{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "線形最小2乗法(LeastSquareFit)\n", "
\n", "
\n", "
\n", "file:/Users/bob/Github/TeamNishitani/jupyter_num_calc/leastsquarefit\n", "
\n", "https://github.com/daddygongon/jupyter_num_calc/tree/master/notebooks_python\n", "
\n", "cc by Shigeto R. Nishitani 2017-19 \n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# pythonによる最小2乗法\n", "\n", "前章では，データに多項式を完全にフィットする補間についてみた．今回は，近似的にフィットする最小二乗法について詳しくみていく．図のようなデータに直線をフィットする場合を考えよう．\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## python code\n", "\n", "x = [1,2,3,4], y=[0,5,15,24]に$y=a0+a1\\,x$をフィットする例を考える．\n", "pythonのcodeは以下の通り．" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-9.5 8.2]\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "\n", "def f(x, a0, a1): #, a2):\n", " return a0 + a1*x #+ a2*x**2\n", "\n", "xdata = np.array([1,2,3,4])\n", "ydata = np.array([0,5,15,24])\n", "plt.plot(xdata,ydata, 'o', color='r')\n", "\n", "params, cov = curve_fit(f, xdata, ydata)\n", "print(params)\n", "\n", "x =np.linspace(0,4,20)\n", "y = f(x,params[0],params[1]) #,params[2])\n", "plt.plot(x,y, color='b')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "結果，a0=-9.5, a1=8.2にfitされることがわかる．" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 最小2乗法の原理\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "もっとも簡単な例で原理を解説する．近似関数として，\n", "\n", "$$\n", "F(x) = a_0+a_1\\,x\n", "$$\n", "という直線近似を考える．もっともらしい関数は$N$点の測定データとの差$d_i = F(x_i)-y_i$を最小にすればよさそうであるが，これはプラスマイナスですぐに消えて不定になる．そこで，\n", "\n", "$$\n", "\\chi^{2}=\\sum_i^N d_i^2=\\sum_i^N\\left(a_0+a_1\\,x_i-y_i\\right)^2\n", "$$\n", "という関数を考える．この$\\chi^2$(カイ二乗)関数が，$a_0, a_1$をパラメータとして変えた時に最小となる$a_0, a_1$を求める．これは，それらの微分がそれぞれ0となる場合である．これは$\\chi^2$の和$\\sum$(sum)の中身を展開し，\n", "\n", " \n", "\n", "$\\chi^2=$\n", "\n", " \n", "\n", "$a_0, a_1$でそれぞれ微分すれば\n", "\n", " \n", "\n", "$ \\frac{\\partial}{\\partial a_0} \\chi^2=$\n", "\n", " \n", "\n", "$ \\frac{\\partial}{\\partial a_1} \\chi^2=$\n", "\n", " \n", "\n", "という$a_0, a_1$を未知変数とする2元の連立方程式が得られる．これは前に説明した通り逆行列で解くことができる．\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $\\chi^2$の極小値から(2変数の例)\n", "\n", "先ほどの例をもとに何をしているか別の角度からみる．\n", "データを関数に入れてsumをとると次のような関数が得られる．" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(a0 + a1)**2 + (a0 + 2*a1 - 5)**2 + (a0 + 3*a1 - 15)**2 + (a0 + 4*a1 - 24)**2\n", "4*a0**2 + 20*a0*a1 - 88*a0 + 30*a1**2 - 302*a1 + 826\n" ] } ], "source": [ "%matplotlib notebook\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sympy import *\n", "\n", "a0, a1 = symbols('a0, a1')\n", "def func(x):\n", " return a0+a1*x\n", "\n", "def z_surf(xx,yy):\n", " sum = 0\n", " for i in range(0,4):\n", " tmp = xx[i] - yy[i]\n", " sum = sum + tmp*tmp\n", " return sum\n", "\n", "x1 = np.array([1,2,3,4])\n", "y1 = np.array([0,5,15,24])\n", "eq = z_surf(func(x1),y1)\n", "print(eq)\n", "print(expand(eq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "これはa0,a1を変数とする関数となっている．\n", "データ点(xi,yi)はすでに数値を持っており，未知なのはa0,a1である．\n", "そうすると$\\chi^2(a0,a1)$, つまりa0,a1をパラメータとして，$\\chi^2$の値をz軸とする3次元関数とみなすことができて，それをplotすると次の通り．" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\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(\n", " '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", "\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 = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(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 (fig.ratio !== 1) {\n", " fig.send_message('set_device_pixel_ratio', {\n", " device_pixel_ratio: fig.ratio,\n", " });\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 = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\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 toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\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", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\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", "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", "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], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\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", " fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\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", " var img = evt.data;\n", " if (img.type !