL5IcMCnleMe5TgvwvEfBvl6RPnttVwb8DKwDrg7yNfsDueU44uunUB5u+PDgLXBNR9N9PeS7JtuJcTBzCYAM4GNwMMdnr4d2A9caWb5/RxaSnHOzXXOrXHB/9xuXAaUAnOcc0vbvUYj8N3g4Q19EGbKcs696pz7k3OutcPxHcCjwcPqdk8pxzEKctOZ3wX7ynbHlN+e+Rq+2L0G/xnbmb8HcoGHnHMbQwedc7uAHwUPr+/DGAcEFQbxOSfYv9jJh+4+YAEwGN+kKL0jlPO/dPLc68ABYJqZ5fZfSCntULBvaXdMOe49Fwf7d9sdU37jZGbHAHcC9zvnXu/i1K5y/HyHcyQCFQbxqQr2qyM8vybYT+qHWNJFxJw751qADUAWMKE/g0pFZpYFfDF42P4DVDmOk5ndYmZ3mNm9ZvYG8K/4ouDOdqcpv3EIfl5/hb/99c/dnN5VjrfjWxrGmNngXg1ygMlKdAApqijY74nwfOj40H6IJV0o573nTuAjwHPOuRfaHVeO43cLvmNnyF+Aq51zte2OKb/x+RfgZGCGc+5gN+dGk+P84LwDvRPewKMWg75hwV5DPvqPch4FM/sa8I/4UTRXxnp5sFeOO3DOjXDOGb5j56X4v/rfMbNTYngZ5bcDMzsd30pwj3Puzd54yWCvHHdBhUF8QtVoUYTnCzucJz2nnPeQmX0FuB8/tO5s51x9h1OU4x5yzn3gnPsDvnNyMfDLdk8rvzFodwthNXBblJdFm+O9PQhtwFNhEJ9VwT5SH4JQT+RIfRAkdhFzHnyAjMd3pFvfn0GlCjP7OvAQ8Dd8UbCjk9OU417inNuEL8COM7OS4LDyG5sCfK6OARrbTWrk8KO/AH4eHLsveNxVjkfibyNscc7pNkIXVBjEZ26wn9lx9jgzGwJMBw4Ci/o7sAHs1WB/fifPfQw/CmShc66p/0JKDWb2LeBeYBm+KKiJcKpy3LtGBfvDwV75jU0T8O8RtneCc+YHj0O3GbrK8QUdzpFIEj2RQqpuaIKj3s5nNd1PcFSLJoeJNa+3BblZChzVzbnKcWy5nQyM6OR4Bm0THC1Qfvsk93fQ+QRH49EERz3eNCVynDqZEnkFcAZ+Nr/VwDSnKZG7ZGazgFnBwxHAefhm1DeCY3Wu3fSlwflP4//jz8FPJ/tJgulkgcudfqDDgrnhn8T/xfognd+73uice7LdNcpxlILbM3fj5yBYh/9lNBw4C9/5cAdwrnNuebtrlN9eYGZ34G8ndDYl8leBB9CUyPFLdGWSyhtwNPAL/Fz0zcAmfOeuLv8y0xbO3x34Cj7StrGTa6YDzwG78Ldr3gO+AWQm+vtJti2K/DpgnnIcd34/gp/5dBlQh+8fsAdYEuS+088B5bdXch/62Z4d4fmLgdeAffi5C5YAVyU67lTZ1GIgIiIiYep8KCIiImEqDERERCRMhYGIiIiEqTAQERGRMBUGIiIiEqbCQERERMJUGIiIiEiYCgMREREJU2EgIiIiYSoMREREJOz/AUme3RlxCfDeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b18f400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"plt.semilogy(np.abs(x_true - np.array(approximation_bs, dtype=np.float64)), label=\"Binary\")\n",
"plt.semilogy(np.abs(x_true - np.array(approximation_gs, dtype=np.float64)), label=\"Golden\")\n",
"plt.legend(fontsize=16)\n",
"plt.xticks(fontsize=20)\n",
"_ = plt.yticks(fontsize=20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Время работы"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"455 µs ± 35.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"416 µs ± 26.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%timeit binary_search(f, a, b, epsilon)\n",
"%timeit golden_search(f, a, b, epsilon)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Резюме\n",
"1. Введение в численные методы оптимизации\n",
"2. Общая схема работы метода\n",
"3. Способы сравнивнения методов оптимизации\n",
"4. Зоопарк задач и методов\n",
"5. Одномерная минимизация"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Семинар 13\n",
"\n",
"# Методы спуска (Descent methods)\n",
"# Пять способов получить градиентный спуск"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## На прошлом семинаре...\n",
"1. Введение в численные методы оптимизации\n",
"2. Общая схема работы метода\n",
"3. Как сравнивать методы оптимизации?\n",
"4. Зоопарк задач и методов\n",
"5. Одномерная минимизация"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Что такое методы спуска?\n",
"\n",
"Последовательность $x_k$ генерируется по правилу\n",
"$$\n",
"x_{k+1} = x_k + \\alpha_k h_k\n",
"$$\n",
"так что\n",
"$$\n",
"f(x_{k+1}) < f(x_k)\n",
"$$\n",
"Направление $h_k$ называется *направлением убывания*.\n",
"\n",
"**Замечание**: существуют методы, которые не требуют монотонного убывания функции от итерации к итерации."
