{"id":534,"date":"2018-05-30T00:50:59","date_gmt":"2018-05-29T15:50:59","guid":{"rendered":"http:\/\/www.typea.info\/blog\/index.php\/2018\/05\/30\/python_7\/"},"modified":"2018-05-30T00:50:59","modified_gmt":"2018-05-29T15:50:59","slug":"python_7","status":"publish","type":"post","link":"https:\/\/www.typea.info\/blog\/index.php\/2018\/05\/30\/python_7\/","title":{"rendered":"Python \u6570\u5b66\u3001\u7d71\u8a08\u3001\u6a5f\u68b0\u5b66\u7fd2\uff1a\u6b63\u898f\u5206\u5e03\u3001\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570"},"content":{"rendered":"<p><a href=\"https:\/\/www.amazon.co.jp\/Python%E3%81%AB%E3%82%88%E3%82%8B%E7%B5%B1%E8%A8%88%E5%88%86%E6%9E%90%E5%85%A5%E9%96%80-%E5%B1%B1%E5%86%85-%E9%95%B7%E6%89%BF\/dp\/4274222349\/ref=as_li_ss_il?ie=UTF8&amp;qid=1527608967&amp;sr=8-3&amp;keywords=python+%E7%B5%B1%E8%A8%88&amp;linkCode=li2&amp;tag=typea09-22&amp;linkId=897afd5f977ab8d4c13c751b17fc8f5c\" target=\"_blank\"><img decoding=\"async\" src=\"\/\/ws-fe.amazon-adsystem.com\/widgets\/q?_encoding=UTF8&amp;ASIN=4274222349&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=JP&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=typea09-22\" border=\"0\"><\/a><img loading=\"lazy\" decoding=\"async\" width=\"1\" height=\"1\" style=\"margin: 0px !important; border: currentcolor !important; border-image: none !important;\" alt=\"\" src=\"https:\/\/ir-jp.amazon-adsystem.com\/e\/ir?t=typea09-22&amp;l=li2&amp;o=9&amp;a=4274222349\" border=\"0\"><\/p>\n<h2>1.\u6b63\u898f\u5206\u5e03\u3068\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570<\/h2>\n<h3>1.1 \u6b63\u898f\u5206\u5e03<\/h3>\n<h3><\/h3>\n<ul>\n<li>\u6b63\u898f\u5206\u5e03\u306f\u3001<a href=\"https:\/\/ja.wikipedia.org\/wiki\/%E7%A2%BA%E7%8E%87%E5%AF%86%E5%BA%A6%E9%96%A2%E6%95%B0\">\u6570\u5f0f(\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570)<\/a>\u3067\u8868\u73fe\u3067\u304d\u308b\u3002<\/li>\n<ul>\n<li>\u30c7\u30fc\u30bf\u306e\u4e2d\u5fc3\u3092\u793a\u3059\u5e73\u5747(\u03bc)\u3068\u30d0\u30e9\u30c4\u30ad\u306e\u5927\u304d\u3055\u3092\u793a\u3059\u6a19\u6e96\u504f\u5dee(\u03c3)\u304c\u308f\u304b\u308c\u3070\u3001\u6b63\u898f\u5206\u5e03\u306e\u5f62\u304c\u6c7a\u307e\u308b<\/li>\n<\/ul>\n<li>\u5e73\u5747\u5024(\u03bc)\u3092\u4e2d\u5fc3\u306b\u5de6\u53f3\u5bfe\u79f0\u306e\u91e3\u308a\u9418\u578b\u306b\u5206\u5e03<\/li>\n<li>\u5e73\u5747\u5024\u3001\u4e2d\u592e\u5024\u3001\u6700\u983b\u5024\u304c\u4e00\u81f4<\/li>\n<li>\u30b0\u30e9\u30d5\u3067\u56f2\u307e\u308c\u305f\u90e8\u5206\u304c\u78ba\u7387\u3092\u8868\u3057\u3001\u5168\u4f53\u306e\u5408\u8a08\u304c1\u3068\u306a\u308b<\/li>\n<li>\u5bfe\u5fdc\u3059\u308b\u9762\u7a4d\u3092\u6c42\u3081\u308b\u3068\u78ba\u7387\u304c\u6c42\u307e\u308b<\/li>\n<\/ul>\n<h3>1.2 \u03c3\u533a\u9593<\/h3>\n<p>\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u306e\u7bc4\u56f2\u306b\u3064\u3044\u3066\u5b9a\u7a4d\u5206(\u9762\u7a4d\u3092\u6c42\u3081\u308b)\u3053\u3068\u3067\u3001\u78ba\u7387\u304c\u6c42\u307e\u308b\u3002<\/p>\n<table border=\"0\" cellspacing=\"0\" cellpadding=\"2\">\n<tbody>\n<tr>\n<td valign=\"top\">\u533a\u9593<\/td>\n<td valign=\"top\">\u7bc4\u56f2\u958b\u59cb<\/td>\n<td valign=\"top\">\u7bc4\u56f2\u7d42\u4e86<\/td>\n<td valign=\"top\">\u6b63\u898f\u5206\u5e03\u306e\u5834\u5408\u53ce\u307e\u308b\u78ba\u7387<\/td>\n<\/tr>\n<tr>\n<td valign=\"top\">1\u03c3 \u533a\u9593<\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) &#8211; \u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) + \u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u7d04 68.27%<\/td>\n<\/tr>\n<tr>\n<td valign=\"top\">2\u03c3 \u533a\u9593<\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) &#8211; 2*\u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) + 2*\u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u7d04 95.45%<\/td>\n<\/tr>\n<tr>\n<td valign=\"top\">3\u03c3 \u533a\u9593<\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) &#8211; 3*\u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u5e73\u5747(\u03bc) + 3*\u6a19\u6e96\u504f\u5dee(\u03c3) <\/td>\n<td valign=\"top\">\u7d04 99.73%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>1.3 \u8a66\u3057\u3066\u307f\u308b<\/h3>\n<p><a href=\"https:\/\/www.typea.info\/blog\/index.php\/2018\/05\/24\/python_6\">\u57fa\u672c\u7d71\u8a08\u91cf\u306e\u78ba\u8a8d\u3067\u4f7f\u7528\u3057\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a> \u306b\u3064\u3044\u3066\u3001\u6b63\u898f\u5206\u5e03\u3068\u4eee\u5b9a\u3057\u30b0\u30e9\u30d5\u3092\u63cf\u753b\u3001\u307e\u305f\u3001\u03c3\u533a\u9593\u306e\u78ba\u7387\u3092\u5b9a\u7a4d\u5206\u306b\u3088\u308a\u8a08\u7b97\u3002<\/p>\n<pre class=\"prettyprint linenums\">from sklearn import datasets\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport numpy as np\r\nfrom scipy.stats import norm\r\nfrom scipy import integrate\r\n\r\n\r\ndef normal_dist():\r\n    \"\"\"\r\n    https:\/\/ja.wikipedia.org\/wiki\/%E7%A2%BA%E7%8E%87%E5%AF%86%E5%BA%A6%E9%96%A2%E6%95%B0\r\n    https:\/\/mathtrain.jp\/gaussdistribution\r\n    https:\/\/mathtrain.jp\/pmitsudo\r\n    <blockquote class=\"wp-embedded-content\" data-secret=\"Flst0CLAsr\"><a href=\"https:\/\/logics-of-blue.com\/%e7%a2%ba%e7%8e%87%e5%af%86%e5%ba%a6%e9%96%a2%e6%95%b0%e3%81%a8%e6%ad%a3%e8%a6%8f%e5%88%86%e5%b8%83\/\">\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u3068\u6b63\u898f\u5206\u5e03<\/a><\/blockquote><iframe loading=\"lazy\" title=\"&#8220;\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u3068\u6b63\u898f\u5206\u5e03&#8221; &#8212; Logics of