== 'image/png') {\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", " img.type = 'image/png';\n", " }\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", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\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(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\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(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from https://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", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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", " * https://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", " }\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", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\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", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, 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", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "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.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\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", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(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 = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", " '';\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 / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\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 () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\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", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('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", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\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(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "def f(a0, a1):\n", " return 4*a0**2 + 20*a0*a1 - 88.0*a0 + 30*a1**2 - 302.0*a1 + 826.0\n", " \n", "a0 = np.arange(-20, 20, 5)\n", "a1 = np.arange(-20, 20, 5)\n", "A0, A1 = np.meshgrid(a0, a1)\n", "Z1 = f(A0, A1)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", "ax.plot_wireframe(A0, A1, Z1) \n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a0=-9.5, a1=8.2あたりに最小値があるはずですが．．．見にくいよね．\n", "こういうのをsteepな関数って言いますが，それが後で述べる特異値分解を使わなければいけない理由です．\n", "値が微妙でなければ，微分して0において，連立方程式とみなして解くことができます．\n", "それが，上の「最小2乗法の原理」で述べた解法になります．" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 正規方程式(Normal Equations)による解\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "より一般的な場合の最小二乗法の解法を説明する．先程の例では1次の多項式を近似関数とした．これをより一般的な関数，例えば，$\\sin, \\cos, \\tan, \\exp, \\sinh$などとする．これを線形(linear)につないだ関数を\n", "\n", "$$\n", "F \\left(x \\right)=a _{0}\\sin \\left(x \\right)+a _{1}\\cos \\left(x \\right)+a _{2}\\exp \\left(-x \\right)+a _{3}\\sinh \\left(x \\right)+\\cdots ={\\sum_{k=1}^{M}}a _{k }X _{k }\\left(x \\right)\n", "$$\n", "ととる．実際には，$X_k(x)$はモデルや，多項式の高次項など論拠のある関数列をとる．これらを基底関数(base functions)と呼ぶ．ここで線形といっているのは，パラメータ$a_k$について線形という意味である．このような，より一般的な基底関数を使っても，$\\chi^2$関数は\n", "\n", "$$\n", "{\\chi}^{2}=\\sum _{i=1}^{N} \\left( F \\left( x_{{i}} \\right) -y_{{i}} \\right) ^{2}\n", "=\\sum _{i=1}^{N} \\left( \\sum _{k=1}^{M}a_{{k}}X_{{k}} \\left( x_{{i}} \\right) -y_{{i}} \\right) ^{2}\n", "$$\n", "と求めることができる．\n", "この関数を，$a_k$を変数とする関数とみなす．\n", "この関数が最小値を取るのは，\n", "$\\chi^2$を$M$個の$a_k$で偏微分した式がすべて0となる場合である．\n", "これを実際に求めてみると，\n", "\n", "$$\n", "\\sum _{i=1}^{N} \\left( \\sum _{j=1}^{M}a_{{j}}X_{{j}} \\left( x_{{i}} \\right) -y_{{i}} \\right) X_{{k}} \\left( x_{{i}} \\right) =0\n", "$$\n", "となる．ここで，$k = 1..M$の$M$個の連立方程式である．この連立方程式を最小二乗法の正規方程式(normal equations)と呼ぶ．\n", "\n", "上記の記法のままでは，ややこしいので，行列形式で書き直す．$N \\times M$で，各要素を\n", "\n", "$$\n", "A_{ij} = X_j(x_i)\n", "$$\n", "とする行列$A$を導入する．この行列は，\n", "\n", "$$\n", "A=\\left[\n", "\\begin{array}{cccc}\n", "X_1(x_1) & X_2(x_1) & \\cdots & X_M(x_1) \\\\\n", "\\vdots & \\vdots & \\cdots & \\vdots \\\\\n", "\\vdots & \\vdots & \\cdots & \\vdots \\\\\n", "\\vdots & \\vdots & \\cdots & \\vdots \\\\\n", "X_1(x_N) & X_2(x_N) & \\cdots & X_M(x_N) \n", "\\end{array}\n", "\\right]\n", "$$\n", "となる．