{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "UXWMdTw6dZXp"
            },
            "source": [
                "# \u30d9\u30f3\u30c1\u30de\u30fc\u30af\u95a2\u6570 \u6a19\u6e96\u8aa4\u5dee\u6a5f\u80fd \u306e\u4f7f\u3044\u65b9"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "view-in-github"
            },
            "source": [
                "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/OpenJij/OpenJijTutorial/blob/master/source/ja/002-Evaluation_errorbar.ipynb)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u95a2\u6570\u306b\u8ffd\u52a0\u3057\u305f\u6a19\u6e96\u8aa4\u5dee\u6a5f\u80fd\u306e\u4f7f\u3044\u65b9\u3092\u89e3\u8aac\u3057\u307e\u3059\u3002  "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "jARNE9idcsUF"
            },
            "source": [
                "## \u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3001\u30e2\u30c7\u30eb\u306e\u6e96\u5099\n",
                "OpenJij \u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u304b\u3089 Fork \u3057\u305f\u81ea\u5206\u306e\u30ea\u30dd\u30b8\u30c8\u30ea\u5185\u306b\u3001\u3055\u3089\u306b feature/error_bar \u30ea\u30dd\u30b8\u30c8\u30ea\u3092 branch \u3057\u307e\u3057\u305f\u3002\n",
                "\u6a19\u6e96\u8aa4\u5dee\u95a2\u6570\u304c\u5b9f\u88c5\u3055\u308c\u308b\u307e\u3067\u306f\u3001\u81ea\u5206\u306eGitHub\u304b\u3089\u3001 OpenJij \u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3001\u5b9f\u884c\u304c\u3067\u304d\u307e\u3059\u3002  \n",
                "\n",
                "\u53cd\u5f37\u78c1\u60271\u6b21\u5143\u30a4\u30b8\u30f3\u30b0\u30e2\u30c7\u30eb\u306e\u6e96\u5099"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "import random\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "import openjij as oj\n",
                "\n",
                "# \u53cd\u5f37\u78c1\u60271\u6b21\u5143\u30a4\u30b8\u30f3\u30b0\u30e2\u30c7\u30eb \u3092\u4f5c\u308b\n",
                "N = 30\n",
                "h = {0: -10}\n",
                "J = {(i, i+1): 1 for i in range(N-1)}"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "35hEh_F8fL7I"
            },
            "source": [
                "## \u30d9\u30f3\u30c1\u30de\u30fc\u30af\u95a2\u6570\n",
                "\u547c\u3073\u51fa\u3057\u65b9\u306f\u3001\u4eca\u307e\u3067\u901a\u308a\u3067\u3059\u3002\n",
                "\n",
                "\u5f15\u6570\u306b\u5909\u5316\u306f\u3042\u308a\u307e\u305b\u3093\u3002  \n",
                "\u8fd4\u308a\u5024\u3068\u3057\u3066\u3001\u4ee5\u4e0b\u306e\u8a08\u7b97\u7d50\u679c\u304c\u65b0\u305f\u306b\u8f9e\u66f8\u578b\u3067\u683c\u7d0d\u3055\u308c\u308b\u3088\u3046\u306b\u3057\u307e\u3057\u305f\u3002  \n",
                "\n",
                "- se_success_prob: \u6210\u529f\u78ba\u7387\u306e\u6a19\u6e96\u8aa4\u5dee\u306e\u30ea\u30b9\u30c8  \n",
                "      iteration \u56de\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u6642\u3001\u6210\u529f\u78ba\u7387\u306e\u671f\u5f85\u5024\u306e\u6a19\u6e96\u504f\u5dee  \n",
                "      step_num \u3054\u3068\u306b\u3001\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\n",
                "\n",
                "- se_residual_energy: \u6b8b\u7559\u30a8\u30cd\u30eb\u30ae\u30fc\u306e\u6a19\u6e96\u8aa4\u5dee\u306e\u30ea\u30b9\u30c8  \n",
                "      iteration \u56de\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u6642\u306e\u3001\u6b8b\u7559\u30a8\u30cd\u30eb\u30ae\u30fc\u5024\u306e\u5e73\u5747\u306e\u6a19\u6e96\u504f\u5dee  \n",
                "      step_num \u3054\u3068\u306b\u3001\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\n",
                "\n",
