From 21d48678cd58cf7945f2ce3be49dd546d03f948d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BB=84=E9=BE=99?=
 <11727257+huanglong-2022@user.noreply.gitee.com>
Date: Wed, 26 Oct 2022 16:22:14 +0000
Subject: [PATCH 1/2] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20=E9=BB=84=E9=BE=99-?=
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---
 .../.keep"                                                        | 0
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diff --git "a/code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/.keep" "b/code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/.keep"
new file mode 100644
index 0000000..e69de29
-- 
Gitee


From c5cf5534ad69b1588765fab7782024fbed424ab1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BB=84=E9=BE=99?=
 <11727257+huanglong-2022@user.noreply.gitee.com>
Date: Wed, 26 Oct 2022 16:25:28 +0000
Subject: [PATCH 2/2] =?UTF-8?q?=E4=BD=9C=E4=B8=9A?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: 黄龙 <11727257+huanglong-2022@user.noreply.gitee.com>
---
 ...345\272\246\351\242\204\346\265\213.ipynb" | 5318 +++++++++++++++++
 1 file changed, 5318 insertions(+)
 create mode 100644 "code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213.ipynb"

diff --git "a/code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213.ipynb" "b/code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213.ipynb"
new file mode 100644
index 0000000..439c377
--- /dev/null
+++ "b/code/2022_autumn/\351\273\204\351\276\231-\345\237\272\344\272\216BP\347\245\236\347\273\217\347\275\221\347\273\234\347\232\204\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213/\351\253\230\347\206\265\345\220\210\351\207\221\347\241\254\345\272\246\351\242\204\346\265\213.ipynb"
@@ -0,0 +1,5318 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1cbf64b9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 Error 242.569920172063\n",
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+      "Epoch 8 Error 2.15140692084654\n",
+      "Epoch 9 Error 2.109162297411169\n",
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+      "Epoch 11 Error 2.0288843969466925\n",
+      "Epoch 12 Error 1.990721449652861\n",
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+      "Epoch 4750 Error 0.20729630591149967\n",
+      "Epoch 4751 Error 0.2072932036861752\n",
+      "Epoch 4752 Error 0.20729010215733157\n",
+      "Epoch 4753 Error 0.20728700132468336\n",
+      "Epoch 4754 Error 0.2072839011879467\n",
+      "Epoch 4755 Error 0.20728080174683697\n",
+      "Epoch 4756 Error 0.20727770300107046\n",
+      "Epoch 4757 Error 0.20727460495036368\n",
+      "Epoch 4758 Error 0.2072715075944327\n",
+      "Epoch 4759 Error 0.20726841093299428\n",
+      "Epoch 4760 Error 0.2072653149657655\n",
+      "Epoch 4761 Error 0.20726221969246342\n",
+      "Epoch 4762 Error 0.20725912511280478\n",
+      "Epoch 4763 Error 0.20725603122650785\n",
+      "Epoch 4764 Error 0.20725293803328979\n",
+      "Epoch 4765 Error 0.2072498455328682\n",
+      "Epoch 4766 Error 0.20724675372496212\n",
+      "Epoch 4767 Error 0.20724366260928936\n",
+      "Epoch 4768 Error 0.2072405721855682\n",
+      "Epoch 4769 Error 0.2072374824535176\n",
+      "Epoch 4770 Error 0.20723439341285638\n",
+      "Epoch 4771 Error 0.2072313050633039\n",
+      "Epoch 4772 Error 0.20722821740457914\n",
+      "Epoch 4773 Error 0.20722513043640195\n",
+      "Epoch 4774 Error 0.20722204415849152\n",