]
},
{
"cell_type": "markdown",
"metadata": {
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"slide_type": "fragment"
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},
"source": [
"```python\n",
"def DescentMethod(f, x0, epsilon, **kwargs):\n",
" x = x0\n",
" while StopCriterion(x, f, **kwargs) > epsilon:\n",
" h = ComputeDescentDirection(x, f, **kwargs)\n",
" alpha = SelectStepSize(x, h, f, **kwargs)\n",
" x = x + alpha * h\n",
" return x\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Способ 1: направление убывания\n",
"Рассмотрим линейную аппроксимацию дифференцируемой функции $f$ вдоль некоторого направления убывания $h, \\|h\\|_2 = 1$:\n",
"$$\n",
"f(x + \\alpha h) = f(x) + \\alpha \\langle f'(x), h \\rangle + o(\\alpha)\n",
"$$\n",
"Из условия убывания\n",
"$$\n",
"f(x) + \\alpha \\langle f'(x), h \\rangle + o(\\alpha) < f(x)\n",
"$$\n",
"и переходя к пределу при $\\alpha \\rightarrow 0$:\n",
"$$\n",
"\\langle f'(x), h \\rangle \\leq 0\n",
"$$\n",
"Также из неравенства Коши-Буняковского-Шварца\n",
"$$\n",
"\\langle f'(x), h \\rangle \\geq -\\| f'(x) \\|_2 \\| h \\|_2 = -\\| f'(x) \\|_2\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Таким образом, направление антиградиента \n",
"$$\n",
"h = -\\dfrac{f'(x)}{\\|f'(x)\\|_2}\n",
"$$\n",
"даёт направление **наискорейшего локального** убывания функции$~f$.\n",
"\n",
"В итоге метод имеет вид\n",
"$$\n",
"x_{k+1} = x_k - \\alpha f'(x_k)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Способ 2: схема Эйлера решения ОДУ\n",
"\n",
"Рассмотрим обыкновенное диференциальное уравнение вида:\n",
"$$\n",
"\\frac{dx}{dt} = -f'(x(t))\n",
"$$\n",
"и дискретизуем его на равномерной сетке с шагом $\\alpha$:\n",
"$$\n",
"\\frac{x_{k+1} - x_k}{\\alpha} = -f'(x_k),\n",
"$$\n",
"где $x_k \\equiv x(t_k)$ и $\\alpha = t_{k+1} - t_k$ - шаг сетки.\n",
"\n",
"Отсюда получаем выражение для $x_{k+1}$\n",
"$$\n",
"x_{k+1} = x_k - \\alpha f'(x_k),\n",
"$$\n",
"которое в точности совпадает с выражением для градиентного спуска.\n",
"\n",
"Такая схема называется явной или прямой схемой Эйлера.\n",
"\n",
"**Вопрос:** какая схема называется неявной или обратной?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Способ 3: манипуляции с необходимым условием\n",
"\n",
"\\begin{align*}\n",
"& f'(x) = 0\\\\\n",
"& -\\alpha f'(x) = 0\\\\\n",
"& x - \\alpha f'(x) = x\\\\\n",
"& x_k - \\alpha f'(x_k) = x_{k+1}\n",
"\\end{align*}\n",
"Это **HE** доказательство корректности, только формальное получение выражения!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Способ 4: минимизация квадратичной оценки сверху \n",
"#### (А. В. Гасников Метод универсального градиентного спуска https://arxiv.org/abs/1711.00394)\n",
"\n",
"Глобальная оценка сверху на функцию $f$ в точке $x_k$:\n",
"$$\n",
"f(y) \\leq f(x_k) + \\langle f'(x_k), y - x_k \\rangle + \\frac{L}{2} \\|y - x_k \\|_2^2 = g(y), \n",
"$$\n",
"где $\\lambda_{\\max}(f''(x)) \\leq L$ для всех допустимых $x$.\n",
"\n",
"Справа &mdash; квадратичная форма, точка минимума которой имеет аналитическое выражение:\n",
"\\begin{align*}\n",
"& g'(y^*) = 0 \\\\\n",
"& f'(x_k) + L (y^* - x_k) = 0 \\\\\n",
"& y^* = x_k - \\frac{1}{L}f'(x_k) = x_{k+1}\n",
"\\end{align*}\n",
"Этот способ позволяет оценить значение шага как $\\frac{1}{L}$. Однако часто константа $L$ неизвестна."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Способ 5: концепция доверительных областей\n",
"\n",
"Минимизация линейной аппроксимации в шаре радиуса $r$ с центром в точке $x_k$\n",
"\\begin{align}\n",
"& \\min \\; f(x_k) + \\langle f'(x_k), x - x_k \\rangle\\\\\n",
"\\text{s.t. } & \\| x - x_k \\|^2_2 \\leq r^2\n",
