From 3e01aa4f731c2fc99cc74bd1507608e47744a491 Mon Sep 17 00:00:00 2001
From: zhang117228 <13198271+zhang117228@user.noreply.gitee.com>
Date: Thu, 13 Jul 2023 09:19:58 +0000
Subject: [PATCH] =?UTF-8?q?=E9=B8=A2=E5=B0=BE=E8=8A=B1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: zhang117228 <13198271+zhang117228@user.noreply.gitee.com>
---
 .../homeworks/iris.ipynb                      | 727 ++++++++++++++++++
 1 file changed, 727 insertions(+)
 create mode 100644 QuickDraw在线交互识别系统/homeworks/iris.ipynb

diff --git a/QuickDraw在线交互识别系统/homeworks/iris.ipynb b/QuickDraw在线交互识别系统/homeworks/iris.ipynb
new file mode 100644
index 0000000..c99d446
--- /dev/null
+++ b/QuickDraw在线交互识别系统/homeworks/iris.ipynb
@@ -0,0 +1,727 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c5c256bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
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+       "        [5.5, 2.4, 3.7, 1. ],\n",
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+       "        [6.3, 2.3, 4.4, 1.3],\n",
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+       "        [5. , 2.3, 3.3, 1. ],\n",
+       "        [5.6, 2.7, 4.2, 1.3],\n",
+       "        [5.7, 3. , 4.2, 1.2],\n",
+       "        [5.7, 2.9, 4.2, 1.3],\n",
+       "        [6.2, 2.9, 4.3, 1.3],\n",
+       "        [5.1, 2.5, 3. , 1.1],\n",
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+       "        [5.8, 2.7, 5.1, 1.9],\n",
+       "        [7.1, 3. , 5.9, 2.1],\n",
+       "        [6.3, 2.9, 5.6, 1.8],\n",
+       "        [6.5, 3. , 5.8, 2.2],\n",
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+       "        [6.5, 3.2, 5.1, 2. ],\n",
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+       "        [6.8, 3. , 5.5, 2.1],\n",
+       "        [5.7, 2.5, 5. , 2. ],\n",
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+       "        [6.5, 3. , 5.5, 1.8],\n",
+       "        [7.7, 3.8, 6.7, 2.2],\n",
+       "        [7.7, 2.6, 6.9, 2.3],\n",
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+       "        [6.3, 2.5, 5. , 1.9],\n",
+       "        [6.5, 3. , 5.2, 2. ],\n",
+       "        [6.2, 3.4, 5.4, 2.3],\n",
+       "        [5.9, 3. , 5.1, 1.8]]),\n",
+       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#逻辑回归对鸢尾花数据集\n",
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import load_iris\n",
+    "iris=load_iris()\n",
+    "iris.data,iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "455a18cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#sigmoid函数\n",
+    "def sigmoid(x):\n",
+    "    return 1/(1+np.exp(-x))\n",
+    "\n",
+    "#x为将-10到10分成100份\n",
+    "x=np.linspace(-10,10,100)\n",
+    "y=sigmoid(x)\n",
+    "plt.plot(x,y)\n",
+    "#[0.5]乘len是为了保持和x的数目一致，有这么多个0.5\n",
+    "plt.plot(x,[0.5]*len(y),'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edfd6016",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#手动实现逻辑回归\n",
+    "class Logistic:\n",
+    "    def __init__(self,lr=0.01,n_iters=101):\n",
+    "        self.lr=lr\n",
+    "        self.n_iters=n_iters\n",
+    "        self.weights=None\n",
+    "        self.bias=None\n",
+    "    \n",
+    "    def fit(self,x,y):\n",
+    "        [m,d]=np.shape(x)\n",
+    "        self.weights=np.zeros(d)\n",
+    "        self.bias=0\n",
+    "        for i in range(self.n_iters):\n",
+    "            linear_model=np.dot(x,self.weights)+self.bias\n",
+    "            y_pred=self.sigmoid(linear_model)\n",
+    "            \n",
+    "            dw=(1/m)*np.dot(x.T,(y_pred-y))\n",
+    "            db=(1/m)*np.sum(y_pred-y)\n",
+    "            \n",
+    "            self.weights-=self.lr*dw\n",
+    "            self.bias-=self.lr*db\n",
+    "            \n",
+    "            if i%100==0:\n",
+    "                loss=self.cross_entropy(y,y_pred)\n",
+    "                print(f'loss at iteration {i} : {loss}')\n",
+    "                \n",
+    "    def predict(self,x):\n",
+    "        linear_model=np.dot(x,self.weights)+self.bias\n",
+    "        y_pred=self.sigmoid(linear_model)\n",
+    "        for i in range(len(y_pred)):\n",
+    "            if y_pred[i]>0.5:\n",
+    "                y_pred[i]=1\n",
+    "            else:\n",
+    "                y_pred[i]=0   \n",
+    "        return y_pred       \n",
+    "    \n",
+    "        \n",
+    "    def sigmoid(self,x):\n",
+    "        return 1/(1+np.exp(-x))\n",
+    "    \n",
+    "    def cross_entropy(self,y_true,y_pred):\n",
+    "        return -np.mean(y_true*np.log(y_pred)+(1-y_true)*np.log(1-y_pred))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "97172fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=iris.data[:,:2]\n",
+    "y=iris.target\n",
+    "\n",
+    "#括号中代表索引下标  你、\n",
+    "\n",
+    "   \n",
+    "x=x[y!=2]\n",
+    "y=y[y!=2]\n",
+    "\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n",
+    "\n",
+    "clf=LogisticRegression()\n",