これをデザイン行列と呼ぶ．すると先程の正規方程式は，\n", "\n", "$$\n", "A^t . A . a = A^t . y\n", "$$\n", "で与えられる．$A^t$は行列$A$の転置(transpose)\n", "\n", "$$\n", "A^t = A_{ij}^t = A_{ji}\n", "$$\n", "を意味し，得られた行列は，$M \\times N$である．$a, y$はそれぞれ，\n", "\n", "$$\n", "a=\\left[\n", "\\begin{array}{c}\n", "a_1\\\\a_2\\\\\\vdots\\\\a_M\n", "\\end{array}\n", "\\right],\\,\n", "y=\\left[\n", "\\begin{array}{c}\n", "y_1\\\\y_2\\\\\\vdots\\\\y_N\n", "\\end{array}\n", "\\right]\n", "$$\n", "である．\n", "\n", "$M = 3, N = 25$として行列の次元だけで表現すると，\n", "\n", "$$\n", "\\left[\n", "\\begin{array}{ccccc}\n", "& & \\cdots & &\\\\\n", "\\cdots & \\cdots & \\cdots & \\cdots & \\cdots \\\\\n", "& & \\cdots & &\\\\\n", "\\end{array}\n", "\\right]\n", "\\left[\n", "\\begin{array}{ccc}\n", "& \\vdots &\\\\\n", "& \\vdots &\\\\\n", "\\cdots & \\cdots & \\cdots\\\\\n", "& \\vdots &\\\\\n", "& \\vdots &\\\\\n", "\\end{array}\n", "\\right]\n", "\\left[\n", "\\begin{array}{c}\n", "\\vdots\\\\\n", "\\vdots\\\\\n", "\\vdots\n", "\\end{array}\n", "\\right]\n", "=\n", "\\left[\n", "\\begin{array}{ccccc}\n", "& & \\cdots & &\\\\\n", "\\cdots & \\cdots & \\cdots & \\cdots & \\cdots \\\\\n", "& & \\cdots & &\\\\\n", "\\end{array}\n", "\\right]\n", "\\left[\n", "\\begin{array}{c}\n", "\\vdots\\\\\n", "\\vdots\\\\\n", "\\vdots\\\\\n", "\\vdots\\\\\n", "\\vdots\n", "\\end{array}\n", "\\right]\n", "$$\n", "となる．これは少しの計算で$3 \\times 3$の逆行列を解く問題に変形できる．\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## python codeによる具体例 \n", "\n", "4点のデータに対して，2次関数つまり3個のパラメータでfitする．\n", "その場合，デザイン行列は4行3列になる．\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "array([[ 1., 1., 1.],\n", " [ 1., 2., 4.],\n", " [ 1., 3., 9.],\n", " [ 1., 4., 16.]])\n" ] }, { "data": { "text/plain": [ "array([-4.5, 3.2, 1. ])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from pprint import pprint\n", "import scipy.linalg as linalg\n", "\n", "xdata=np.array([1,2,3,4])\n", "ydata=np.array([0,5,15,24])\n", "\n", "def ff(x,i):\n", " return x**i\n", "\n", "Av = np.zeros([4,3])\n", "for i in range(0,3):\n", " for j in range(0,4):\n", " Av[j][i]=ff(xdata[j],i)\n", "\n", "pprint(Av)\n", "\n", "Ai = linalg.inv(np.dot(np.transpose(Av),Av))\n", "b = np.dot(np.transpose(Av),ydata)\n", "np.dot(Ai,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 特異値分解(Singular Value Decomposition)による解\n", "\n", "\n", "正規方程式を解くときには，少し注意が必要である．単純な逆行列による解法では，間違った答えに行き着く可能性が高い．より信頼性の高い方法では，特異値分解を用いる．正規方程式での共分散行列,特異値分解の導出や標準偏差との関係はNumRecipeを参照せよ．" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "array([[ 1., 1., 1.],\n", " [ 1., 2., 4.],\n", " [ 1., 3., 9.],\n", " [ 1., 4., 16.]])\n", "array([19.62136402, 1.71206987, 0.26625288])\n", "array([[19.62136402, 0. , 0. ],\n", " [ 0. , 1.71206987, 0. ],\n", " [ 0. , 0. , 0.26625288],\n", " [ 0. , 0. , 0. ]])\n", "[[0.05096486 0. 0. 0. ]\n", " [0. 0.58408831 0. 0. ]\n", " [0. 0. 3.75582793 0. ]]\n" ] }, { "data": { "text/plain": [ "array([-4.5, 3.2, 1. ])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from pprint import pprint\n", "import scipy.linalg as linalg\n", "\n", "xdata=np.array([1,2,3,4])\n", "ydata=np.array([0,5,15,24])\n", "\n", "#def f(x,a1,a2,a3):\n", "# return a1+a2*x+a3*x**2\n", "def ff(x,i):\n", " return x**i\n", "\n", "Av = np.zeros([4,3])\n", "for i in range(0,3):\n", " for j in range(0,4):\n", " Av[j][i]=ff(xdata[j],i)\n", "m,n = Av.shape\n", "pprint(Av)\n", "\n", "U, s, Vs = linalg.svd(Av)\n", "pprint(s)\n", "S = linalg.diagsvd(s,m,n)\n", "pprint(S)\n", "iS = np.zeros([3,4])\n", "for i in range(0,3):\n", " iS[i][i] = 1.0/s[i]\n", "print(iS)\n", "left = np.dot(np.transpose(Vs),iS)\n", "right= np.dot(np.transpose(U),ydata)\n", "np.dot(left,right)\n", "#print(right)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# scipy.linalg.lstsq\n", "\n", "scipy.linalg.lstsqによるcurve fitについて紹介しておく.\n", "あらかじめ，デザイン行列$A$を作っておいて，これを\n", "$$\n", "A x = b\n", "$$\n", "とみなした場合の$x$について解く．\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 1. 1.]\n", " [ 1. 2. 4.]\n", " [ 1. 3. 9.]\n", " [ 1. 4. 16.]]