"\\end{align}\n",
"\n",
"Используя метод множителей Лагранжа и условия ККТ, получим\n",
"$$\n",
"x_{k+1} = x_k - \\frac{1}{2\\lambda} f'(x_k),\n",
"$$\n",
"где $\\lambda$ &mdash; множитель Лагранжа, и\n",
"$$\n",
"\\lambda = \\frac{\\| f'(x_k)\\|_2}{2r}\n",
"$$\n",
"\n",
"**Вопрос:** как выбрать $r$?\n",
"\n",
"**Вопрос:** какое соотношение между константой Липшица $L$ градиента $f'(x_k)$ из способа 4 и $\\lambda$?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Итого: метод градиентного спуска &mdash; дёшево и сердито\n",
"```python\n",
"def GradientDescentMethod(f, x0, epsilon, **kwargs):\n",
" x = x0\n",
" while StopCriterion(x, f, **kwargs) > epsilon:\n",
" h = ComputeGradient(x, f, **kwargs)\n",
" alpha = SelectStepSize(x, h, f, **kwargs)\n",
" x = x - alpha * h\n",
" return x\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Как выбрать шаг $\\alpha_k$? (J. Nocedal, S. Wright Numerical Optimization, $\\S$ 3.1.)\n",
"\n",
"Список подходов:\n",
"- Постоянный шаг \n",
"$$\n",
"\\alpha_k = \\overline{\\alpha}\n",
"$$\n",
"- Априорно заданная последовательность, например\n",
"$$\n",
"\\alpha_k = \\dfrac{\\overline{\\alpha}}{\\sqrt{k+1}}\n",
"$$\n",
"- Наискорейший спуск\n",
"$$\n",
"\\alpha_k = \\arg\\min_{\\alpha \\geq 0} f(x_k - \\alpha f'(x_k))\n",
"$$\n",
"- Требование **достаточного** убывания, требование **существенного** убывания и условие кривизны: для некоторых $\\beta_1, \\beta_2$, таких что $0 < \\beta_1 < \\beta_2 < 1$ найти $x_{k+1}$ такую что\n",
"\n",
" - Достаточное убывание: $f(x_{k+1}) \\leq f(x_k) - \\beta_1 \\alpha_k \\langle f'(x_k), h_k \\rangle$ или\n",
" $ f(x_k) - f(x_{k+1}) \\geq \\beta_1 \\alpha_k \\langle f'(x_k), h_k \\rangle\n",
" $\n",
" - Существенное убывание: $f(x_{k+1}) \\geq f(x_k) - \\beta_2 \\alpha_k \\langle f'(x_k), h_k \\rangle$ или\n",
" $\n",
" f(x_k) - f(x_{k+1}) \\leq \\beta_2 \\alpha_k \\langle f'(x_k), h_k \\rangle\n",
" $\n",
" - Условие кривизны: $\\langle f'(x_{k+1}), h_k \\rangle \\geq \\beta_2 \\langle f'(x_k), h_k \\rangle$\n",
"\n",
"Обычно коэффициенты выбирают так: $\\beta_1 \\in (0, 0.3)$, а $\\beta_2 \\in (0.9, 1)$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Анализ и мотивация подходов к выбору шага $\\alpha_k$\n",
"- Постоянный шаг: самое простое и неэффективное решение\n",
"- Априорно заданная последовательность: немногим лучше постоянного шага\n",
"- Наискорейший спуск: самое лучшее решение, но применимо только если вспомогательная задача решается аналитически или ооооооочень быстро. <br></br>\n",
"То есть почти всегда неприменимо :)\n",
"- Требование достаточного убывания, требование существенного убывания и условие кривизны:\n",
" - требование достаточного убывания гарантирует, что функция в точке $x_{k+1}$ не превосходит линейной аппроксимации с коэффициентом наклона $\\beta_1$\n",
" - требование существенного убывания гарантирует, что функция в точке $x_{k+1}$ убывает не меньше, чем линейная аппроксимация c коэффициентом наклона $\\beta_2$\n",
" - условие кривизны гарантирует, что угол наклона касательной в точке $x_{k+1}$ не меньше, чем угол наклона касательной в точке $x_k$, <br></br>\n",
"умноженный на $\\beta_2$ \n",
"\n",
"Требование существенного убывания и условие кривизны обеспечивают убывание функции по выбранному направлению $h_k$. Обычно выбирают одно из них.\n",
"[comment]: <> (<img src=\"Goldstein.png\", style=\"width: 600px;\">)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"#### Альтернативные названия\n",
"- Требование достаточного убывания $\\equiv$ правило Армихо\n",
"- Требование достаточного убывания + условие кривизны $\\equiv$ правило Вольфа\n",