+    "clf.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred=clf.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a40fc0f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qipcuLAOWRcSj+fNbyRJ2t7kGbWYGtLZ0mXgrEhEvS1oq6f0R8UdgArComracoM3MqPkojn8AbshHcDwPfL6aRpygzcygktJF5W1FzAfGbGk7TtBmZkCkN5mdE7SZGdS2B10rTtBmZtTuImEtOUGbmeEetJlZsiKcoM3MkpTidKNO0GZmQGuCPegub/VWZrKkb+bP95A0tv6hmZk1ToQqXhqlkrk4/j9wKPDZ/Pk64Id1i8jMrACtLap4aZRKShwHR8SHJT0JEBGv57cvmpn1Gj11FMdGSX2AAJA0jCS/XtHMrHop1qArSdCXA78EdpJ0MfAZ4Py6RmVm1mA9cphdRNwg6XGyKfMETIqIxXWPzMysgXrkXByS9gD+AtxRui4iXqxnYGZmjdRTSxx3kdWfBfQH9gL+COxbx7jMzBqqtSdeJIyI/UufS/ow8IW6RWRmVoCe2oN+m4h4QtJB9Qim1IDhh9X7ENYDrX/p4aJDsF6qR14klPTVkqdNZF9+uKJuEZmZFaCn9qAHljzeRFaT/kV9wjEzK0aCgzg6T9D5DSrbRcQ5DYrHzKwQLa2VzHzRWB0maEl9I2JTflHQzKxXS/H26M560I+R1ZvnS7od+DnwZtvGiLitzrGZmTVM0DNr0EOA14BP8tZ46ACcoM2s12hNsAjdWYLeKR/B8TRvJeY2CZ6KmVn1WntYD7oPsB2UjdoJ2sx6lZ5W4miOiIsaFomZWYFaeliCTi9aM7M66WmjOCY0LAozs4L1qAQdEasaGYiZWZF6Wg3azOxdI8HZRp2gzcyg5w2zMzN712gpOoAynKDNzIBWuQdtZpakFO++c4I2M6OHDbMzM3s3qfUojnw+/XnA8og4rpo2nKDNzKjLrd5nAouBQdU2kN5XCJiZFaBVlS9dkbQbcCxw1ZbE5ARtZkZWg650kTRV0rySZWq75r4PfJ0tLG27xGFmRvdGcUTEdGB6uW2SjgNejYjHJY3fkpicoM3MqOlFwo8BJ0g6BugPDJI0IyImd7chlzjMzOheiaMzEfFPEbFbRIwETgYeqCY5g3vQZmYAtKR3I6ETtJkZ1OdGlYh4CHio2v2doM3M8J2EZmbJ8lwcZmaJ8oT9ZmaJconDzCxRnrDfzCxRLnGYmSXKJQ4zs0R5FIeZWaJaE0zRTtBmZvgioZlZslyDNjNLlEdxmJklyjVoM7NEpZeenaDNzADXoM3MktWSYB/aCdrMDPegzcyS5YuEZmaJSi89O0GbmQEucZiZJcsXCc3MEpViDbqp6ACsc0cdOZ6FT8/mmUVz+Po5f190OFaQ8y+ZxrhjT2bS5C9uXrdm7TpOP/M8jjlpCqefeR5r1q4rMMKeL7qxNIoTdMKampq4/LKLOe74yex/wOGcdNIk9tln76LDsgJMOuZTXDnt229bd9XPZnLImNHcfcvVHDJmNFfPmFlQdL1DK1Hx0ihO0Akbe9CBPPfcCyxZ8iIbN25k5sxZnHD8UUWHZQUYM3p/Bg8a+LZ1Dz78CBOPPgKAiUcfwQOzHykitF6jtRtLozhBJ2z4iF1Yuuylzc+XLW9m+PBdCozIUvLa66sZNnQIAMOGDmHV6jUFR9SzRTf+a5SGJ2hJn+9k21RJ8yTNa219s5FhJUl65/yHEeldyDDrDVqIipdGKaIH/a2ONkTE9IgYExFjmpq2bWRMSVq+rJnddxu++fluI3alufmVAiOylOy4w/asWLkKgBUrVzFk+8EFR9SzvWtKHJIWdLD8Adi5HsfsjebOm8+oUXsxcuTu9OvXjxNPnMgdd95XdFiWiPEfP4RZ99wPwKx77ufwww4tOKKerTWi4qVR6jUOemfgKOD1dusF/K5Ox+x1WlpaOPOs87n7rhvp09TEtdfdwqJFfyo6LCvAORdcytwnF7B69VomTJrMl6ecyumnnsjX/uUSbrvzXnbdeRjTvv3PRYfZo6VYPFQ9apqSrgZ+GhFzymy7MSI+11UbfbcakeL7ZQVb/9LDRYdgCeo39D1b/IVVn9vzbyrOOTf++ZcN+YKsuvSgI2JKJ9u6TM5mZo3WyNEZlfKt3mZmwCYnaDOzNKXYg/aNKmZm1G6YnaTdJT0oabGkhZLOrDYm96DNzKjpTWCbgK9FxBOSBgKPS/p1RCzqbkNO0GZm1G660YhoBprzx+skLQZGAE7QZmbV6M4t3JKmAlNLVk2PiOllXjcSOBB4tJqYnKDNzOheDzpPxu9IyKUkbQf8AjgrItZWE5MTtJkZtZ2ITFI/suR8Q0TcVm07TtBmZtRuEiRl01BeDSyOiGlb0paH2ZmZUdP5oD8GnAp8UtL8fDmmmpjcgzYzo6ajOOaQTQy3xZygzcyAlmjkTM+VcYI2MyPNW72doM3MoKET8VfKCdrMjDQn7HeCNjOjdhcJa8kJ2swMJ2gzs2R5FIeZWaI8isPMLFH1+ALtLeUEbWaGa9BmZslyD9rMLFEtNZvPrnacoM3M8J2EZmbJ8igOM7NEuQdtZpYo96DNzBLlHrSZWaJ8q7eZWaJc4jAzS1S4B21mlibf6m1mlijf6m1mlij3oM3MEtXS6hq0mVmSPIrDzCxRrkGbmSXKNWgzs0S5B21mlihfJDQzS5RLHGZmiXKJw8wsUZ5u1MwsUR4HbWaWKPegzcwS1ZrgdKNNRQdgZpaCiKh46YqkT0v6o6T/lHRutTG5B21mRu1GcUjqA/wQ+BSwDJgr6faIWNTdttyDNjMDohtLF8YC/xkRz0fEBuBmYGI1MSXbg960YbmKjiEVkqZGxPSi47C0+PeitrqTcyRNBaaWrJpe8rMYASwt2bYMOLiamNyD7hmmdv0Sexfy70VBImJ6RIwpWUo/KMsl+qrqJ07QZma1tQzYveT5bsBL1TTkBG1mVltzgb0l7SVpK+Bk4PZqGkq2Bm1v4zqjlePfiwRFxCZJXwHuBfoA10TEwmraUooThJiZmUscZmbJcoI2M0uUE3TianXLqPUekq6R9Kqkp4uOxerLCTphJbeMHg18EPispA8WG5Ul4Frg00UHYfXnBJ22mt0yar1HRMwGVhUdh9WfE3Tayt0yOqKgWMyswZyg01azW0bNrOdxgk5bzW4ZNbOexwk6bTW7ZdTMeh4n6IRFxCag7ZbRxcDMam8Ztd5D0k3AI8D7JS2TNKXomKw+fKu3mVmi3IM2M0uUE7SZWaKcoM3MEuUEbWaWKCdoM7NEOUFbXUhqkTRf0tOSfi5pmy1o61pJn8kfX9XZhFGSxkv6aBXHeEHS0GpjNKsHJ2irl/URMToi9gM2AF8s3ZjP1NdtEXF6RCzq5CXjgW4naLMUOUFbIzwMjMp7tw9KuhH4g6Q+kv6fpLmSFkj6AoAyV0haJOkuYKe2hiQ9JGlM/vjTkp6Q9JSk30gaSfZBcHbeez9M0jBJv8iPMVfSx/J9d5R0n6QnJf2Y8vOemBXKXxprdSWpL9l81r/KV40F9ouIJZKmAmsi4iBJWwP/Iek+4EDg/cD+wM7AIuCadu0OA34CjMvbGhIRqyRdCbwREd/JX3cj8L2ImCNpD7K7MvcBLgDmRMRFko4Fptb1jTCrghO01csASfPzxw8DV5OVHh6LiCX5+iOBD7XVl4HBwN7AOOCmiGgBXpL0QJn2DwFmt7UVER3Nj3wE8EFpcwd5kKSB+TH+R77vXZJer+40zerHCdrqZX1EjC5dkSfJN0tXAf8QEfe2e90xdD2tqip4DWRlvEMjYn2ZWDzPgSXNNWgr0r3AlyT1A5D0PknbArOBk/Ma9a7A4WX2fQT4hKS98n2H5OvXAQNLXncf2YRT5K8bnT+cDZySrzsa2KFWJ2VWK07QVqSryOrLT+RfgPpjsr/qfgk8C/wB+BHw2/Y7RsQKsrrxbZKeAm7JN90B/E3bRULgDGBMfhFyEW+NJvkWME7SE2SllhfrdI5mVfNsdmZmiXIP2swsUU7QZmaJcoI2M0uUE7SZWaKcoM3MEuUEbWaWKCdoM7NE/TfPfmwfRPySUgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#混淆矩阵\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "score=accuracy_score(y_test,y_pred)\n",
+    "plt.title(f\"The accuracy score is {score*100}%\")\n",
+    "cm=confusion_matrix(y_test,y_pred)\n",
+    "sns.heatmap(cm,annot=True)\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('True')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d67e6803",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#ROC曲线\n",
+    "from sklearn.metrics import roc_curve,auc\n",
+    "\n",
+    "fpr,tpr,thresholds=roc_curve(y_test,y_pred)\n",
+    "roc_auc=auc(fpr,tpr)\n",
+    "plt.figure()\n",
+    "plt.plot(fpr,tpr,color='darkorange',lw=2,label='ROC curve (area=%0.2f)' % roc_auc) #伪真率为横坐标，真正率为纵坐标\n",
+    "plt.plot([0,1],[0,1],color='navy',lw=2,linestyle='--') #画对角线\n",
+    "#x和y的范围\n",
+    "plt.xlim=([0,1])\n",
+    "plt.ylim=([0,1.05])\n",
+    "#横纵坐标的含义\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('Receiver operating characteristic example')\n",