Blue\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" src=\"https:\/\/logics-of-blue.com\/%e7%a2%ba%e7%8e%87%e5%af%86%e5%ba%a6%e9%96%a2%e6%95%b0%e3%81%a8%e6%ad%a3%e8%a6%8f%e5%88%86%e5%b8%83\/embed\/#?secret=Flst0CLAsr\" data-secret=\"Flst0CLAsr\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\r\n    :return:\r\n    \"\"\"\r\n    boston = datasets.load_boston()\r\n\r\n    df = pd.DataFrame(boston.target)\r\n    df.columns = ['Price']\r\n\r\n    x = df['Price']\r\n    # CSV\u306b\u51fa\u529b\r\n    # x.to_csv('~\/work\/house_price.csv')\r\n\r\n    import pprint\r\n    print(pprint.pprint(dir(x)))\r\n\r\n    fig = plt.figure()\r\n    # 2 * 1 \u306e \u30de\u30c8\u30ea\u30c3\u30af\u30b9 1\u756a\u76ee\u306e\u30bb\u30eb\u306b\u30b0\u30e9\u30d5\u3092\u63cf\u753b\r\n    ax = fig.add_subplot(2,1,1)\r\n    ax.set_title('Boston house price')\r\n    ax.hist(x, bins=int(round(math.sqrt(x.count()), 0)),\r\n            rwidth=0.8, label='house price(x $1,000)')\r\n\r\n    # 2 * 1 \u306e \u30de\u30c8\u30ea\u30c3\u30af\u30b9 2\u756a\u76ee\u306e\u30bb\u30eb\u306b\u30b0\u30e9\u30d5\u3092\u63cf\u753b\r\n    ax2 = fig.add_subplot(2, 1, 2)\r\n    nx = np.arange(df.min(), df.max(), 1.0)\r\n\r\n    # \u78ba\u7387\u5bc6\u5ea6\u95a2\u6570(probability density function\u3001PDF)\u3092 \u30e9\u30e0\u30c0\u306b\u30e9\u30c3\u30d7\u3057\u3001\r\n    # \u95a2\u6570\u306b\u3059\u308b(\u03c3\u533a\u9593\u7b97\u51fa\u7528)\r\n    pdf = lambda px : norm.pdf(px, loc=x.mean(), scale=x.std())\r\n\r\n    ax2.plot(nx, pdf(nx))\r\n\r\n    # \u03c3\u533a\u9593\u306e\u78ba\u7387\u3092\u7b97\u51fa(\u5b9a\u7a4d\u5206)\r\n    sig = []\r\n    for i in range(1, 4, 1):\r\n        s, s1e = integrate.quad(pdf, x.mean() - (x.std() * i), x.mean() + (x.std() * i))\r\n        sig.append(s)\r\n\r\n    label = \"\"\"\r\n     mean={0:f}\r\n     standard deviation={1:f}\r\n     1\u03c3 : {2:f} \r\n     2\u03c3 : {3:f}\r\n     3\u03c3 : {4:f}\r\n     \"\"\".format(\r\n        x.mean(),\r\n        x.std(),\r\n        sig[0], sig[1], sig[2])\r\n\r\n    ax2.text(27, 0.01, label)\r\n\r\n\r\n    plt.show()\r\n\r\nif __name__ == \"__main__\":\r\n    normal_dist()<\/pre>\n<p><a href=\"https:\/\/www.typea.info\/blog\/wp-content\/uploads\/image\/Python-_1475A\/normal.png\"><img loading=\"lazy\" decoding=\"async\" width=\"578\" height=\"434\" title=\"normal\" style=\"display: inline; background-image: none;\" alt=\"normal\" src=\"https:\/\/www.typea.info\/blog\/wp-content\/uploads\/image\/Python-_1475A\/normal_thumb.png\" border=\"0\"><\/a><\/p>\n<p><a href=\"https:\/\/www.amazon.co.jp\/Python%E3%83%87%E3%83%BC%E3%82%BF%E3%82%B5%E3%82%A4%E3%82%A8%E3%83%B3%E3%82%B9%E3%83%8F%E3%83%B3%E3%83%89%E3%83%96%E3%83%83%E3%82%AF-%E2%80%95Jupyter%E3%80%81NumPy%E3%80%81pandas%E3%80%81Matplotlib%E3%80%81scikit-learn%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%9F%E3%83%87%E3%83%BC%E3%82%BF%E5%88%86%E6%9E%90%E3%80%81%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-Jake-VanderPlas\/dp\/4873118417\/ref=as_li_ss_il?