"- Требование достаточного убывания + требование существенного убывания $\\equiv$ правило Гольдштейна"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Зачем нужно условие существенного убывания?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
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"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42430f4391914c83b9e16e2af707e7a1",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"interactive(children=(FloatSlider(value=1.5, description='Initial point', max=4.0, min=-4.0), FloatSlider(value=0.8, description='Step', max=1.2), Output()), _dom_classes=('widget-interact',))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as ipywidg\n",
"import numpy as np\n",
"\n",
"def gd(f, gradf, x0, step, eps=1e-6, maxiter=10):\n",
" x = x0\n",
" x_hist = [x]\n",
" n_iter = 0\n",
" while abs(gradf(x)) > eps and n_iter < maxiter:\n",
" x = x - step * gradf(x)\n",
" x_hist.append(x)\n",
" n_iter += 1\n",
" return x, x_hist\n",
"\n",
"f = lambda x: x**2\n",
"gradf = lambda x: 2 * x\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"plt.rc(\"text\", usetex=True)\n",
"\n",
"def update(x0, step):\n",
" _, x_hist = gd(f, gradf, x0, step)\n",
" x = np.linspace(-np.max(x_hist) - 1, np.max(x_hist) + 1)\n",
" ax.clear()\n",
" ax.plot(x, f(x), color=\"r\", label=\"$f(x) = x^2$\")\n",
" y_hist = np.array([f(x) for x in x_hist])\n",
" x_hist = np.array(x_hist)\n",
" plt.quiver(x_hist[:-1], y_hist[:-1], x_hist[1:]-x_hist[:-1], y_hist[1:]-y_hist[:-1], \n",
" scale_units='xy', angles='xy', scale=1, width=0.005, color=\"green\", label=\"Descent path\")\n",
" ax.legend()\n",
" fig.canvas.draw()\n",
"\n",
"step_slider = ipywidg.FloatSlider(value=0.8, min=0, max=1.2, step=0.1, description=\"Step\")\n",
"x0_slider = ipywidg.FloatSlider(value=1.5, min=-4, max=4, step=0.1, description=\"Initial point\")\n",
"_ = ipywidg.interact(update, x0=x0_slider, step=step_slider)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"def plot_alpha(f, x, h, alphas):\n",
" df = np.zeros_like(alphas)\n",
" for i, alpha in enumerate(alphas):\n",
" df[i] = f(x + alpha * h)\n",
" plt.plot(alphas, df)\n",
" plt.xlabel(r\"$\\alpha$\", fontsize=18)\n",
" plt.ylabel(r\"$f(x + \\alpha h)$\", fontsize=18)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_alpha(lambda x: x**2, 0.5, -1, np.linspace(1e-3, 1.01, 10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Backtracking \n",
"\n",
"```python\n",
"def SelectStepSize(x, f, h, rho, alpha0, beta1, beta2):\n",
" # 0 < rho < 1\n",
" # alpha0 - initial guess of step size\n",
" # beta1 and beta2 - constants from conditions\n",
" alpha = alpha0\n",
" # Check violating sufficient decrease and curvature conditions \n",
" while (f(x - alpha * h) >= f(x) + beta1 * alpha grad_f(x_k).dot(h)) and \n",
" (grad_f(x - alpha * h).dot(h) <= beta2 * grad_f(x_k).dot(h)):\n",
" alpha *= rho\n",
" return alpha\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Теоремы сходимости (Б.Т. Поляк Введение в оптимизацию, гл. 1, $\\S$ 4; гл. 3, $\\S$ 1; Ю.Е. Нестеров Введение в выпуклую оптимизацию, $\\S$ 2.2)\n",
"От общего к частному:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Теорема 1.** \n",
"Пусть \n",
"\n",
"- $f(x)$ дифференцируема на $\\mathbb{R}^n$, \n",
"- градиент $f(x)$ удовлетворяет условию Липшица с константой $L$\n",
"- $f(x)$ ограничена снизу\n",
"- $\\alpha = const$ и $0 < \\alpha < \\frac{2}{L}$\n",
"\n",
"Тогда для градиентного метода выполнено:\n",
"$$\n",
"\\lim\\limits_{k \\to \\infty} f'(x_k) = 0,\n",
"$$\n",