+    "#legend表示标签放在右下角\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "78d77fb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\1895947525.py:14: UserWarning: You passed a edgecolor/edgecolors ('k') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  scatter1=plt.scatter(x[y==1,0],x[y==1,1],c='purple',edgecolors='k',marker='+',label='Class 1')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#定义网格范围，绘制决策边界\n",
+    "x_min,x_max=x[:,0].min()-1,x[:,0].max()+1\n",
+    "y_min,y_max=x[:,1].min()-1,x[:,1].max()+1\n",
+    "xx,yy=np.meshgrid(np.arange(x_min,x_max,0.02),\n",
+    "                  np.arange(y_min,y_max,0.02))\n",
+    "\n",
+    "#对每个点在模型中预测\n",
+    "Z=clf.predict(np.c_[xx.ravel(),yy.ravel()])\n",
+    "#reshape成网格的形状\n",
+    "Z=Z.reshape(xx.shape)\n",
+    "#绘制等高线，用不同颜色表示分类区域\n",
+    "plt.contourf(xx,yy,Z,1,alpha=0.8)\n",
+    "scatter0=plt.scatter(x[y==0,0],x[y==0,1],c='yellow',edgecolors='k',marker='o',label='Class 0')\n",
+    "scatter1=plt.scatter(x[y==1,0],x[y==1,1],c='purple',edgecolors='k',marker='+',label='Class 1')\n",
+    "\n",
+    "plt.legend(handles=[scatter0,scatter1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "268e5dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
+    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,mean_squared_error\n",
+    "from sklearn.svm import SVC\n",
+    "plt.rcParams['font.sans-serif']=['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus']=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "33757302",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.91520253,  1.05978084],\n",
+       "       [ 0.32315141, -1.02666268],\n",
+       "       [-0.91520253,  0.82795378],\n",
+       "       [-0.04835477, -0.79483563],\n",
+       "       [-0.54369635,  1.52343495],\n",
+       "       [-0.91520253,  1.52343495],\n",
+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-1.53437949,  0.82795378],\n",
+       "       [-1.03903792, -2.41762503],\n",
+       "       [-0.17219017, -0.09935445],\n",
+       "       [ 1.93301152, -0.56300857],\n",
+       "       [ 2.3045177 ,  1.75526201],\n",
+       "       [ 0.19931601, -0.33118151],\n",
+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-0.04835477, -0.79483563],\n",
+       "       [ 0.4469868 , -0.33118151],\n",
+       "       [-0.91520253, -1.25848974],\n",
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+       "       [ 1.06616377,  0.1324726 ],\n",
+       "       [-1.16287331,  0.1324726 ],\n",
+       "       [ 0.81849298, -0.56300857],\n",
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+       "       [-0.79136713,  1.05978084],\n",
+       "       [ 1.06616377,  0.1324726 ],\n",
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+       "       [-0.04835477, -0.56300857],\n",
+       "       [-0.17219017, -1.25848974],\n",
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+       "       [ 0.32315141, -0.33118151],\n",
+       "       [ 1.06616377, -0.09935445],\n",
+       "       [-1.28670871, -0.09935445],\n",
+       "       [ 0.69465759,  0.1324726 ],\n",
+       "       [-0.17219017, -0.33118151],\n",
+       "       [-1.03903792, -1.72214386],\n",
+       "       [-0.79136713,  0.82795378],\n",
+       "       [-0.17219017, -1.02666268],\n",
+       "       [ 1.06616377,  0.1324726 ],\n",
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+       "       [-0.04835477, -0.79483563],\n",
+       "       [-1.03903792,  0.59612672],\n",
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+       "       [-1.16287331, -1.4903168 ],\n",
+       "       [ 0.57082219,  0.59612672],\n",
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+       "       [ 0.32315141, -0.56300857],\n",
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+       "       [ 0.57082219, -1.72214386],\n",
+       "       [-1.16287331,  0.1324726 ],\n",
+       "       [-1.03903792,  0.36429966],\n",
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+       "       [ 1.06616377,  0.59612672],\n",
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+       "       [-0.91520253,  1.75526201],\n",
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+       "       [-1.03903792,  1.05978084],\n",
+       "       [-1.16287331,  1.29160789],\n",
+       "       [ 0.4469868 , -0.56300857],\n",
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+       "       [ 0.81849298, -0.09935445],\n",
+       "       [-0.41986095, -1.4903168 ],\n",
+       "       [-1.16287331, -1.25848974],\n",
+       "       [-0.29602556, -0.79483563],\n",
+       "       [ 2.3045177 , -1.02666268],\n",
+       "       [-1.78205028,  0.36429966],\n",
+       "       [-1.53437949,  0.1324726 ],\n",
+       "       [ 0.4469868 , -1.95397092],\n",
+       "       [-0.29602556, -0.09935445],\n",
+       "       [ 0.19931601, -0.09935445],\n",
+       "       [-0.41986095,  2.68257024],\n",
+       "       [ 2.3045177 , -0.09935445],\n",
+       "       [ 2.55218849,  1.75526201],\n",
+       "       [-0.54369635,  1.98708907],\n",