                "- se_lower_tts: TTS\u306e\u4e0b\u4f4d\u8aa4\u5dee\u306e\u30ea\u30b9\u30c8\n",
                "      \u6210\u529f\u78ba\u7387\u306e\u4e0a\u4f4d\u8aa4\u5dee\u3092\u57fa\u306b\u7b97\u51fa\u3057\u305fTTS\u306e\u4e0b\u4f4d\u8aa4\u5dee\n",
                "\n",
                "- se_upper_tts: TTS\u306e\u4e0a\u4f4d\u8aa4\u5dee\u306e\u30ea\u30b9\u30c8\n",
                "      \u6210\u529f\u78ba\u7387\u306e\u4e0b\u4f4d\u8aa4\u5dee\u3092\u57fa\u306b\u7b97\u51fa\u3057\u305fTTS\u306e\u4e0b\u4f4d\u8aa4\u5dee"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "# \u6700\u9069\u89e3\n",
                "correct_state = [(-1)**i for i in range(N)]\n",
                "\n",
                "# \u30b9\u30c6\u30c3\u30d7\u6570\u3068\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306e\u53cd\u5fa9\u6570\u3092\u4e0e\u3048\u307e\u3059\n",
                "step_num_list = list(range(10, 101, 10))  # [10, 20,,,, 100]\n",
                "iteration = 100\n",
                "\n",
                "# benchmark \u95a2\u6570\u3067 TTS \u6b8b\u7559\u30a8\u30cd\u30eb\u30ae\u30fc \u6210\u529f\u78ba\u7387 \u6a19\u672c\u5e73\u5747 \u6a19\u6e96\u8aa4\u5dee\u3092\u8a08\u7b97\n",
                "sampler = oj.SASampler(num_reads=iteration)\n",
                "result = oj.solver_benchmark(\n",
                "                      solver=lambda time, **args: sampler.sample_ising(h,J,num_sweeps=time), \n",
                "                      time_list=step_num_list, solutions=[correct_state], p_r=0.99\n",
                "            )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# seaborn \u3092\u4f7f\u3046\u3068\u30b0\u30e9\u30d5\u304c\u898b\u3084\u3059\u304f\u306a\u308b\n",
                "!pip install seaborn\n",
                "import seaborn as sns\n",
                "sns.set()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "b3rIcLQ8hNcE"
            },
            "source": [
                "### TTS \u6b8b\u7559\u30a8\u30cd\u30eb\u30ae\u30fc \u6210\u529f\u78ba\u7387\n",
                "TTS \u6b8b\u7559\u30a8\u30cd\u30eb\u30ae\u30fc \u6210\u529f\u78ba\u7387 \u306e\u53ef\u8996\u5316\u306f\u4eca\u307e\u3067\u901a\u308a\u3067\u304d\u307e\u3059\u3002"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Success probability')"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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                            "      <!-- 0.10 -->\n",
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                            "       <use xlink:href=\"#ArialMT-48\"/>\n",
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                            "</svg>\n"
                        ],
                        "text/plain": [
                            "<Figure size 1080x216 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, (axL,axC,axR) = plt.subplots(ncols=3, figsize=(15,3))\n",
                "plt.subplots_adjust(wspace=0.4)\n",
                "\n",
                "fontsize = 10\n",
                "axL.plot(result['time'], result['tts'], color = 'black')\n",
                "axL.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axL.set_ylabel('TTS', fontsize=fontsize)\n",
                "\n",
                "axC.plot(result['time'], result['residual_energy'], color = 'black')\n",
                "axC.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axC.set_ylabel('Residual energy', fontsize=fontsize)\n",
                "\n",
                "axR.plot(result['time'], result['success_prob'], color = 'black')\n",
                "axR.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axR.set_ylabel('Success probability', fontsize=fontsize)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "colab_type": "text",