+      "Epoch 4775 Error 0.20721895857056824\n",
+      "Epoch 4776 Error 0.20721587367235206\n",
+      "Epoch 4777 Error 0.20721278946356322\n",
+      "Epoch 4778 Error 0.20720970594392274\n",
+      "Epoch 4779 Error 0.2072066231131506\n",
+      "Epoch 4780 Error 0.20720354097096838\n",
+      "Epoch 4781 Error 0.20720045951709687\n",
+      "Epoch 4782 Error 0.20719737875125713\n",
+      "Epoch 4783 Error 0.2071942986731715\n",
+      "Epoch 4784 Error 0.2071912192825608\n",
+      "Epoch 4785 Error 0.2071881405791475\n",
+      "Epoch 4786 Error 0.20718506256265373\n",
+      "Epoch 4787 Error 0.2071819852328015\n",
+      "Epoch 4788 Error 0.20717890858931368\n",
+      "Epoch 4789 Error 0.2071758326319127\n",
+      "Epoch 4790 Error 0.20717275736032176\n",
+      "Epoch 4791 Error 0.2071696827742637\n",
+      "Epoch 4792 Error 0.2071666088734618\n",
+      "Epoch 4793 Error 0.20716353565763998\n",
+      "Epoch 4794 Error 0.20716046312652156\n",
+      "Epoch 4795 Error 0.2071573912798305\n",
+      "Epoch 4796 Error 0.2071543201172911\n",
+      "Epoch 4797 Error 0.20715124963862744\n",
+      "Epoch 4798 Error 0.20714817984356457\n",
+      "Epoch 4799 Error 0.2071451107318263\n",
+      "Epoch 4800 Error 0.20714204230313796\n",
+      "Epoch 4801 Error 0.20713897455722463\n",
+      "Epoch 4802 Error 0.20713590749381172\n",
+      "Epoch 4803 Error 0.2071328411126243\n",
+      "Epoch 4804 Error 0.20712977541338887\n",
+      "Epoch 4805 Error 0.20712671039583044\n",
+      "Epoch 4806 Error 0.20712364605967523\n",
+      "Epoch 4807 Error 0.20712058240464948\n",
+      "Epoch 4808 Error 0.20711751943048012\n",
+      "Epoch 4809 Error 0.20711445713689325\n",
+      "Epoch 4810 Error 0.207111395523616\n",
+      "Epoch 4811 Error 0.20710833459037514\n",
+      "Epoch 4812 Error 0.20710527433689815\n",
+      "Epoch 4813 Error 0.20710221476291243\n",
+      "Epoch 4814 Error 0.20709915586814534\n",
+      "Epoch 4815 Error 0.20709609765232462\n",
+      "Epoch 4816 Error 0.20709304011517882\n",
+      "Epoch 4817 Error 0.20708998325643566\n",
+      "Epoch 4818 Error 0.20708692707582363\n",
+      "Epoch 4819 Error 0.2070838715730712\n",
+      "Epoch 4820 Error 0.20708081674790735\n",
+      "Epoch 4821 Error 0.20707776260006042\n",
+      "Epoch 4822 Error 0.2070747091292606\n",
+      "Epoch 4823 Error 0.20707165633523625\n",
+      "Epoch 4824 Error 0.20706860421771742\n",
+      "Epoch 4825 Error 0.20706555277643354\n",
+      "Epoch 4826 Error 0.20706250201111456\n",
+      "Epoch 4827 Error 0.20705945192149078\n",
+      "Epoch 4828 Error 0.2070564025072923\n",
+      "Epoch 4829 Error 0.2070533537682497\n",
+      "Epoch 4830 Error 0.20705030570409316\n",
+      "Epoch 4831 Error 0.2070472583145542\n",
+      "Epoch 4832 Error 0.20704421159936376\n",
+      "Epoch 4833 Error 0.20704116555825258\n",
+      "Epoch 4834 Error 0.20703812019095222\n",
+      "Epoch 4835 Error 0.2070350754971946\n",
+      "Epoch 4836 Error 0.20703203147671118\n",
+      "Epoch 4837 Error 0.20702898812923426\n",
+      "Epoch 4838 Error 0.20702594545449557\n",
+      "Epoch 4839 Error 0.20702290345222746\n",
+      "Epoch 4840 Error 0.20701986212216306\n",
+      "Epoch 4841 Error 0.20701682146403466\n",
+      "Epoch 4842 Error 0.2070137814775749\n",
+      "Epoch 4843 Error 0.20701074216251739\n",
+      "Epoch 4844 Error 0.20700770351859527\n",
+      "Epoch 4845 Error 0.20700466554554167\n",