"а функция монотонно убывает $f(x_{k+1}) < f(x_k)$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"** Теорема 2.**\n",
"Пусть\n",
"- $f(x)$ дифференцируема на $\\mathbb{R}^n$, \n",
"- градиент $f(x)$ непрерывен\n",
"- множество $\\{ x: f(x) \\leq f(x_0) \\}$ ограничено\n",
"- $\\alpha_k = \\arg\\min\\limits_{\\alpha \\geq 0} f(x_k - \\alpha f'(x_k))$\n",
"\n",
"Тогда \n",
"$$\n",
"f'(x_k) \\to 0, \\; k \\to \\infty \\qquad x_{k_i} \\to x^*\n",
"$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Теорема 3.** Пусть\n",
"- $f(x)$ дифференцируема на $\\mathbb{R}^n$\n",
"- $f(x)$ выпукла \n",
"- $f'(x)$ удовлетворяет условию Липшица с константой $L$\n",
"- $\\alpha = \\dfrac{1}{L}$\n",
"\n",
"Тогда \n",
"$$\n",
"f(x_k) - f^* \\leq \\dfrac{2L \\| x_0 - x^*\\|^2_2}{k+4}\n",
"$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Теорема 4.** \n",
"Пусть\n",
"\n",
"- $f(x)$ дифференцируема на $\\mathbb{R}^n$, \n",
"- градиент $f(x)$ удовлетворяет условию Липшица с константой $L$\n",
"- $f(x)$ является сильно выпуклой с константой $l$\n",
"- $\\alpha = const$ и $0 < \\alpha < \\frac{2}{L}$\n",
"\n",
"Тогда градиентный метод сходится к единственной точке глобального минимума $x^*$ с линейной скоростью:\n",
"$$\n",
"\\| x_k - x^* \\|_2 \\leq cq^k, \\qquad 0 \\leq q < 1\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Теорема 5.**\n",
"Пусть\n",
"\n",
"- $f(x)$ дифференцируема на $\\mathbb{R}^n$, \n",
"- градиент $f(x)$ удовлетворяет условию Липшица с константой $L$\n",
"- $f(x)$ является сильно выпуклой с константой $l$\n",
"- $\\alpha = \\dfrac{2}{l + L}$\n",
"\n",
"Тогда для градиентного метода выполнено:\n",
"$$\n",
"\\| x_k - x^* \\|^2_2 \\leq \\left( \\dfrac{M - 1}{M + 1} \\right)^k \\|x_0 - x^*\\|^2_2 \\qquad f(x_k) - f^* \\leq \\dfrac{L}{2} \\left( \\dfrac{M - 1}{M + 1} \\right)^{2k} \\| x_0 - x^*\\|^2_2,\n",
"$$\n",
"где $M = \\frac{L}{l}$"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Теорема 6.**\n",
"Пусть \n",
"- $f(x)$ дважды дифференцируема и $l\\mathbf{I} \\preceq f''(x) \\preceq L\\mathbf{I}$ для всех $x$\n",
"- $\\alpha = const$ и $0 < \\alpha < \\frac{2}{L}$\n",
"\n",
"Тогда \n",
"$$\n",
"\\| x_k - x^*\\|_2 \\leq \\|x_0 - x^*\\|_2 q^k, \\qquad q = \\max(|1 - \\alpha l|, |1 - \\alpha L|) < 1\n",
"$$\n",
"и минимальное $q^* = \\dfrac{L - l}{L + l}$ при $\\alpha^* = \\dfrac{2}{L + l}$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### От чего зависит $q^*$ и как это использовать?\n",
"Из Теорем 5 и 6 имеем \n",
"$$\n",
"q^* = \\dfrac{L - l}{L + l} = \\dfrac{L/l - 1}{L/l + 1} = \\dfrac{M - 1}{M + 1},\n",
"$$\n",
"где $M$ - оценка числа обусловленности $f''(x)$.\n",
"\n",
"**Вопрос**: что такое число обусловленности матрицы?\n",
"\n",
"- При $M \\gg 1$, $q^* \\to 1 \\Rightarrow$ оооочень **медленная** сходимости градиентного метода. Например при $M = 100$: $q^* \\approx 0.98 $\n",
"- При $M \\simeq 1$, $q^* \\to 0 \\Rightarrow$ **ускорение** сходимости градиентного метода. Например при $M = 4$: $q^* = 0.6 $\n",
"\n",
"**Вопрос**: какая геометрия у этого требования?\n",
"\n",
"**Мораль**: необходимо сделать оценку $M$ как можно ближе к 1!\n",
"\n",
"О том, как это сделать, Вам будет предложено подумать в домашнем задании :)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Выбор начального приближения\n",
"\n",
"Обратите внимание, что ни в одной из теорем приведённых выше не было условий на начальное приближение $x_0$!\n",
"\n",
"- Для невыпуклой функции градиентный спуск сойдётся к некоторой стационарной точке, которая может быть как локальным минимумом, так и седловой точкой \n",
"- Для выпуклых функций стационарная точка будет точкой минимума\n",