                "id": "qcjyWsVVhif0"
            },
            "source": [
                "### \u6a19\u6e96\u8aa4\u5dee\n",
                "\u6a19\u6e96\u8aa4\u5dee\u306f\u3001se_success_prob\u3068se_residual_energy\u306b\u683c\u7d0d\u3055\u308c\u3066\u3044\u307e\u3059\u3002  \n",
                "\u3053\u308c\u3089\u3092\u4f7f\u3048\u3070\u3001\u6a19\u6e96\u8aa4\u5dee\u306e\u30a8\u30e9\u30fc\u30d0\u30fc\u3092\u8868\u793a\u3067\u304d\u307e\u3059\u3002"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[13388.196321142404, 9911.102719950344, 7710.473222454254, 11779.439837899115, 14848.876789318652, 16224.713930223505, 23163.040767149592, 34171.31829135721, 13500.864960678293, 33035.50440761748]\n",
                        "[10060.731171089232, 5437.479680351055, 3893.4360537639604, 5714.889476543427, 5937.451546843923, 6243.406315991658, 6459.91006968379, 9529.994155075381, 4097.1222783392495, 12231.748228729884]\n",
                        "[2.7689475811345194, 2.280882338575193, 2.0175993335414915, 2.060106891411318, 1.9508092079074686, 1.8214213564070665, 1.8063859784194247, 1.6497321484459715, 1.639290312077982, 1.50137983673067]\n",
                        "[0.14070529413628965, 0.27265992434429076, 0.30151134457776363, 0.31446603773522014, 0.3684529491774706, 0.3775251680686369, 0.4351941398892446, 0.4351941398892446, 0.42295258468165076, 0.38612291966536916]\n"
                    ]
                }
            ],
            "source": [
                "# \u6a19\u6e96\u8aa4\u5dee\u306e\u78ba\u8a8d\n",
                "print(result['se_upper_tts'])\n",
                "print(result['se_lower_tts'])\n",
                "print(result['se_residual_energy'])\n",
                "print(result['se_success_prob'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Text(0, 0.5, 'Success probability')"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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                            "<Figure size 1080x216 with 3 Axes>"
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                "fig, (axL,axC,axR) = plt.subplots(ncols=3, figsize=(15,3))\n",
                "plt.subplots_adjust(wspace=0.4)\n",
                "\n",
                "fontsize = 10\n",
                "axL.plot(result['time'], result['tts'])\n",
                "axL.errorbar(result['time'], result['tts'], yerr = (result['se_lower_tts'],result['se_upper_tts']), capsize=5, fmt='o', markersize=5, ecolor='black', markeredgecolor = \"black\", color='w')\n",
                "axL.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axL.set_ylabel('TTS', fontsize=fontsize)\n",
                "\n",
                "axC.plot(result['time'], result['residual_energy'])\n",
                "axC.errorbar(result['time'], result['residual_energy'], yerr = result['se_residual_energy'], capsize=5, fmt='o', markersize=5, ecolor='black', markeredgecolor = \"black\", color='w')\n",
                "axC.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axC.set_ylabel('Residual energy', fontsize=fontsize)\n",
                "\n",
                "axR.plot(result['time'], result['success_prob'])\n",
                "axR.errorbar(result['time'], result['success_prob'], yerr = result['se_success_prob'], capsize=5, fmt='o', markersize=5, ecolor='black', markeredgecolor = \"black\", color='w')\n",
                "axR.set_xlabel('annealing time', fontsize=fontsize)\n",
                "axR.set_ylabel('Success probability', fontsize=fontsize)"
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