+       "       [-0.41986095, -1.72214386],\n",
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+       "       [-1.03903792, -0.09935445],\n",
+       "       [-0.29602556, -0.33118151],\n",
+       "       [-0.29602556, -0.56300857],\n",
+       "       [-0.41986095, -1.4903168 ],\n",
+       "       [-0.17219017,  1.75526201],\n",
+       "       [-0.17219017, -0.56300857]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "iris=load_iris()\n",
+    "x=iris.data[:,:2]\n",
+    "y=iris.target\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)\n",
+    "scaler=StandardScaler()\n",
+    "scaler.fit(x_train)\n",
+    "x_train_std=scaler.transform(x_train)\n",
+    "x_test_std=scaler.transform(x_test)\n",
+    "x_train_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1428bb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\2116216888.py:27: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  plt.scatter(x=X[y == cl, 0],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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x48pQ24rIH9zN+lXt+bhgNW8veZdzJ09xQccOnDhczbmzYptIdeuZ/3mGlatWctOim0jLSKMov4iVc1cCaEBXtsSEYZFtxoABJj+StkyNYxl33GG/z/nevZSkpzduf/y4q61xXQlQorMpDYOtMfDBB9Bx0jrGDO/k126KM26dydemj6Fjr7q1QCdLdvHp8ndY8soLLe7j2GFjuWnRTfS/oi4Jvfu93fxl9l/YuH1ji++volO/pH5bjTEZdp/pnLmKCIFOdPrScNQsUlcZumN/mV/3KCnez7Bx3vPjw8alUhKgBOipE6dIy0jzupaWkcapE6cCcn8VezSYq7ALRqITGld2NvVL6JBK78pQX3r1TWX7Bu/AvX3Dfnr1TXX9XDsdkjtQlF/kda0ov4gOyR1c3ysQ/VGRT+fMQ8xtRadT+y8OH+YCu4lZh0pPJ/86dowdx441uh7NiU6oq/T0zI17kp9t20JWlvPfKy+0KkPHDMdxusUY6NrpLlY99hRT/ssakW/fsJ9Vj62nW8cHePgH+Rw5/htKiq3g3rXTXQy9fDwX9/mr3xWj06ZPY+XclUyZP6V2znzV3FVMmz7NVUXqgX3Xc/pkK+740fHan8PSp1KaVfGqIpsG8xBzW9Hp1L5naSklbdo0uu5U6emkAvtEZ4VDnwJ2dqdnfjwIgdy70rNu1YpnFYuvBOWlXdMpKDtIWVWZYzAXgaGXW2eHfrr8N7zxhBW0R2X8iD2fwftbn2LKf13HrbVB/im+LPmIs63+wUQ/K0Y9Sc5XZ3uvZhk6YqjfFanPZy8m/aL2fPrRZADu+NFxlj6VwtoV7itenfqpIocmQEPMKeHolFh0at9z924OJiU1ut6rooKS+gupA9yfgAhBorP+MkSP+qtYfMnL8y8ZapdInTndPjG6cs6LXhWjAF98+AXv/vpdXnCRMJ1560xG3zO60X0aVqR6rm/+9btkZv6RtSvqpmdu+Pap2pG6v/d3208VHJoAVREjVInO+pWeHv4EcrC6VV44sMlkqF0i1SkxGqiKUbcVqSXF+7njR96/YTkFcl/318rWyKfBXIVMsBKddhpWeoL13t9fRC/tms7RI1BY7m7fc6fEaHMqRhumLaqr3Vek9uqbytKnvH/zWvpUiuPPwen+Wtka+XTOPMI5JSidnKuuJuOOOxpdd0qYNmsrWreCmOi041TpWX8O3Z8RennhQAooZODl/j+3a6e7eOWB/6ZL71a1xURH/1nNoK/cxuvZuV5z5r7O1pz9H90o2vMPuvZ6lpJ9++nVJ5XDJbNo1+4H5Gb/wrYi9bn/eIlWrc9TfqacpHZJVFe2oW/q/9bOkdefMwf7EbqeARq9NJiHmNuKTqcEZTn2yc5EcJUwdb0VrVshKgSqz7nS07rub3WmJxn6ZpF/Z4aKwAVtDcld23DVDO8y/0v6D2ds1gC/ztasroaiPf/g0MlfknljFrdOvJgtrx9gxxO/pDsPcO+P7+aVBhWp//fsLpKSWzF53jT6XNabfR/+k9WPrOV42TZu/u5VtYHbM+XSvqP9LpF6Bmj00gRohIu0hKkrYQjk9dklKN2W2fubDPUIVAJxxr/PJP3Gq0jpXVcBevyfu9mb8zZL/tj4PmOHjeXGX95En4y6xOu+/F3kPPAXNny8scU/BxUZNAGqQi6UFZ1O7BKUbvmbDPUIVAKxZN9+Rk682OvayIkXU7LP/j6nTpwifWSa17X0kVbFaCB+Diry+XugcxcRuUZEuga7Qyr6NUx0uq1AtEv8Nec+gWrvJhnanASi3XN79Ully+sHvK5vef0AvfrY36dDcgf2binyurZ3S/MqRlV08ucM0M7AX4HXgEUiMsEYU2rTbikwGHjNGDM/4D2NMk6Vm1+eOMFFycmNrjtVgLr1RWkpw0sb/ePhPHDWZqvbSrBNmDarPzaJTreVmAsXwpkzMGcOtGplBfLHH4eyMhgzxv/75ObC7t25nCh/lkOlRXTvlkZy0iz698+iW/dc1r9Vd/268bMoPZTl2D4rqy4Z2mn4QZ/TLb4SiPffeT8b39pIxfkKEtskMnb8WC4f9UKjCs3f/CqFrW/fx7b8n3JhnwQqzpwksV1Hjuyron3CL9iQu5FXflevQvM/ppOedhs5c5dz4/wppI9MY++WInLmriI97TbHaZVwnW2qZ6oGhz8J0GHAbGPM+zWBfQSwvn4DEZkKJBhjrhCRl0RkgDFmVxD6GzWcKjd7HTniqgLUbcI0EXjF5vp4oPEhcFaC1dUZo05s5sfdVmJWV1uBfPt2K4DPmWP9+fHH0KMHvP22f/cxxgrkOw/M44aHJzFo5C3s3FLM2gXzOHwkn+oOr3Pd/ZNIHXwL+3cUs/rJebQ6lU9p+euN2lv3y/KrMhScE4hrV6zlg48/4DvPf4e+X+9L8QfF5MzJYdeOe8D8Dqir0PxrzWqTCzokceWdmfQY0IV/7TpKbnYe58u2snjx640qSQf1nw1AzgMv11aMpqfdxrXf+jEijcv2w3W2qZ6pGjx+J0BF5CpgPnC9MeZEg8/+F8g1xqwTkX8H2hpjfut0r3hIgIZra9leU6Y4nul50OZMz4D0x0ei020lpmckvn173bVhw+Dhh2HNGv/vs2DhDYyYOYpOfeu+t7LiveTMXcL3np5B32F114u37+X39y3hxvkzGrXf9sL7PPyTtYD7ZGh9X+/3db7z/Hfo941+tdf2bN7DH37wB2b+5+eNKjTztvw7g78zhuR6laQnSnaR46OSdPHyF/xOdLqtJA1UBWi4nhsrWpwAFREBbgGOYQ3mGmoPlNS8Pgr0sLnHDBHJF5H8UrejPhWxmkp0uq3EbNXKCtz1PfwwJCTA5Mne1ydPdr7PodIiBo3s63Vt0Mi+nDlzktTB3tdTB1vX7dofKi2qndP2JEM//WeZ/UNr2M2BV5yvoO/Xve/f9+t9qThfwe33ef/3cPt9xynZt5/hDSpJhzdRSeom0Rmus031TNXg8SuYG8ssYDvwLZsmp4C2Na872N3XGLPEGJNhjMnoFuwilQhWVV3NjqKiRl//clEYFBHy8vyq6HRbibluHdx7r/e1e++FX/7SGrF7/p4x1vvcXPv7dO+Wxs4txV7Xdm4pJjGxo+31Vgkd+ejtYq/7f/R2MeXn0rz6O/zCdFY/cwm/WnyajW9sZOatM/nm6G8y89aZbHxjI6+82NGrwtKzS2FC60SKP/B+bvEHxSS0TmTmTd5jn5k39aB1Yt+AVZLaac7Zpnbfr1t6pmrwNBnMReRBEfHUrXQCymyabQXG1LweDhQFoG8xa3BCQqOv6lBsORso9ROdfgRyz9z2woXWn5s22Qf0qip47TXYvx+6d4c//tH685//tE4C2r4dunSBJ5+0/ty+3bre8EdnDCQnzWLtgnWUFe/lop5VlBXvZc2CdSSZu1izYB0Fm/dSVVlFwWbretWpu1iXvY5Du6z2h3bt5bVfrKP85Czefruuv6tWwYdr08n7x3s88+wzjL5nNPevv5/R94zm+ecW88nWt1i7okNtQPdUXKZedA05c3LYs3kPlecr2bN5DzlzckhqlcW7G9rSvWclOZtL6N6zks1vteVIySxWPbaekyW7uGTAWU6W7GLVY+tJT7MqSb/48AuqKqv44sMvyM3OZfr3p7v6Rzj9+9PJtbnPtOnTbK8PHW7t1lj/+1383GLXAd3tc91+X/GsyTnzmqTnCiAJKACeBW41xsyt1yYZ2AT8A5gIjDLGOM6lxMOcudNqlr2lpaS3bpx33g8cXLmyxc/tNW0a2FSGnjOGS7o2XlnqenWNy0Kg5qxm2bvXOiLUs5qlogKSkqBzZzh6tO4+XbpARgZMnGj/XLvVKf36ZbFnTy7bPn2WysoiWrdOY8TXrOv/+EcuR0/XXe/Sfhb/9m9ZnDvXeK4+v+gqrnvoG3xt5Fdrrze1S+EDd9msZhn5Am+ua8e/vmxd+331uKiSjsnVXNDudQ6XtWxfdF/crCpZ/tvlAdtNUVezNJ+vOfOAVYDWBP1rgLeNMT