"- Способы выбора начального приближения:\n",
" - случайная точка\n",
" - безградиентные методы случайного поиска, например [эволюционный алгоритм](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.differential_evolution.html), алгоритм муравьиной колонии (ant colony) и т.д.\n",
" - обычно эти методы могут дать лишь незначительное уменьшение функции, однако сильно отойти от локальных минимумов"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Вычислительный аспект и эксперименты\n",
"1. Для каждого шага метода нужно хранить только текущую точку и вектор градиента: $O(n)$ памяти\n",
"2. Поиск $\\alpha_k$:\n",
" - дан априори\n",
" - ищется из аналитического решения задачи наискорейшего спуска\n",
" - заканчивается за конечное число шагов\n",
"3. Для каждого шага метода нужно вычислять линейную комбинацию векторов: $O(n)$ вычислений + высокопроизводительные реализации"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Pеализация градиентного спуска"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def GradientDescent(f, gradf, x0, epsilon, num_iter, line_search, \n",
" disp=False, callback=None, **kwargs):\n",
" x = x0.copy()\n",
" iteration = 0\n",
" opt_arg = {\"f\": f, \"grad_f\": gradf}\n",
" for key in kwargs:\n",
" opt_arg[key] = kwargs[key]\n",
" while True:\n",
" gradient = gradf(x)\n",
" alpha = line_search(x, -gradient, **opt_arg)\n",
" x = x - alpha * gradient\n",
" if callback is not None:\n",
" callback(x)\n",
" iteration += 1\n",
" if disp:\n",
" print(\"Current function val =\", f(x))\n",
" print(\"Current gradient norm = \", np.linalg.norm(gradf(x)))\n",
" if np.linalg.norm(gradf(x)) < epsilon:\n",
" break\n",
" if iteration >= num_iter:\n",
" break\n",
" res = {\"x\": x, \"num_iter\": iteration, \"tol\": np.linalg.norm(gradf(x))}\n",
" return res"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Выбор шага"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def constant_step(x, gradient, **kwargs):\n",
" return 1e-3\n",
"\n",
"# For quadratic function x'Ax\n",
"def exact_linesearch(x, gradient, **kwargs):\n",
" return max(0, -x.dot(A.dot(gradient)) / gradient.dot(A.dot(gradient)))\n",
"\n",
"def backtracking(x, descent_dir, **kwargs):\n",
" f = kwargs[\"f\"]\n",
" grad_f = kwargs[\"grad_f\"] \n",
" if kwargs[\"method\"] == \"Armijo\":\n",
" beta1 = kwargs[\"beta1\"]\n",
" rho = kwargs[\"rho\"]\n",
" alpha = 1\n",
" while f(x + alpha * descent_dir) >= f(x) + beta1 * alpha * grad_f(x).dot(descent_dir) or \\\n",
" np.isnan(f(x + alpha * descent_dir)):\n",
" alpha *= rho\n",
" if alpha < 1e-16:\n",
" break\n",
" return alpha"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Зависимость от обусловленности матрицы $f''(x)$\n",
"Рассмотрим задачу \n",
"$$\n",
"\\min f(x),\n",
"$$ \n",
"где\n",
"$$\n",
"f(x) = x^{\\top}Ax, \\; A = \n",
"\\begin{bmatrix}\n",
"1 & 0\\\\\n",
"0 & \\gamma\n",
"\\end{bmatrix}\n",
"$$\n",
"\n",
"$$\n",
"f'(x) = 2Ax\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def my_f(x, A):\n",
" return x.dot(A.dot(x))\n",
"\n",
"def my_gradf(x, A):\n",
" return 2 * A.dot(x)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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"\n",
"\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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"\n",
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" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
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"\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
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"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