+      "Epoch 4846 Error 0.20700162824309057\n",
+      "Epoch 4847 Error 0.2069985916109757\n",
+      "Epoch 4848 Error 0.2069955556489308\n",
+      "Epoch 4849 Error 0.20699252035669086\n",
+      "Epoch 4850 Error 0.20698948573398954\n",
+      "Epoch 4851 Error 0.2069864517805618\n",
+      "Epoch 4852 Error 0.20698341849614177\n",
+      "Epoch 4853 Error 0.20698038588046502\n",
+      "Epoch 4854 Error 0.20697735393326666\n",
+      "Epoch 4855 Error 0.2069743226542814\n",
+      "Epoch 4856 Error 0.20697129204324563\n",
+      "Epoch 4857 Error 0.2069682620998942\n",
+      "Epoch 4858 Error 0.20696523282396342\n",
+      "Epoch 4859 Error 0.2069622042151891\n",
+      "Epoch 4860 Error 0.2069591762733077\n",
+      "Epoch 4861 Error 0.20695614899805576\n",
+      "Epoch 4862 Error 0.2069531223891688\n",
+      "Epoch 4863 Error 0.20695009644638518\n",
+      "Epoch 4864 Error 0.20694707116944058\n",
+      "Epoch 4865 Error 0.20694404655807286\n",
+      "Epoch 4866 Error 0.2069410226120185\n",
+      "Epoch 4867 Error 0.20693799933101584\n",
+      "Epoch 4868 Error 0.20693497671480238\n",
+      "Epoch 4869 Error 0.20693195476311563\n",
+      "Epoch 4870 Error 0.20692893347569383\n",
+      "Epoch 4871 Error 0.20692591285227527\n",
+      "Epoch 4872 Error 0.20692289289259796\n",
+      "Epoch 4873 Error 0.2069198735964013\n",
+      "Epoch 4874 Error 0.20691685496342305\n",
+      "Epoch 4875 Error 0.20691383699340232\n",
+      "Epoch 4876 Error 0.206910819686079\n",
+      "Epoch 4877 Error 0.20690780304119175\n",
+      "Epoch 4878 Error 0.20690478705847998\n",
+      "Epoch 4879 Error 0.2069017717376835\n",
+      "Epoch 4880 Error 0.20689875707854222\n",
+      "Epoch 4881 Error 0.20689574308079578\n",
+      "Epoch 4882 Error 0.20689272974418477\n",
+      "Epoch 4883 Error 0.2068897170684493\n",
+      "Epoch 4884 Error 0.20688670505332993\n",
+      "Epoch 4885 Error 0.2068836936985673\n",
+      "Epoch 4886 Error 0.20688068300390253\n",
+      "Epoch 4887 Error 0.20687767296907672\n",
+      "Epoch 4888 Error 0.2068746635938306\n",
+      "Epoch 4889 Error 0.20687165487790637\n",
+      "Epoch 4890 Error 0.20686864682104467\n",
+      "Epoch 4891 Error 0.20686563942298775\n",
+      "Epoch 4892 Error 0.20686263268347785\n",
+      "Epoch 4893 Error 0.20685962660225646\n",
+      "Epoch 4894 Error 0.20685662117906653\n",
+      "Epoch 4895 Error 0.2068536164136502\n",
+      "Epoch 4896 Error 0.2068506123057498\n",
+      "Epoch 4897 Error 0.2068476088551088\n",
+      "Epoch 4898 Error 0.2068446060614696\n",
+      "Epoch 4899 Error 0.20684160392457554\n",
+      "Epoch 4900 Error 0.20683860244417013\n",
+      "Epoch 4901 Error 0.2068356016199967\n",
+      "Epoch 4902 Error 0.20683260145179921\n",
+      "Epoch 4903 Error 0.20682960193932118\n",
+      "Epoch 4904 Error 0.20682660308230655\n",
+      "Epoch 4905 Error 0.20682360488049992\n",
+      "Epoch 4906 Error 0.2068206073336458\n",
+      "Epoch 4907 Error 0.20681761044148786\n",
+      "Epoch 4908 Error 0.20681461420377178\n",
+      "Epoch 4909 Error 0.20681161862024203\n",
+      "Epoch 4910 Error 0.20680862369064373\n",
+      "Epoch 4911 Error 0.20680562941472186\n",
+      "Epoch 4912 Error 0.20680263579222216\n",
+      "Epoch 4913 Error 0.20679964282289034\n",
+      "Epoch 4914 Error 0.20679665050647184\n",
+      "Epoch 4915 Error 0.20679365884271247\n",