6PaomHYO4kLFvOBkhzN8pyW4lZVQUPPFD3/pe/tObMq6vh/vvrrj/5pBXw3T7XGPjJT+quL1xoXXe6v137H84eyo/evJuU5DYkipVwrqqs4snrnuSvm19jyuhete1XvVvi8/utroYbv1HXPmdzSe1zI6Vy85ujv8n96+8noXVC7TXP9/vau6+Fp1NxKCQVoMaYY8aYFU0FchWdWrLjoZvEnDHWqpX61qyxAp7bXRDtnus0h19dDTk53tdzcpyf2zs1jZJthzh3vqr2elO7FNolRo2Bl572bv/S0ym2gdvzPhzHwOluipFP92YJsagbmYdwx0NPoM1dn0u7zs9iTBEiaZw5Oovycmu649vfhhtvtALtihXQsyfcdlsu6zd4FwFdPqLxHI7Tbopvvw3HjlnTQd/+NkydCitXWvdv2xaSknJp16Vef47NYugQ2H1wNm0uhDNlp+nYpSMVJyq4bOg83srdgUl8iYrzJ0ls0xGpuJ0RI+/n0sxy7vxxXXHQi79K4aO8JPYXJzba1dDpAIlXXuzo+hi4QLBbH15/N8VgTvuoOr5G5rprYoi5LQIKqxBvXSsCpYdz6dBrHlP/q66oZ+Wj8zj5GZSXZ1FQYAXhggIoL4dTp3NZvWGedxHQQqvYp2FAd9pN0fPbwPnzsGOH9T+LHTus9wmtc7kwfR5Tf16vP4/No+zERJLaJzHqtpF069+JY/uO8v6L7/P5jr+Q1H0LUx+fWnt258o5/8fuz6vYXzy3dufCpU+l8NdXO5Dat4Lr6wVuX7saGgOnT7by2sK2qWPgAsWpGAoIyDFzelxdy+nIXNkL046HCxbewNj7RzUq6tn45PtUnl7LJ5/UtR06FFq3d27vKfZpyG4u2rPUsWGxUkI7+/v//r664qOzcorEpCoO7/iSZ257htteuI3+V9QVaO1+bzd/mf0Xbvv+R7aJUfB/brz+CpmG9wnHfHqgdonU4+r8o7smKlfCuePhodIi26KeQ6VFzJ3r3XbuXN/tndjNRbdqZW0fUN+cOc73r198VH2qA5WVcNGQi6g4X0FaRppX+7QMa/dCp+Pb3OQU6o/eG94nHAK1S6QeV9dyGsyVF38LgXy9b4nu3dLYv8O7uGb/jmK6d0vj8ce92z7+uO/2Tuz679lGwN/7t2vXsfZ6+/Zgzl3Anm3FJLZJpCi/yKt9Ub61e6Gb49t89T0Q9wmUQCVGNcHachrMlcVT0dlEIVBurvcqktqkpUMlpltdk2ex8rF1FG+3inqKt+9l5aPr2Fs4i+3bramPP/3J+nP7dijYNoul977Eouk/Z/43H2DR9J+z9N6XOH1slt/9z8mB++7D9v5Hv5zF+ie9+7N+4TomjL6LNQv+zLa3NlJc/Ak7N73P6v9ey2VXXkbOnBwKNxVy+uRpCjcVkjMnh/S022rntle9W8IN3z7lVVzkj/pTLC25TyA5FQEFqohJi4b8pwlQVTc/3kSi0+0uiG4ZA926ZrF1K/z5oWcx5o81q0ceoUP7LPr0rtsad84cmD8fPt2RT5v2FVx511i69etO6Z5DrH/y7xw9mk91dZbXOnSn/r/zjrWOfehQ7/s//ji0a5fFuPGw/slnOVT6R7p3S2Py+EcAeG/7cjb/32ZOHztF+84dOHeygoSOF1NxZgevzn619izOxIRELuk/nG+M9y/R6UTEan+DnwnTUAjUMXN6XF3LaQI03rlMdLrdBdEtX/c3xrtIqLoafjC7LzcumkpaRl3CsSh/NzmzV/LCU97TI825v1NRklOi9rf3Pcf3nv9eo8pQt7sa+hJJxUQqtDQBqmw1J9HpdhdEt3zdv2FgbdUKzpw5Sb/MNK/r/TLTOHPGfs212/s7cUqMnjt9lh4DUzlXfa72enN2NfRFj4FTdjSYx6mWlOa7rcT0da+G733d3659u3Yd2ZNX5HV9T14R7dp1dHxmIPrvlBht374jJdsOeW2Po4k8FQo6Zx5vWlAI5FRBWX8O2t9Rot0GXDk58PnncOhQ4/t/9hl85StWQU/99mUH72L1Iy8yed6N9MtMY09eEasfyeHMsbsaTZM01f8+ffyrJAW4bvwsVi+cx3U/mUTq4L7s31Fcmxjd8PTrSNJV9MtI5dCnh7wqJZUzrQBtGQ3m8aSFhUBOFZRgXfc3kPtKRHbvbn//zz6zPvdc8wThthfM5VAhrLjnN1RUnCQxsSPHD95F795zbacjnPpfejiXD7/wr5K0/rWGidHLR2SxdVsGr859lpPnV5A2oJcm8vygFaAtpwnQeBHAis5AJOB8JSKh8f3Bvv3kyXX/I/AYM6ZuBO9v/59Y5L6StCkFrd+lW78yJqQNbNbfjydaAeofTYDGuUBXdAYiAecrEWl3f6f2rVpZgbs+X4Hcrr8izaskbcqQytEcPQIHK3Uj0aZoBWjLaTCPcS3ZujaYlZ5uE52+tq4NZkLTbSVpQ+WFA3nn4zJ3nYlDWgHacjpnHqtsEp1bt+Wy/i3/EnxuTwhywxj4xS+sefBvfatuDnzNGti4Ea66qnGi8/PP4Ysi761xc3NnUVCQxdGj1t9pSULWKaHpKRCy+/nYnWTUv3+W18/n0q7pFJQdpLC8kIFJ7qZb4ikhOP3701mc7bzFrmpak8FcRFKAPwIJwGngFmPM+QZtWgN7a74A7jHGfIIKD5v58a3b/N8qNtiVng2fVf/PqirrOfUTne+8A4ltbLbGfWwe58rhqquyWpSQBd8JTbs+796dy84D87jh4UkMGnkLO7cUs3bBvJrPs7yeXXmwJwUFZXQafpCerXv61Z94SwhqBWjL+XMG6A+BXcaYv4nI88Drxpg1DdqMwAryfv0urwnQ4KmdVmmQ6PS1taxdgi8UlZ45OY0Tl1OmwOrVjZ+7c69z/382e23IKyIXLLyBETNH0alvXX/Kivey7QX7n+dHh/dy0TcK/U6GakJQ2WlRAtQY85wx5m81b7sBh2yajQKuF5E8EVlaM1L3IiIzRCRfRPJLbQ5nUC3nNT/eINHpNsEXikpPu8Rlq1b2z/XV/3BURB4qLWLQSO/+DBrp/PM8vzedo0egsLzQr/trQlC55XcCVESuADobY963+fgD4GpjTCaQCExq2MAYs8QYk2GMyegWiafqRDM/djx0m+ALZKWn0/3dnLnZnARlMHXvlsbOLd792bnFuT+ZmXBy3SQOfOnf/TUhqNzyK5iLSBfg18DtDk22G2M8/5rmAwMC0DflDz8rOm23ln1sHV2TG28V27BScuFC689NmwIT0I2BH//YOmNzzBjr/mPGWO9vv906k7Phc5OT7LeivW68/Va3wWSM1Z+1C9ZRVryXi3pWUVa8l7UL1pGcNMvx55OZCUePwJtFTY/OdUtY5ZY/CdA2wKvAz4wxjbehs7wsIo8DBcAUYEHAeqic+VkI5Gtr2W5DshrNMQeq0tNXf6qqrDM8G57pecEFdXPn3s/N4oru/iUog00E+ve3nrvthWfJnW/1Z9DFj9C/f5bPn8/JdZNg0joOpvpOhmpCULnlTwL0B1jB+eOaS28BicaYufXaDAGWAwKsMcbMaXSjejQB2nJu1483J6EZzK1Wq6ut/cgbnuk5d27jwqFI3eK1uT8frQxVzeUrAarl/FGoJTse/uQnde8XLgxvkKyuhltuqXv/pz/53nY2lhS0fpexY/F7qaJSoOX8scPPo93suK24DDanMzerq4P/7EBpyc+t8mBP3vm4TEv9VcBoBWiEmDB7Nidslmwmp6Tw5qJFQdu69rPP4KtfDU6lp5PqauvMzf37YcSIuiPatm2zrj/9dOSP0FtaIeupDC2rKtPRuQoIDeYR4sTx4+TbLNnMOH48aFvXGmOVyYei0rNhfxISICkJBg+23g8eDJ9+al2PxPnx+gJVIducylClnGgwj3BnT1TU7XjYAllZ3kGmftGOJxB5glMgKz3tiMCvfgUrV8LmzdYXwLRpMHVq5Afz+qtsWvJzu7RrOh8Vwo4LC+mZpsFctUyE/zIb38oOnrVetDCQe7jZWjbYAVXECtz1RUMg9wjUz81tZahSTjSYR6LTp61AntgGkpOD+qhgV3pG2nM9z/b13p/2geq/28pQpZzoNEukOXKEsop2ViC/sAscLw3aowJ5pmc0PBf837q2fns3Z5U2p/+ZmbDzCLyJ/xtxKdWQBvMIkZySQsaBA5ytagNJVdC6Co6XkpTSPWjPDHalZ6Q91+3Wtc05q7S5/R90eBI7aboyVCknWjQUIVpyIlBLBbPSM9Ke63brWrdnlbak/wWt3yWhUxk3X66jc2VPi4YiXDgDOQTmTM9oea7brWvdnlXaEkMqR1NV1kkLiVSzaDAPpxZUdKrmcbt1bagTtVoZqppLg3m4LFtmVXSOG++6olM1j9uta4O9FbCdS7umU144kLKqssDfXMU0TYCGQwsrOlXzuN26NlyJWrC2BGaI+0OgVfzSBGgI1c6Ng06rhJHbxGs4ErVuzwxV8cFXAlRH5qHQgk2yVOC5TVyGI1F7fm86RwcWUniRjs6Vf3TOPNiiYG48HFvgKt+0MlS5pcE8mKJgbjw31zuZ50n65eaGs1cK3J0ZqlSTwVxEUkTkdRF5Q0Ryas4EtWu3VETeE5G5dp/Hm7XZO+p2O4zQQF6/wtET0D2rN86e1RF6JBh0eBJHj6BLFVWT/Jkz/w6wyBjzNxF5HsgC1tRvICJTgQRjzBUi8pKIDDDG7ApCfyNfFM2PB2orVxVcVWWdeOfjMsYM12PmlLMmR+bGmOeMMX+redsNOGTTbBywoub1G8CYhg1EZIaI5ItIfqnNiToxIYoCuUe4tsBV/vNUhirli99z5iJyBdDZGPO+zcftgZKa10eBHg0bGGOWGGMyjDEZ3WxO1Il6UZDotBPOrWiV/7QyVDXFr6WJItIF+DVwk0OTU0DbmtcdiLPEqrV+vOWnAYVaOLeiVe54TiUqG1KoUy3Klj8J0DbAq8DPjDHFDs22Uje1MhwoCkjvokC4N8lqCacKxyuvDH6Fo2qeggI9lUjZ82dkfgcwApgjInOAt4BEY0z9VSurgE0icjEwERgV6I5GnCicH7djdzaojsgj06Vd08lbl86BC9cxMC3cvVGRpslgbox5Hni+iTYnRGQccA2QbYyJ0QxnjRgJ5B7h2gJXNc9RPZVI2QjY3LYx5pgxZoUxJrYzNFGa6FSxwVMZqlRDujeLC9Ga6FSxRc8MVXbiatVJS0RzolPFnkGHJ1G6R08lUnV0ZN6UGJsfV7FFK0OVh47MffHMj2sgVxFIK0NVfRrMndTf8VADuYpQnspQXXuudJrFhiY6VbTwVIYyRIN5vNOReQOa6FTRSCtDlQZzj7w8K5Bf3EsDuYoql3ZN11OJlAZzQAuBVEzQU4nimwbzKDjaTammZGZaa89V/IrrBKgmOlWsOXoE/nykkJsv18rQeBO3I3NNdKpYNOjwJKrKtDI0HsXfyFwrOlUc0MrQ+BNfI3NNdKo4MKRyNOWFOs0Sb+ImmK/N3qGJThVXtDI0vvgVzEWkh4hs8vF5axHZJyIbar6GBq6LLec1P66BXMWBS7um6+g8zjQ5Zy4inYHfAe19NBsGvGKMiaxsos6Pqzh2fm86BQWFHLhQ9z2PB/6MzKuAW4ATPtqMAq4XkTwRWSoi4U+saiBXcU5PJYovTQZzY8wJP870/AC42hiTCSQCjf4NEpEZIpIvIvmlx4N8RKgmOpWqpZWh8SFQI+jtxpjymtf5wICGDYwxS4AlABkDBpgAPbcRLQRSqk5mJnB4Ev+6cF24u6KCLFCrWV4WkeEikgBMAT4O0H1d0UIgpeyV7unEn7cWajFRDHMdzEVksIjMb3D5MeBl4CPgPWPM3wPQN//pjodK+aSnEsU+v6dZjDHjav7cAcxt8FkB1oqW0NNEp1J+08rQ2BXdRUOa6FTKb1oZGtvCv4SwmTTRqVTzvPNxGUOGlDEwSQN7LInKkbkmOpVqHq0MjV3RNTLX+XGlWkwrQ2NT9IzMdX5cqYDQytDYFB3BXI92UyqgMjO1MjTWRPw0iyY6lQqOQYcnsZN1kBbunqhAiOiRuSY6lQquqjKtDI0VkTky10SnUiExpHI0BWXvhrsbKgAib2SuiU6lQqryYE/e+bhMR+dRLrKCuSY6lQo5z9rzsqqycHdFtUDETLNoolOp8CooAIYUamVolIqIkbkmOpUKL60MjX7hHZlrolOpiKGVodEtfCNzTXQqFVG0MjS6hSWYHz94VhOdSikVQOEZmSe2sebHNZArFVEyM60j5rTMP/qEJ5hf2CUsj1VKNW1I5WiOHkHXnUcZv4K5iPQQkU1NtFkqIu+JyFxf7ZRSka+qrJMWEkWZJoO5iHQGfge099FmKpBgjLkCSBeRAYHrolIq1PQA6Ojjz9LEKuAWYLWPNuOAFTWv3wDGALvqNxCRGcCMmrenvvUt+cxVT8OjK3A43J0IIf1+Y5t+v9Gvr9MHTQZzY8wJABHx1aw9UFLz+igwwuY+S4AlTT0vkohIvjEmI9z9CBX9fmObfr+xLVAJ0FNA25rXHQJ4X6WUUn4IVNDdijW1AjAcKArQfZVSSvnBdTm/iAwGphtj6q9aWQVsEpGLgYnAqMB0L+yialooAPT7jW36/cYwMcYE5kbWqpdrgLeNMbqeSSmlQihgwVwppVT4aKJSKaVigAZzH2oqXz8Mdz+CTURai8g+EdlQ8zU03H0KBRF5TkRuCHc/gk1EflDvn+1HIvJCuPsUTCLSWUTWiUh+rH+v9Wkw9+1J6pZcxrJhwCvGmHE1X5+Eu0PBJiJXAj2NMWvD3ZdgM8Y87/lnC2wCfhPmLgXbbcAfataYdxSRuFhrrsHcgYhMAE4D8ZDMHQVcLyJ5NXvsRMxxgsEgIolYAa1IRCaHuz+hIiK9gB7GmPxw9yXIjgBDRKQT0Bv4Z3i7ExoazG2ISBvgEeChcPclRD4ArjbGZAKJQKyfUPA9YAeQDWSKyD1h7k+ozAKeD3cnQuAdrLL3e4GdWFXpMU+Dub2HgOeMMWXh7kiIbDfGfFnzOh+I9Y3SLgOW1CyhXQaMD3N/gk5EWmF9nxvC3JVQ+DlwtzHmMaAQ+H6Y+xMSGsztXQ3MEpENwKUi8mKY+xNsL4vIcBFJAKYAH4e5P8G2G0iveZ0BFIexL6FyJbDFxMda5M7A0Jp/n0cC8fA96zrzpojIhprEUcwSkSHAckCANcaYOWHuUlCJSEfgJaAH1rTSzcaYEt9/K7qJyAIg3xizMtx9CTYRyQR+izXV8h5wozHmVHh7FXwazJVSKgboNItSSsUADeZKKRUDNJgrpVQM0GCulFIxQIO5UkrFAA3mSikVA/4foE2ZH9XkgTkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "#resolution为区间步长\n",
+    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
+    "\n",
+    "    # setup marker generator and color map\n",
+    "    markers = ('s', 'x', 'o', '^', 'v')\n",
+    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
+    "    #按照标签种类数取图像颜色的种类数\n",
+    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
+    "\n",
+    "    # 图像的横纵坐标取值范围\n",
+    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "    #将间距分成网格\n",
+    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
+    "                           np.arange(x2_min, x2_max, resolution))\n",
+    "    #ravel的是为了降成1维，然后合并成两个特征的矩阵进行预测\n",
+    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
+    "    Z = Z.reshape(xx1.shape)\n",
+    "    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n",
+    "    #plt.xlim(xx1.min(), xx1.max())\n",
+    "    #plt.ylim(xx2.min(), xx2.max())\n",
+    "\n",
+    "    for idx, cl in enumerate(np.unique(y)):\n",
+    "        plt.scatter(x=X[y == cl, 0], \n",
+    "                    y=X[y == cl, 1],\n",
+    "                    alpha=0.8, \n",
+    "                    c=colors[idx],\n",
+    "                    marker=markers[idx], \n",
+    "                    label=cl, \n",
+    "                    edgecolor='black')\n",
+    "\n",
+    "    # highlight test samples\n",
+    "    if test_idx:\n",
+    "        # plot all samples\n",
+    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
+    "\n",
+    "        plt.scatter(X_test[:, 0],\n",
+    "                    X_test[:, 1],\n",
+    "                    c='',\n",
+    "                    edgecolor='black',\n",
+    "                    alpha=1.0,\n",
+    "                    linewidth=1,\n",
+    "                    marker='o',\n",
+    "                    s=100, \n",
+    "                    label='test set')\n",
+    "        \n",
+    "svc=SVC(kernel='linear')\n",
+    "svc.fit(x,y)\n",
+    "plot_decision_regions(x,y, classifier=svc, test_idx=None, resolution=0.02)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a990dd86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_26672\\2116216888.py:27: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