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"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
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" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
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" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
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\" width=\"800\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0,0.5,'Number of iterations with $\\\\varepsilon = 10^{-7}$')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.rc(\"text\", usetex=True)\n",
"\n",
"gammas = [0.1, 0.5, 1, 2, 3, 4, 5, 10, 20, 50, 100, 1000, 5000, 10000]\n",
"num_iter_converg = []\n",
"for g in gammas:\n",
" A = np.array([[1, 0], \n",
" [0, g]], dtype=np.float64)\n",
" f = lambda x: my_f(x, A)\n",
" gradf = lambda x: my_gradf(x, A)\n",
"# x0 = np.random.rand(A.shape[0])\n",
"# x0 = np.sort(x0)\n",
"# x0 = x0[::-1]\n",
" x0 = np.array([g, 1], dtype=np.float64)\n",
"# print x0[1] / x0[0]\n",
" res = GradientDescent(f, gradf, x0, 1e-7, 10000, exact_linesearch)\n",
" num_iter_converg.append(res[\"num_iter\"])\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.loglog(gammas, num_iter_converg)\n",
"plt.xticks(fontsize = 20)\n",
"plt.yticks(fontsize = 20)\n",
"plt.xlabel(r\"$\\gamma$\", fontsize=20)\n",
"plt.ylabel(r\"Number of iterations with $\\varepsilon = 10^{-7}$\", fontsize=20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- При неудачном начальном приближении сходимость для плохо обусловенной задачи очень медленная\n",
"- При случайном начальном приближении сходимость может быть гораздо быстрее теоретических оценок\n",
"- **Open problem:** получить оценки сходимости, использующие случайное начальное приближение, а не худшее из возможных"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Эксперимент на многомерной задаче\n",
"Пусть $A \\in \\mathbb{R}^{m \\times n}$. Рассмотрим систему линейных неравенств: $Ax \\leq 1$ при условии $|x_i| \\leq 1$ для всех $i$.\n",
"\n",
"**Определение.** Аналитическим центром системы неравенств $Ax \\leq 1$ при условии $|x_i| \\leq 1$ является решение задачи\n",
"$$\n",
"f(x) = - \\sum_{i=1}^m \\log(1 - a_i^{\\top}x) - \\sum_{i = 1}^n \\log (1 - x^2_i) \\to \\min_x\n",
"$$\n",
"$$\n",
"f'(x) - ?\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"n = 100\n",
"m = 200\n",
"x0 = np.zeros(n)\n",
"A = np.random.rand(n, m)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Точное решение с помощью CVXPy"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ECOS', 'ECOS_BB', 'SCS', 'LS']\n",
"----------------------------------------------------------------------------\n",
"\tSCS v1.2.6 - Splitting Conic Solver\n",
"\t(c) Brendan O'Donoghue, Stanford University, 2012-2016\n",
"----------------------------------------------------------------------------\n",
"Lin-sys: sparse-indirect, nnz in A = 20700, CG tol ~ 1/iter^(2.00)\n",
"eps = 1.00e-03, alpha = 1.50, max_iters = 2500, normalize = 1, scale = 1.00\n",
"Variables n = 500, constraints m = 1200\n",
"Cones:\tsoc vars: 300, soc blks: 100\n",
"\texp vars: 900, dual exp vars: 0\n",
"Setup time: 2.32e-03s\n",
"----------------------------------------------------------------------------\n",
" Iter | pri res | dua res | rel gap | pri obj | dua obj | kap/tau | time (s)\n",
"----------------------------------------------------------------------------\n",
" 0| 1.31e+02 2.67e+03 7.93e-01 -3.31e+04 -3.82e+03 0.00e+00 5.26e-03 \n",
" 80| 4.89e-05 6.44e-04 3.52e-05 -6.49e+02 -6.49e+02 1.43e-13 2.09e-01 \n",
"----------------------------------------------------------------------------\n",