\n", "[-4.5 3.2 1. ] 1.7999999999999976 3 [19.62136402 1.71206987 0.26625288]\n" ] } ], "source": [ "import numpy as np\n", "from pprint import pprint\n", "import scipy.linalg as linalg\n", "\n", "xdata=np.array([1,2,3,4])\n", "ydata=np.array([0,5,15,24])\n", "\n", "def ff(x,i):\n", " return x**i\n", "\n", "Av = np.zeros([4,3])\n", "for i in range(0,3):\n", " for j in range(0,4):\n", " Av[j][i]=ff(xdata[j],i)\n", "print(Av)\n", "\n", "c, resid, rank, sigma = linalg.lstsq(Av, ydata)\n", "print(c,resid,rank,sigma)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 正規方程式によるのも．．．\n", "\n", "lstsqは~~正規方程式~~によるのと同じかな．SVD!!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-4.5, 3.2, 1. ])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ai = linalg.inv(np.dot(np.transpose(Av),Av))\n", "b = np.dot(np.transpose(Av), ydata)\n", "np.dot(Ai,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2次元曲面へのフィット\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "先程の一般化をより発展させると，3次元$(x_i, y_i, z_i)$で提供されるデータへの，2次元平面でのフィットも可能となる．2次元の単純な曲面は，方程式を使って，\n", "\n", "$$\n", "F(x, y) = a_1+a_2\\,x+a_3\\,y+a_4\\,xy+a_5\\,x^2+a_6\\,y^2\n", "$$\n", "となる．デザイン行列の$i$行目の要素は，\n", "\n", "$$\n", "[1,\\, x_i,\\, y_i,\\, x_i \\, y_i,\\, x_i^2,\\, y_i^2]\n", "$$\n", "として，それぞれ求める．このデータの変換の様子をpythonスクリプトで詳しく示した．後は，通常の正規方程式を解くようにすれば，このデータを近似する曲面を定めるパラメータ$a_1, a_2, \\cdots,a_6$が求まる．最小二乗法はパラメータ$a_k$について線形であればよい．\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 具体例 \n", "実際のデータ解析での例．データの座標をx,y,zで用意して，scipy.linalgのlinalg.lstsqでfitしている．\n", "正規方程式による解法，つまり逆行列で求めた値と一致していることを確認してください．\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.001, -0.001, -0.001, -0.001, -0.001, -0.0005, -0.0005, -0.0005, -0.0005, -0.0005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.001, 0.001, 0.001, 0.001, 0.001]\n", "[-0.001, -0.0005, 0.0, 0.0005, 0.001, -0.001, -0.0005, 0.0, 0.0005, 0.001, -0.001, -0.0005, 0.0, 0.0005, 0.001, -0.001, -0.0005, 0.0, 0.0005, 0.001, -0.001, -0.0005, 0.0, 0.0005, 0.001]\n" ] } ], "source": [ "import numpy as np\n", "z = np.array([0.000046079702088, 0.000029479057275,\n", " 0.000025769637830, 0.000034951410953, 0.000057024385455, 0.000029485453808,\n", " 0.000011519913869, 0.000006442404299, 0.000014252898382, 0.000034951410953,\n", " 0.000025769637773, 0.000006442404242, 0.000000000000057, 0.000006442404242,\n", " 0.000025769637773, 0.000034932221524, 0.000014246501905, 0.000006442404299,\n", " 0.000011519913926, 0.000029479057332, 0.000056973214100, 0.000034932221467,\n", " 0.000025769637773, 0.000029485453808, 0.000046079702031])\n", "x = []\n", "y = []\n", "for i in range(-2,3):\n", " for j in range(-2,3):\n", " x.append(i*0.0005)\n", " y.append(j*0.0005)\n", "print(x)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", "ax.scatter(np.array(x),np.array(y),z) \n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "array([-9.18521222e-13, -6.39644676e-06, 6.39644220e-06, -5.45955358e+00,\n", " 2.57696284e+01, 2.57696284e+01])\n" ] }, { "data": { "text/plain": [ "array([-9.18525713e-13, -6.39644676e-06, 6.39644220e-06, -5.45955358e+00,\n", " 2.57696284e+01, 2.57696284e+01])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pprint import pprint\n", "import scipy.linalg as linalg\n", "\n", "n = z.size\n", "n_j = 6\n", "bb=np.zeros([n])\n", "A=np.zeros([n,n_j])\n", "for i in range(0,n):\n", " A[i,0]=1\n", " A[i,1]=x[i]\n", " A[i,2]=y[i]\n", " A[i,3]=x[i]*y[i]\n", " A[i,4]=x[i]**2\n", " A[i,5]=y[i]**2\n", " bb[i]=z[i]\n", "\n", "c, resid, rank, sigma = linalg.lstsq(A, bb)\n", "pprint(c)\n", "\n", "Ai = linalg.inv(np.dot(np.transpose(A),A))\n", "b = np.dot(np.transpose(A),bb)\n", "np.dot(Ai,b)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\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(\n", " '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", "\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 = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(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 (fig.ratio !