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AarayYin · Nov 5, 2021 · 9580990 · 9580990
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diff --git a/1. Python简介.ipynb b/1. Python简介.ipynb
diff --git a/2. Python的基本使用.ipynb b/2. Python的基本使用.ipynb
diff --git a/3. Python文件读写，库.ipynb b/3. Python文件读写，库.ipynb
diff --git a/4. 机器学习简介.ipynb b/4. 机器学习简介.ipynb
diff --git a/5.1 机器学习算法.ipynb b/5.1 机器学习算法.ipynb
diff --git a/5.2 吸附中的线性回归.ipynb b/5.2 吸附中的线性回归.ipynb
diff --git a/5.3 非线性回归.ipynb b/5.3 非线性回归.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ada63d23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c3f95fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3.000000e-10</td>\n",
+       "      <td>2.270000e-12</td>\n",
+       "      <td>2.240000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.000000e-10</td>\n",
+       "      <td>2.060000e-12</td>\n",
+       "      <td>1.900000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.000000e-10</td>\n",
+       "      <td>2.790000e-12</td>\n",
+       "      <td>1.560000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.990000e-10</td>\n",
+       "      <td>2.600000e-12</td>\n",
+       "      <td>1.930000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.990000e-10</td>\n",
+       "      <td>1.740000e-12</td>\n",
+       "      <td>9.890000e-13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              0             1             2\n",
+       "0  3.000000e-10  2.270000e-12  2.240000e-12\n",
+       "1  3.000000e-10  2.060000e-12  1.900000e-12\n",
+       "2  3.000000e-10  2.790000e-12  1.560000e-12\n",
+       "3  2.990000e-10  2.600000e-12  1.930000e-12\n",
+       "4  2.990000e-10  1.740000e-12  9.890000e-13"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('iz.csv', mode = 'r', encoding = 'UTF-8') as f:\n",
+    "    df = pd.read_csv(f, header = None)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "00c012a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[3] = 0.5 * (df[1] + df[2])\n",
+    "z = np.array(df[0]) * 1e10\n",
+    "current = np.array(df[3]) * 1e12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fcd90eab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from scipy.optimize import curve_fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "922886ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 3.60225002e+00 -2.56599244e-03]\n",
+      " [-2.56599244e-03  1.89800824e-06]]\n",
+      "4.306461770837465 3.919756213516532\n"
+     ]
+    }
+   ],
+   "source": [
+    "def func(x, a, b):\n",
+    "    return a * np.exp(b*x)\n",
+    "\n",
+    "def cal_wf_lin(z, current):\n",
+    "    lr = LinearRegression()\n",
+    "    lr.fit(z.reshape(-1, 1), np.log(current))\n",
+    "    wf = 0.952 * pow(lr.coef_[0], 2)\n",
+    "    return wf\n",
+    "\n",
+    "def cal_wf(z, current):\n",
+    "    pfit, pcov = curve_fit(func, z, current)\n",
+    "    print(pcov)\n",
+    "    wf = 0.952 * pow(pfit[1], 2)\n",
+    "    return wf\n",
+    "\n",
+    "res1 = cal_wf_lin(z, current)\n",
+    "res2 = cal_wf(z, current)\n",
+    "print(res1, res2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8d4bea2",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}