"Status: Solved\n",
"Timing: Solve time: 2.09e-01s\n",
"\tLin-sys: avg # CG iterations: 2.89, avg solve time: 2.59e-04s\n",
"\tCones: avg projection time: 2.29e-03s\n",
"----------------------------------------------------------------------------\n",
"Error metrics:\n",
"dist(s, K) = 1.7077e-05, dist(y, K*) = 2.2204e-16, s'y/|s||y| = -9.3095e-12\n",
"|Ax + s - b|_2 / (1 + |b|_2) = 4.8928e-05\n",
"|A'y + c|_2 / (1 + |c|_2) = 6.4429e-04\n",
"|c'x + b'y| / (1 + |c'x| + |b'y|) = 3.5160e-05\n",
"----------------------------------------------------------------------------\n",
"c'x = -648.8600, -b'y = -648.8143\n",
"============================================================================\n",
"Optimal value = -648.8599505343861\n"
]
}
],
"source": [
"import cvxpy as cvx\n",
"print(cvx.installed_solvers())\n",
"x = cvx.Variable(n, 1)\n",
"\n",
"obj = cvx.Minimize(cvx.sum_entries(-cvx.log(1 - A.T * x)) - \n",
" cvx.sum_entries(cvx.log(1 - cvx.square(x))))\n",
"prob = cvx.Problem(obj)\n",
"prob.solve(solver=\"SCS\", verbose=True)\n",
"x = x.value\n",
"print(\"Optimal value =\", prob.value)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Решение с помощью градиентного спуска"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"f = lambda x: -np.sum(np.log(1 - A.T.dot(x))) - np.sum(np.log(1 - x*x))\n",
"grad_f = lambda x: np.sum(A.dot(np.diagflat(1 / (1 - A.T.dot(x)))), \\\n",
" axis=1) + 2 * x / (1 - np.power(x, 2))\n",
"def my_callback(x, array):\n",
" array.append(x)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient norm = 6.001795377638296e-05 and optimal value = -648.8919249928828\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/alex/anaconda3/envs/cvxpy/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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"\n",
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"\n",
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"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
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" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
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"\n",
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"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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" this.ws.onmessage = this._make_on_message_function(this);\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
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"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
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"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
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" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
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"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
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"\n",
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" mouse_event_fn(event);\n",
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"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
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"\n",
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" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
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"\n",
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"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
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" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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