== 1) {\n", " fig.send_message('set_device_pixel_ratio', {\n", " device_pixel_ratio: fig.ratio,\n", " });\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 = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\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 toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\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", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\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", "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", "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], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\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", " fig.rubberband_canvas.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\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", " var img = evt.data;\n", " if (img.type !== 'image/png') {\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", " img.type = 'image/png';\n", " }\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", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\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(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\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(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from https://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", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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", " * https://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", " }\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", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\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", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, 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", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "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.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\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", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(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 = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", " '';\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 / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\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 () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\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", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('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", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\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(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook \n", "# for jupyter notebook\n", "# %matplotlib inline # for vscode\n", "\n", "\n", "def z_surf(xx,yy):\n", " val = c[0] + c[1]*xx + c[2]*yy\n", " val += c[3]*xx*yy + c[4]*xx**2\n", " val += c[5]*yy**2\n", " return val\n", "\n", "x1 = np.arange(-0.001, 0.00125, 0.00025)\n", "y1 = np.arange(-0.001, 0.00125, 0.00025)\n", "X, Y = np.meshgrid(x1, y1)\n", "Z1 = z_surf(X,Y)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection=\"3d\")\n", "ax.scatter(np.array(x),np.array(y),z, color='r') \n", "ax.plot_wireframe(X,Y,Z1) \n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 課題\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1次元の線形最小二乗法\n", "\n", "次の4点のデータを$y = a_0+a_1 x+a_2 x^2$で近似せよ(2006年度期末試験)．\n", "```python\n", "xdata = np.array([1,2,3,4])\n", "ydata = np.array([1,3,4,10])\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2次元の最小二乗フィット\n", "\n", "以下のデータを\n", "\n", "$$\n", "f(x, y) = a_0+a_1 x+a_2 y+a_3 xy\n", "$$\n", "で近似せよ\n", "```python\n", " x, y, z\n", "-1, -1, 2.00000\n", "-1, 0, 0.50000\n", "-1, 1, -1.00000\n", " 0, -1, 0.50000\n", " 0, 0, 1.00000\n", " 0, 1, 1.50000\n", " 1, -1, -1.00000\n", " 1, 0, 1.50000\n", " 1, 1, 4.00000\n", "```\n" ] }, { "attachments": { "image.png": { "image/png": "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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "結果は以下の通り．鞍点になってます．\n", "\n", "![image.png](attachment:image.png)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": { "height": "12px", "width": "252px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "vscode": { "interpreter": { "hash": "f3f87633aac09da3bda522f97956bee375b5501d1579e6458804e567301cb62a" } } }, "nbformat": 4, "nbformat_minor": 2 }