+      "Epoch 4916 Error 0.20679066783135855\n",
+      "Epoch 4917 Error 0.2067876774721564\n",
+      "Epoch 4918 Error 0.2067846877648526\n",
+      "Epoch 4919 Error 0.20678169870919333\n",
+      "Epoch 4920 Error 0.20677871030492556\n",
+      "Epoch 4921 Error 0.20677572255179624\n",
+      "Epoch 4922 Error 0.20677273544955263\n",
+      "Epoch 4923 Error 0.20676974899794162\n",
+      "Epoch 4924 Error 0.20676676319671114\n",
+      "Epoch 4925 Error 0.20676377804560844\n",
+      "Epoch 4926 Error 0.20676079354438168\n",
+      "Epoch 4927 Error 0.2067578096927783\n",
+      "Epoch 4928 Error 0.20675482649054672\n",
+      "Epoch 4929 Error 0.20675184393743562\n",
+      "Epoch 4930 Error 0.20674886203319287\n",
+      "Epoch 4931 Error 0.20674588077756742\n",
+      "Epoch 4932 Error 0.20674290017030775\n",
+      "Epoch 4933 Error 0.2067399202111631\n",
+      "Epoch 4934 Error 0.20673694089988248\n",
+      "Epoch 4935 Error 0.20673396223621554\n",
+      "Epoch 4936 Error 0.20673098421991135\n",
+      "Epoch 4937 Error 0.2067280068507196\n",
+      "Epoch 4938 Error 0.20672503012839002\n",
+      "Epoch 4939 Error 0.20672205405267272\n",
+      "Epoch 4940 Error 0.206719078623318\n",
+      "Epoch 4941 Error 0.2067161038400759\n",
+      "Epoch 4942 Error 0.20671312970269706\n",
+      "Epoch 4943 Error 0.2067101562109319\n",
+      "Epoch 4944 Error 0.20670718336453164\n",
+      "Epoch 4945 Error 0.20670421116324658\n",
+      "Epoch 4946 Error 0.20670123960682835\n",
+      "Epoch 4947 Error 0.2066982686950282\n",
+      "Epoch 4948 Error 0.20669529842759737\n",
+      "Epoch 4949 Error 0.20669232880428773\n",
+      "Epoch 4950 Error 0.20668935982485057\n",
+      "Epoch 4951 Error 0.2066863914890386\n",
+      "Epoch 4952 Error 0.20668342379660357\n",
+      "Epoch 4953 Error 0.20668045674729757\n",
+      "Epoch 4954 Error 0.20667749034087315\n",
+      "Epoch 4955 Error 0.20667452457708294\n",
+      "Epoch 4956 Error 0.20667155945567978\n",
+      "Epoch 4957 Error 0.2066685949764166\n",
+      "Epoch 4958 Error 0.2066656311390466\n",
+      "Epoch 4959 Error 0.2066626679433227\n",
+      "Epoch 4960 Error 0.2066597053889982\n",
+      "Epoch 4961 Error 0.2066567434758276\n",
+      "Epoch 4962 Error 0.20665378220356356\n",
+      "Epoch 4963 Error 0.2066508215719605\n",
+      "Epoch 4964 Error 0.2066478615807723\n",
+      "Epoch 4965 Error 0.20664490222975387\n",
+      "Epoch 4966 Error 0.2066419435186586\n",
+      "Epoch 4967 Error 0.20663898544724155\n",
+      "Epoch 4968 Error 0.2066360280152574\n",
+      "Epoch 4969 Error 0.20663307122246097\n",
+      "Epoch 4970 Error 0.2066301150686073\n",
+      "Epoch 4971 Error 0.2066271595534517\n",
+      "Epoch 4972 Error 0.2066242046767492\n",
+      "Epoch 4973 Error 0.20662125043825574\n",
+      "Epoch 4974 Error 0.2066182968377265\n",
+      "Epoch 4975 Error 0.2066153438749179\n",
+      "Epoch 4976 Error 0.2066123915495856\n",
+      "Epoch 4977 Error 0.2066094398614857\n",
+      "Epoch 4978 Error 0.2066064888103743\n",
+      "Epoch 4979 Error 0.20660353839600834\n",
+      "Epoch 4980 Error 0.20660058861814432\n",
+      "Epoch 4981 Error 0.2065976394765391\n",
+      "Epoch 4982 Error 0.20659469097094926\n",
+      "Epoch 4983 Error 0.20659174310113224\n",
+      "Epoch 4984 Error 0.20658879586684514\n",
+      "Epoch 4985 Error 0.20658584926784557\n",
+      "Epoch 4986 Error 0.20658290330389067\n",
+      "Epoch 4987 Error 0.2065799579747386\n",