_encoding=UTF8&amp;pd_rd_i=4873118417&amp;pd_rd_r=e134abb3-6357-11e8-b88d-43678a1b55c8&amp;pd_rd_w=VAMZh&amp;pd_rd_wg=rP3Xn&amp;pf_rd_i=desktop-dp-sims&amp;pf_rd_m=AN1VRQENFRJN5&amp;pf_rd_p=3472031948783866574&amp;pf_rd_r=Q9WTG9Y0NW1E6XB8HAVA&amp;pf_rd_s=desktop-dp-sims&amp;pf_rd_t=40701&amp;psc=1&amp;refRID=Q9WTG9Y0NW1E6XB8HAVA&amp;linkCode=li2&amp;tag=typea09-22&amp;linkId=6393adde77dda91310177c84ddf274ab\" target=\"_blank\"><img decoding=\"async\" src=\"\/\/ws-fe.amazon-adsystem.com\/widgets\/q?_encoding=UTF8&amp;ASIN=4873118417&amp;Format=_SL160_&amp;ID=AsinImage&amp;MarketPlace=JP&amp;ServiceVersion=20070822&amp;WS=1&amp;tag=typea09-22\" border=\"0\"><\/a><img loading=\"lazy\" decoding=\"async\" width=\"1\" height=\"1\" style=\"margin: 0px !important; border: currentcolor !important; border-image: none !important !important;\" alt=\"\" src=\"https:\/\/ir-jp.amazon-adsystem.com\/e\/ir?t=typea09-22&amp;l=li2&amp;o=9&amp;a=4873118417\" border=\"0\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1.\u6b63\u898f\u5206\u5e03\u3068\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570 1.1 \u6b63\u898f\u5206\u5e03 \u6b63\u898f\u5206\u5e03\u306f\u3001\u6570\u5f0f(\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570)\u3067\u8868\u73fe\u3067\u304d\u308b\u3002 \u30c7\u30fc\u30bf\u306e\u4e2d\u5fc3\u3092\u793a\u3059\u5e73\u5747(\u03bc)\u3068\u30d0\u30e9\u30c4\u30ad\u306e\u5927\u304d\u3055\u3092\u793a\u3059\u6a19\u6e96\u504f\u5dee(\u03c3)\u304c\u308f\u304b\u308c\u3070\u3001\u6b63\u898f\u5206\u5e03\u306e\u5f62\u304c\u6c7a\u307e\u308b \u5e73\u5747\u5024(\u03bc)\u3092\u4e2d\u5fc3\u306b\u5de6\u53f3\u5bfe [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","_veu_custom_css":"","veu_display_promotion_alert":"","vkexunit_cta_each_option":"","footnotes":""},"categories":[35,86],"tags":[],"class_list":["post-534","post","type-post","status-publish","format-standard","hentry","category-math","category-statistics"],"veu_head_title_object":{"title":"","add_site_title":""},"_links":{"self":[{"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/posts\/534","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=534"}],"version-history":[{"count":0,"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/posts\/534\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=534"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=534"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.typea.info\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=534"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}