+      "Epoch 4988 Error 0.20657701328014746\n",
+      "Epoch 4989 Error 0.20657406921987465\n",
+      "Epoch 4990 Error 0.20657112579367876\n",
+      "Epoch 4991 Error 0.20656818300131813\n",
+      "Epoch 4992 Error 0.20656524084255082\n",
+      "Epoch 4993 Error 0.20656229931713652\n",
+      "Epoch 4994 Error 0.206559358424833\n",
+      "Epoch 4995 Error 0.2065564181654\n",
+      "Epoch 4996 Error 0.2065534785385962\n",
+      "Epoch 4997 Error 0.20655053954418123\n",
+      "Epoch 4998 Error 0.20654760118191418\n",
+      "Epoch 4999 Error 0.20654466345155512\n",
+      "测试集上的MAE/MSE\n",
+      "34.40483809416179\n",
+      "1948.863749051113\n",
+      "=============mape==============\n",
+      "mape= 10.041865761471188 %\n",
+      "R2 =  0.9623280195847855\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#本代码参考CSDN博主「积极向上的mr.d」的原创文章，原文链接：https://blog.csdn.net/qq_45077760/article/details/124508235\n",
+    "#本代码参考CSDN博主「积极向上的mr.d」的原创文章，原文链接：https://blog.csdn.net/qq_45077760/article/details/124508235\n",
+    "#本代码参考CSDN博主「积极向上的mr.d」的原创文章，原文链接：https://blog.csdn.net/qq_45077760/article/details/124508235\n",
+    "#本代码参考CSDN博主「积极向上的mr.d」的原创文章，原文链接：https://blog.csdn.net/qq_45077760/article/details/124508235\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "def sigmoid(x):\n",
+    "    '''\n",
+    "    激活函数\n",
+    "    '''\n",
+    "    return 1.0 / (1.0 + np.exp(-x))\n",
+    "\n",
+    "def sigmoid_prime(x):\n",
+    "    return sigmoid(x) * (1 - sigmoid(x))\n",
+    "\n",
+    "class BPNNRegression:\n",
+    "    '''\n",
+    "    神经网络回归与分类的差别在于：\n",
+    "    1. 输出层不需要再经过激活函数\n",
+    "    2. 输出层的 w 和 b 更新量计算相应更改\n",
+    "    '''\n",
+    "    def __init__(self, sizes):\n",
+    "\n",
+    "        # 神经网络结构\n",
+    "        self.num_layers = len(sizes)\n",
+    "        self.sizes = sizes\n",
+    "\n",
+    "        # 初始化偏差，除输入层外， 其它每层每个节点都生成一个 biase 值（0-1）\n",
+    "        self.biases = [np.random.randn(n, 1) for n in sizes[1:]]\n",
+    "        # 随机生成每条神经元连接的 weight 值（0-1）\n",
+    "        self.weights = [np.random.randn(r, c)\n",
+    "                        for c, r in zip(sizes[:-1], sizes[1:])]\n",
+    "        \n",
+    "    def feed_forward(self, a):\n",
+    "        '''\n",
+    "        前向传输计算输出神经元的值\n",
+    "        '''\n",
+    "        for i, b, w in zip(range(len(self.biases)), self.biases, self.weights):\n",
+    "            # 输出神经元不需要经过激励函数\n",
+    "            if i == len(self.biases) - 1:\n",
+    "                a = np.dot(w, a) + b\n",
+    "                break\n",
+    "            a = sigmoid(np.dot(w, a) + b)\n",
+    "        return a\n",
+    "    \n",
+    "    def MSGD(self, training_data, epochs, mini_batch_size, eta, error = 0.01):\n",
+    "        '''\n",
+    "        小批量随机梯度下降法\n",
+    "        '''\n",
+    "        n = len(training_data)\n",
+    "        for j in range(epochs):\n",
+    "            # 随机打乱训练集顺序\n",
+    "            random.shuffle(training_data)\n",
+    "            # 根据小样本大小划分子训练集集合\n",
+    "            mini_batchs = [training_data[k:k+mini_batch_size]\n",
+    "                            for k in range(0, n, mini_batch_size)]\n",
+    "            # 利用每一个小样本训练集更新 w 和 b\n",
+    "            for mini_batch in mini_batchs:\n",
+    "                self.updata_WB_by_mini_batch(mini_batch, eta)\n",
+    "            \n",
+    "            #迭代一次后结果\n",
+    "            err_epoch = self.evaluate(training_data)\n",
+    "            print(\"Epoch {0} Error {1}\".format(j, err_epoch))\n",
+    "            if err_epoch < error:\n",
+    "                break\n",
+    "            # if test_data:\n",
+    "            #     print(\"Epoch {0}: {1} / {2}\".format(j, self.evaluate(test_data), n_test))\n",
+    "            # else:\n",
+    "            # print(\"Epoch {0}\".format(j))\n",
+    "        return err_epoch\n",
+    "    \n",
+    "    def updata_WB_by_mini_batch(self, mini_batch, eta):\n",
+    "        '''\n",
+    "        利用小样本训练集更新 w 和 b\n",
+    "        mini_batch: 小样本训练集\n",
+    "        eta: 学习率\n",
+    "        '''\n",
+    "        # 创建存储迭代小样本得到的 b 和 w 偏导数空矩阵，大小与 biases 和 weights 一致，初始值为 0   \n",
+    "        batch_par_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        batch_par_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "\n",
+    "        for x, y in mini_batch:\n",
+    "            # 根据小样本中每个样本的输入 x, 输出 y, 计算 w 和 b 的偏导\n",
+    "            delta_b, delta_w = self.back_propagation(x, y)\n",
+    "            # 累加偏导 delta_b, delta_w \n",
+    "            batch_par_b = [bb + dbb for bb, dbb in zip(batch_par_b, delta_b)]\n",
+    "            batch_par_w = [bw + dbw for bw, dbw in zip(batch_par_w, delta_w)]\n",
+    "        # 根据累加的偏导值 delta_b, delta_w 更新 b, w\n",
+    "        # 由于用了小样本，因此 eta 需除以小样本长度\n",
+    "        self.weights = [w - (eta / len(mini_batch)) * dw\n",
+    "                        for w, dw in zip(self.weights, batch_par_w)]\n",
+    "        self.biases = [b - (eta / len(mini_batch)) * db\n",
+    "                        for b, db in zip(self.biases, batch_par_b)]\n",
+    "\n",
+    "    def back_propagation(self, x, y):\n",
+    "        '''\n",
+    "        利用误差后向传播算法对每个样本求解其 w 和 b 的更新量\n",
+    "        x: 输入神经元，行向量\n",
+    "        y: 输出神经元，行向量\n",
+    "        \n",
+    "        '''\n",
+    "        delta_b = [np.zeros(b.shape) for b in self.biases]\n",
+    "        delta_w = [np.zeros(w.shape) for w in self.weights]\n",
+    "\n",
+    "        # 前向传播，求得输出神经元的值\n",
+    "        a = x # 神经元输出值\n",
+    "        # 存储每个神经元输出\n",
+    "        activations = [x] \n",
+    "        # 存储经过 sigmoid 函数计算的神经元的输入值，输入神经元除外\n",
+    "        zs = []\n",
+    "        for b, w in zip(self.biases, self.weights):\n",
+    "            z = np.dot(w, a) + b\n",
+    "            zs.append(z)\n",
+    "            a = sigmoid(z) # 输出神经元\n",
+    "            activations.append(a)\n",
+    "        #-------------\n",
+    "        activations[-1] = zs[-1] # 更改神经元输出结果\n",
+    "        #-------------\n",
+    "        # 求解输出层δ\n",
+    "        # 与分类问题不同，Delta计算不需要乘以神经元输入的倒数\n",
+    "        #delta = self.cost_function(activations[-1], y) * sigmoid_prime(zs[-1])\n",
+    "        delta = self.cost_function(activations[-1], y) #更改后\n",
+    "        #-------------\n",
+    "        delta_b[-1] = delta\n",
+    "        delta_w[-1] = np.dot(delta, activations[-2].T)\n",
+    "        for lev in range(2, self.num_layers):\n",
+    "            # 从倒数第1层开始更新，因此需要采用-lev\n",
+    "            # 利用 lev + 1 层的 δ 计算 l 层的 δ \n",
+    "            z = zs[-lev]\n",
+    "            zp = sigmoid_prime(z)\n",
+    "            delta = np.dot(self.weights[-lev+1].T, delta) * zp\n",
+    "            delta_b[-lev] = delta\n",
+    "            delta_w[-lev] = np.dot(delta, activations[-lev-1].T)\n",
+    "        return (delta_b, delta_w)\n",
+    "    \n",
+    "    def evaluate(self, train_data):\n",
+    "        test_result = [[self.feed_forward(x), y]\n",
+    "                        for x, y in train_data]\n",
+    "        return np.sum([0.5 * (x - y) ** 2 for (x, y) in test_result])\n",
+    "    \n",
+    "    def predict(self, test_input):\n",
+    "        test_result = [self.feed_forward(x)\n",
+    "                        for x in test_input]\n",
+    "        return test_result\n",
+    "\n",
+    "    def cost_function(self, output_a, y):\n",
+    "        '''\n",
+    "        损失函数\n",
+    "        '''\n",
+    "        return (output_a - y)\n",
+    "    pass\n",
+    "\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "#导入必要的库\n",
+    "df1=pd.read_csv('hea-hardness.csv')\n",
+    "df1=df1.iloc[:,:]\n",
+    "#进行数据归一化\n",
+    "from sklearn import preprocessing\n",
+    "min_max_scaler = preprocessing.MinMaxScaler()\n",
+    "df0=min_max_scaler.fit_transform(df1)\n",
+    "df = pd.DataFrame(df0, columns=df1.columns)\n",
+    "x=df.iloc[:,:-1]\n",
+    "y=df.iloc[:,-1]\n",
+    "#划分训练集测试集\n",
+    "cut=20#取最后cut=25个数据为测试集\n",
+    "x_train, x_test=x.iloc[:-cut],x.iloc[-cut:]#列表的切片操作\n",
+    "y_train, y_test=y.iloc[:-cut],y.iloc[-cut:]\n",
+    "x_train, x_test=x_train.values, x_test.values\n",
+    "y_train, y_test=y_train.values, y_test.values\n",
+    "#神经网络搭建\n",
+    "bp1 = BPNNRegression([7, 15, 1])\n",
+    "train_data = [[sx.reshape(7,1), sy.reshape(1,1)] for sx, sy in zip(x_train, y_train)]\n",
+    "test_data = [np.reshape(sx, (7,1)) for sx in x_test]\n",
+    "#神经网络训练\n",
+    "bp1.MSGD(train_data, 5000, len(train_data), 0.2)\n",
+    "#神经网络预测\n",
+    "y_predict=bp1.predict(test_data)\n",
+    "y_pre = np.array(y_predict)  # 列表转数组\n",
+    "y_pre=y_pre.reshape(20,1)\n",
+    "y_pre=y_pre[:,0]\n",
+    "y_test=y_test*763+120\n",
+    "y_pre=y_pre*763+120\n",
+    "#画图 #展示在测试集上的表现\n",
+    "draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_pre)],axis=1);\n",
+    "draw.iloc[:,0].plot(figsize=(12,6))\n",
+    "draw.iloc[:,1].plot(figsize=(12,6))\n",
+    "plt.legend(('real', 'predict'),loc='upper right',fontsize='15')\n",
+    "plt.title(\"Test Data\",fontsize='30') #添加标题\n",
+    "#输出精度指标\n",
+    "print('测试集上的MAE/MSE')\n",
+    "print(mean_absolute_error(y_pre, y_test))\n",
+    "print(mean_squared_error(y_pre, y_test) )\n",
+    "mape = np.mean(np.abs((y_pre-y_test)/(y_test)))*100\n",
+    "print('=============mape==============')\n",
+    "print('mape=',mape,'%')\n",
+    "# 画出真实数据和预测数据的对比曲线图\n",
+    "print(\"R2 = \",metrics.r2_score(y_test, y_pre)) # R2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6c595e3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[378.84636083 187.03700462 502.23738671 303.3787932  248.24616957\n",
+      " 510.54608432 510.17016432 316.76616376 406.6814889  859.30862052\n",
+      " 463.48852136 790.4451477  798.3489766  502.23738671 521.62595305\n",
+      " 832.20864548 248.95979688 664.88199066 419.313602   857.21884516]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_pre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cbb58d5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[357. 130. 446. 280. 182. 567. 506. 300. 370. 883. 520. 794. 802. 615.\n",
+      " 500. 849. 310. 699. 406. 855.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8d35646",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
Gitee