+      "  plt.scatter(x=X[y == cl, 0],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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KLxrMo5zrDtDkZPtkp11jEFZTgN3K9ZXAd2yun8E++erVfABP1sftiED79vWB3H/J5d/70jlyeCv7++1vsJuibUfknXewekVHPil6g3cXbeD08ROc0ymVYwdrOP2F2CZS3frV//6K5SuWc8PCG0jPTKe0sJTlc5cDaEBXtsREoMg2c9AgUxilW6Ymmsxbb7Xf57ykhL0ZGU3HHz3qLnHpVogCub/Gwdb3eVHbDfQYWBFUZ+jtN83iP2aOpVOf+lqg43t38M+l77Ho1RdbPcdxw8dxw8IbuPDS+iT0zvd38qf7/sTazWtbfX8VmwamDNxojMm0+5qumavoUFDgaaIzkMZPzb7P3XSG7i3bw/DxDdfHh4/vy16PEqAnjp0gPTO9wbX0zHROHDvhyf1V/NFlFhVx1tp4Kpx/LTzoXRB3egIPpLJ4MO9RzNgRBDy8os+Avmxes6fBk/nmNXvoM6Bvi963sdTOqZQWljZ4Mi8tLCW1c6q7G9m8vxfLQCr6aDAPM7cdnU7jPzt4kHPs/ot06PR08u8jR9h65EiT62FJdIZwSaW+09MKXL7kZ/v2kJ3t/Pcu7p5BUcV+KqorHIO5MdC9y22seOxppv239US+ec0eVjy2mh6dHuCROws5dPTX7C2zgnv3Lrcx7JIJnN//z0F3jM6YOYPlc5cz7fFpdWvmK+auYMbMGa46UvftvoaTx9tw6w+P1v0cFj+d1qKOVxXdNJiHmduOTqfxvcvL2duuXZPrTp2eTs5in+g86zAnz87u9O2tEoJA3rjTs75qxUp+NvdkemrDGIq65NFlxH7bgC4Cwy6xDoL+59Jf8/aTVtAenflDdn0KH2x8mmn/fRU31QX5p/l878d80eZvTA6yY9SX5HztvobVLMNGDgu6I/X5nBfIOK8j//x4KgC3/vAoi59OY9WyVK79xglXHa9O81TRQxOgYeaUcHRKLDqN771zJ/tTUppc73P2LHv9C6k9no8nWrFJVrD8yxB9/KtYmvPxwRLO+1rgbXLtli9mzbRPjC6f81KDjlGAz/7xGRue2cCLLhKms26axZi7xjS5T+OOVN/19c9sICvrD6xaVr88c+03TtQ9qQd7f7fzVKGhCVAVNbxuAnLSuNMTgg/kYC23HD4ExZX2naG+92j8uVNi1KuOUbcdqXvL9nDrDxv+huUUyAPdXztbo58GcxU2XjcBBdK40xOsz938IlpZPJiiInfv60uM+tu8Zk+LOkYbpy1qatx3pPYZ0JfFTzf8zWvx02mOPwc9AzR26Zp5lHNKUDo5XVND5q23NrnulDBtzVa0QQtD7bg/505P6+vBPqH7kqHBnkrkS4y++sD/o2u/NnXNRIf/VcNFX7qZt3LyG6yZBzpb877v9qB019/o3udZ9u7eQ5/+fTm4dzYdOtxJfs5PbTtSn/vuy7Rpe4bKU5WkdEihpqodA/r+sm6N3H/NHOyf0PUM0NilwTzM3HZ0OiUoK7FPdiaDq4Rpi7aidSMM6+ONBer0bN/eXVneqQ1jONwlj/197ZOhjd/3nPaGzt3bcfntDdv8L7hwBOOyBwV1tmZNDZTu+hsHjv+MrOuyuWny+Xz41j62PvkzevIAd997B6826kj9v2d3kNK5DVPnzaD/V/qx+x//4o1HV3G0YhPTv315XeD2Lbl07FRj+3PQM0BjlyZAo1y0JUxdiUAg9+dVfXUwyVAfrxKIt39zFhnXXU5av/o686P/2klJ7rss+kPT+4wbPo7rfnYD/TPrE6+7C3eQ+8CfWPPJWq0zjxOaAFVhF65EZyBOnZ5uBZMM9fEqgbh39x5GTT6/wbVRk89n7277+5w4doKMUekNrmWMsjpGvfo5qOgW7IHOXUVkkoh0D/WEVOxrnOhs/Mtfc78M2iX+WnIfL8f7kqHNtfq3JIFo9759+vflw7ca7sv74Vv76NPf/j6pnVMp+bC0wbWSD1vWMapiUzBngJ4L/Bl4E1goIhONMeU24xYDQ4A3jTGPez7TGOPUufn5sWOc17lzk+tOHaBufVZezojyJv94OAN8YbPVbRXYJkxbNB+bRKfbTswFC+DUKZgzB9q0sQL5E09ARQWMHRv8ffLzYefOfI5VPsuB8lJ69kinc8psLrwwmx4981n9Tv31qybMpvxAtuP47OzgOkMhcALx/u/fz9p31nL2zFmS2yUzbsI4Lhn9YpMOzV//PI2N797DpsIH6dY/ibOnjpPcoROHdlfTMemnrMlfy6u/9evQ/O5MMtJvJnfuUq57fBoZo9Ip+bCU3LkryEi/2XFZJVJnm+qZqqERTAJ0OHCfMeaD2sA+EljtP0BErgeSjDGXisjLIjLIGLMjBPONGU6dm30OHXLVAeo2YZoMvGpzfQLQ9BA4K8Hq6oxRJzbr4247MWtqrEC+ebMVwOfMsf785BPo1QvefTe4+xhjBfJt++Zx7SNTuGjUjWz7sIxV8+dx8FAhNalvcdX9U+g75Eb2bC3jjafm0eZEIeWVbzUZb90vG5H6ztB93ZzXz50SiKuWreKjTz7iW89/iwFfHUDZR2Xkzsllx9a7wPwWqO/Q/HNttck5qSlc9v0seg3qyr93HCY/p4AzFRt54YW3mnSSXnThfQDkPvBKXcdoRvrNXPn1exFp2rYfqbNN9UzV0Ak6ASoilwOPA9cYY441+tovgXxjTJ6IfBNob4z5jdO9EiEBGqmtZftMm+Z4pud+mzM9PZlPgESn205M35P45s3114YPh0cegZUrg7/P/AXXMnLWaLoMqP/eKspKyJ27iO/84nYGDK+/Xra5hN/ds4jrHr+9yfhNL37AIz9a1eDe27rnMXQoDE5pPiHq89WBX+Vbz3+LgV8bWHdt1/pd/P7O3zPrB9ubdGgWfPhNhnxrLJ39OkmP7d1BboBO0heWvhh0otNtJ6lXHaCRet940eoEqIgIcCNwBOthrrGOwN7a14eBXjb3uF1ECkWksNztU5+KWs0lOt12YrZpYwVuf488AklJMHVqw+tTpzrf50B5KReNGtDg2kWjBnDq1HH6Dml4ve8Q67rd+APlpU3WtE9vC9xMZLcGfvbMWQZ8teH9B3x1AGfPnOWWexr+93DLPUfZu3sPIxp1ko5oppPUTaIzUmeb6pmqoRNUMDeW2cBm4Os2Q04A7Wtfp9rd1xizyBiTaYzJ7BHqJpUoVl1Tw9bS0iYf/3bRGBQVCgqC6uh024mZlwd3393w2t13w89+Zj2x+/6eMdbn+fn29+nZI51tH5Y1uLbtwzKSkzvZXm+T1ImP3y1rcP+P3y2j8nR6g/kaA6XvZfD+q1/ml68sYdZNs7h6zNXMumkWa99ey6svdWrQYenbpTCpbTJlHzV837KPykhqm8ysGxo++8y6oRdtkwd41klqpyVnm659e22T79ctPVM1dJoN5iLykIj4+la6ABU2wzYCY2tfjwBKPZhb3BqSlNTkoyaMZ2u2mn+iM4hA7lvbXrDA+nPdOvuAXl0Nb74Je/ZAz57whz9Yf/7rX/DRR9bSS9eu8NRT1p+bN1vXG//ojIHOKbNZNT+PirISzutdTUVZCSvn55FibmPl/DyK1pdQXVVN0XrrevWJ28jLyePADmv8gR0lvPnTPCqPz+bdd+vn6/t+yt7bwV+W5pL1gyzuX30/Y+4aw/PPvcCWje+wallqXUD3dVz2PW8SuXNy2bV+F1Vnqti1fhe5c3JJaZPNhjXt6dm7itz1e+nZu4r177Tn0N7ZrHhsNcf37uCCQV9wfO8OVjy2mox0q5P0s398RnVVNZ/94zPyc/KZ+b2Zrv4RzvzeTPJt7jNj5gzb68NGWLs1jrlrTN33+8JzL7gO6G7f1+33lciaXTOvTXouA1KAIuBZ4CZjzFy/MZ2BdcDfgMnAaGOM41pKIqyZO1WzlJSXk9G2ad55D7B/+fJWv2+fGTPApjP0tDFc0L1pZanr6hqXjUAtqWYpKbGOCPVVs5w9CykpcO65cPhw/X26doXMTJg82f597apTBg7MZteufDb981mqqkpp2zadkf9hXf/b3/I5fLL+eteOs/nP/8zm9Omma/XbSq5l9A9HkjHmPDqlnAM0v0vhA7fZVLOMepG/53Xg35+3rfu+ep1XRafONZzT4S0OVrRuX/RA3FSVLP3NUs92U9RqlpYLtGbuWQdobdCfBLxrjAlYjJsIwdxJRLac9UhLN8py24lZXQ0PPFD/+c9+Zq2Z19TA/ffXX3/qKSvgu31fY+BHP6q/vmCBdd3p/nbj7/3xMO5d9RBnzzlB+/ZwTptzqK6q5qmrnuLP699k2pg+deNXbNgb8PutqYHrvlY/Pnf93rr3jZbOzavHXM39q+8nqW1S3TXf9/vmhjcjM6kEFJYOUGPMEWPMsuYCuYpNrdnx0E1izhirasXfypVWwHO7C6Ld+zqt4dfUQG5uw+u5uc7v27NHOnu2llF9NI0vvoCz5myzuxTaJUaNgZd/0XD8y79Isw3cvs/dNkN5QXdTjH66N0uYxdyTeRh3PPQF2vzV+XQ491mMKUUknVOHZ1NZaS13fOMbcN11VqBdtgx694abb85n9ZqGTUCXjGy6huO0m+K778KRI9Zy0De+AddfD8uXW/dv3x5SUvLp0NVvPkdmM2wo7Nx/Hyldkzhx5Dgdu3Wg5ng1Xxk2j3fyt2KSX+bsmeMkt+uEnL2FkaPu5+KsSr5/b31z0Es/T+PjghT2lCU32dXQ6QCJV1/q5PoYOC/Y1Yf776YYymUfVS/Qk7numhhmbpuAIirMW9eKQPnBfFL7zOP6/65v6ln+k3kc/xQqK7MpKrKCcFERVFbCiZP5vLFmXsMmoAVWs0/jgO60m6Lvt4EzZ2DrVut/Flu3Wp8ntc2nW8Y8rv8fv/k8No+KY5NJ6ZjC1747iq7pXTlYVs76l9azfeufSOn5Idc/cX3d2Z3L5/wfO7dXs6dsbt3OhYufTuPPr6XSd8BZrvEL3IF2NTQGTh5v02AL2+aOgfOKUzMU4Mkxc3pcXevpk7myF6EdD+cvuJZx949u0tSz9qkPqDq5ii1b6scOGwZtOzqPb9zs42O3Fu0rdWzcrJTUwf7+v7unafPR9s1F/PbO5/nOi9/hwkvrG7R2vr+TP933J27+3se2iVEIfm3cv0Km8X0isZ7u1S6RelxdcHTXROVKJHc8PFBeatvUc6C8lLlzG46dOzfweCd2a9Ft2ljbB/ibM8f5/nbNR+d/KYOqM1UMyGx4PT3T2r3Q6fg2NzkF/6f3xveJBK92idTj6lpPg7lqINhGoECft4Yvsehvz9YyevZI54knGo594onA453Yzd+3jUCw9+/QoVOT64e27ycpuS2ffVTa4HppobV7oZvj2wLN3Yv7eMWrxKgmWFtPg7my+Do6m2kEys+nSUfkihXOnZhude88m+WP5VG22WrqKdtcwvKf5FFSPJvNm62ljz/+0fpz82Yo2jSbxXe/zMKZ/8PjVz/Awpn/w+K7X+bkkdlBzz83F+65B9v7H/58Nqufajif1QvymDjmNlbOf51N76ylrGwLm95Zy8r5r9O/27WsmJtL8bpiTh4/SfG6YnLn5JKRfnPd2vaKDXu59hsnGjQXBcN/iaU19/GSUxOQV01M2jQUPE2Aqvr18WYSnW53QXTLGOjRPZuNG+H1h5/FmD/UVo88SmrHbPr3q98ad84cePxx+OfWQtp1PMtlt42jx8CelO86wOqn/srhw4XU1GQ3qEN3mv9771l17MOGNbz/E09Ahw7ZjJ8Aq596lgPlf6Bnj3SmTngUgPc3L2X9/63n5JETdDw3lcqT1YwcPojV7/2NZfcu48wXZ0jpkEJyUjIXXDiCr00ILtHpRMQaf22QCdNw8OqYOT2urvU0AZroXCY63e6C6Fag+xvTsEmopgbuvG8A1y28nvTM+oRjaeFOcu9bzotPN1wGacn9nZqSnBK1vsRo94EZJKUdJbVDEns+3uN6V8NAoqmZSIWXJkCVrZYkOt3uguhWoPs3Dqxt2sCpU8cZmJXe4PrArHROnbKvuXZ7fyfNJUY7dgRTncTpM9Ut2tUwED0GTtnRYJ6gWtOa77YTM9C9Gn8e6P524zt06MSugtIG13cVlNKhQyfH9/Ri/sEkRtubVKqqoGRziSbyVMjpmnmiaUUjkFMHpf8adLBPiXYbcOXmwvbtcOBA0/t/+il86UtWQ4//+Ir9t/HGoy8xdd51DMxKZ1dBKW88msupI7c1WSZpbv79+wfXSQpw1YTZvLFgHlf9aAp9hwxgz9ayusTo6gV5ddd3b/qct5/J4/5777a9j6qnHaCto8E8kbSyEcipgxKs68EG8kCJyJ497e//6afW133XfEG4/TlzOVAMy+76NWfPHic5uRNH999Gv35zbZcjnOZffjCff3wWXCep/7XGidFLRmazcVNmg+tZV9xMebde7K/aH/D80ESmHaCtpwnQROFhR6cXCbhAiUhoen+wHz91av3/CHzGjq1/gg92/k8udN9J6sbHB0s472vOZ4cmOu0ADY4mQBOc1x2dXiTgAiUi7e7vNL5NGytw+wsUyO3mK9KyTlI3zpRkcPgQFFcWe3K/eKMdoK2nwTzOtWbr2lB2erpNdAbaujaUCU23naROsrKgsngw+z53N69EoR2gradr5vHKJtG5cVM+q98JLsHn9oQgN4yBn/7UWgf/+tfr18BXroS1a+Hyy5smOrdvh89KG26Nm58/m6KibA4ftv5OaxKyTglNX4OQ3c/H7iSjCy/Mdvz5XNw9g22Hiik+r5jBKc0vtyRSQnDm92byQo7zFruqec0GcxFJA/4AJAEngRuNMWcajWkLlNR+ANxljNmCigyb9fGNm4LfKjbUnZ6N38v/z+pq6338E53vvQfJ7Wy2xn1sHqcr4fLLs1uVkIXACU27Oe/cmc+2ffO49pEpXDTqRrZ9WMaq+fNqv57t+N7H86ZQRB77ugVeP0+0hKB2gLZeMGeA/hewwxjzFxF5HnjLGLOy0ZiRWEE+qN/lNQEaOnXLKo0SnYG2lrVL8IWj0zM3t2nicto0eOMN+zM3neb/4/tWhb0jcv6Caxk5azRdBtTPp6KshE0vBpcw3dY9j7EjujhWt2hCUNlpVQLUGPOcMeYvtZ/2AA7YDBsNXCMiBSKyuPZJvQERuV1ECkWksNzmcAbVeg3WxxslOt0m+MLR6WmXuGzTxv59A80/Eh2RB8pLuWhUw/lcNCr4hGll8WDe+6SC/VX2pyxqQlC5FXQCVEQuBc41xnxg8+WPgCuMMVlAMjCl8QBjzCJjTKYxJrNHNJ6qE8uC2PHQbYLPy05Pp/u35MxNf80lKEOpZ490tn3YcD7bPgx+Phd3z6C6ootjQNeEoHIrqGAuIl2BZ4BbHIZsNsb48vSFwCAP5qaCEWRHp+3Wso/l0b1z061iG3dKLlhg/blunTcB3Ri4917rjM2xY637jx1rfX7LLdaZnI3ft3OK/Va0V02w3+o2lIyx5rNqfh4VZSWc17uairISVs3Po3PK7KB/PkOrxlBZPJiteyqafE23hFVuBZMAbQe8BvzYGNN0GzrLKyLyBFAETAPmezZD5SzIRqBAW8v2GJrdZI3Zq07PQPOprrbO8Gx8puc559SvnTd832wu7RlcgjLURODCC6333fTis+Q/bs3novMf5cILnZOfds6UZHB4cNMKF00IKreCSYDeiRWcP6m99A6QbIyZ6zdmKLAUEGClMWZOkxv50QRo67mtH29JQjOUW63W1Fj7kTc+03Pu3KaNQ9G6xatXP5+CAug0JY+u3dAOURVQoASotvPHoNbsePijH9V/vmBBZINkTQ3ceGP953/8Y+BtZ+OZL6AHqnBRStv540WQR7vZcdtxGWpOZ27W1IT+vb3i5c8tKwuqK7rYrp8rFQztAI0SE++7j2M2JZud09L4+8KFIdu69tNP4ctfDk2np5OaGuvMzT17YOTI+iPaNm2yrv/iF9H/hB6KDtmhVWMo2rWB1w8V6xO6ci3K/5NJHMeOHqUwLa3Jx7GjR61E55pUK9HpMpCDc0Jz7Fjr6/5VKr4g/8UXoXtCF7HO3ExJgSFDrM+HDLE+T0qKzvVxf/4dsl7/3IZWjaG6ogsV1RUezVYlCn0yjwF1Ox62QnZ2wwSdf9OOLxD5kqNednraEYGf/xyWL4f1660PgBkz4Prroz+Y+1fZhOLnVrW/N0VFFXQZofufq+Dpk3mU++LYWc/u5WZr2VAHVBErcPuLhUDuE8qf28XdM+o6RHXLXBUsDeZRrGL/F9aLVj6VBxLqTs9oe1/fewf6PJjxoZ7/xd0zOJ43RbfMVUHTZZZodPIkFcfbQHI7aJ8Usrfx8kzPWHhfcL91rduzSr2cf1YWbDsEf0dPKFLN02AeJTqnpZF59CicPMkX1e0g5RxoW01KWs+QvWeoOz2j7X3dbl3bkrNKvZ7/RQensI08Xj9UzPRLNKArZ9o0FE08PKfTjVB2ekbb+7rdutbtWaWhmn9R2w2MG4cmRBOcNg3FAK/P6XQjElvIRup93W5d6/as0lCp2t874Ja5SmkwjwKtOadTueN269pIJmr9aYWLao4G80hqRXu+cs/t1rWh3grYLV9ALypCA7pqQhOgkRKh9fFE5nbr2kglagO5uHsGBXkZ7OuWx+D08L+/il6aAI0EDeQR5TbxGqkEcSDbuuuWuYlIE6BRJJKJTmVxm7iMVII4kIsOTuHwIXh9oy63KIsG83AqKLD+jLL18Uhsgata76KDU6iu6MLrG4u1ykVpMA+nui1so0h+fsNkni/pl58fyVmpYPnOEdVdFlWzwVxE0kTkLRF5W0Rya88EtRu3WETeF5G5dl9PaP5VKy3YwjZUQrmVqwovrXBRwVSzfAtYaIz5i4g8D2QDK/0HiMj1QJIx5lIReVlEBhljdoRgvrEnipOdod7KVYWHr8KliDz2ddN9XBJVs0/mxpjnjDF/qf20B3DAZth4YFnt67eBsY0HiMjtIlIoIoXlNifqxKNYSHZGagtc5a2sLDieZyVF/16qT+iJKOg1cxG5FDjXGPOBzZc7AntrXx8GejUeYIxZZIzJNMZk9khLa9FkY0msdHVGS4ejar2sLCspWr6riyZEE1BQTUMi0hV4BrjBYcgJoH3t61QSObHairM6wy2SW9Gq0HrvkwrGjtCNuRJJMAnQdsBrwI+NMWUOwzZSv7QyAij1ZHaxJoYCOTh3OF52WeQ6HFXr+c4R1Y25EkswT+a3AiOBOSIyB3gHSDbG+FetrADWicj5wGRgtNcTjXpRnOgMxO5sUH0ij31Dq8bwcXEJ71HM0KEVDE7RpGi8azaYG2OeB55vZswxERkPTAJyjDGJkeH048Why5ESjR2OqvW0yiWxeLa2bYw5YoxZZoxJuN/r6pKdSkUZX5WLin+Jm6j0SKxUrajElZWFliwmAA3mLaV7kasY4itZ1H1c4pcG85ZYsiSmqlaUAq1yiXcazN3yr1rRQK5ijG9jLj1+Lv7oSUMuWOvjsVu1ohToaUXxSp/M3dJAruKEJkXjiwbzIGn5oYonvn1c9LSi+KHBvDlataLimJ5WFD80mAfiq1rRZKeKY5oUjQ8azJ3E6F4rSrXExd0zqCwerCcWxTCtZrGhVSsqEeleLrFNn8ydaCBXCUgTo7FLg3kjWrWiVH1iVJOisUOXWXxi7GAJpcJBTyyKHfpkDlq1opQNrXKJLUEFcxHpJSLrAny9rYjsFpE1tR/DvJtiaK3K2apVK0o58K9y0W7R6BbMGaDnAr8FOgYYNhx41RgzvvZji1cTDIsHH9JArpSDi7tncDxvirb/R7lgnsyrgRuBYwHGjAauEZECEVksIjGxFq/JTqWC46ty0T3Ro1ezwdwYcyyIMz0/Aq4wxmQByUCTc6pE5HYRKRSRwvKjET4iVFv0lWoR3RM9enmVAN1sjPm89nUhMKjxAGPMImNMpjEms0damkdv2wJataJUq2hiNDp5FcxfEZERIpIETAM+8ei+3tKqFaU84VtH18Ro9HAdzEVkiIg83ujyY8ArwMfA+8aYv3owt9DQqhWlPJGVhSZGo4gYY8L+ppmDBpnChQvD+p51yU5dI1fKc0VtN5DUpYKxI7pog1EIDUwZuNEYk2n3tYRoGtJArlRoaWI08mKihLDFNNmpVNgMrRrDx8UlvEcxQ4dWMDhFd10Mp/h9MtdArlTYaWI0cuI3mG/frlUrSkWAf2JUt9ENn/gM5r5TgpRSEeHrGNXzRcMn7tbM9ZQgpaKHrqOHT1w9mWvVilLRR88XDY+4CuaABnKlopAmRkMvPoK5/8ZZSqmopOeLhlbsB3Pdb0WpmKKJ0dCI7WDuq1rR/VaUiim686L3YjuYgwZypWKUJka9FbPBvO7sTg3kSsUsTYx6JyaDuZYgKhU/NDHqjdhrGiooAFI1kCsVZy46OIWithvqAvrQoWiTkQuxFcz9N89SSsWdoVVj4CB8fLCEIorZ162Yieka0IMRO8ssWoKoVMLwraXrKUbBi40nc18Joi6tKJUwsrKAg1MoqtjA64eK9RSjZgT1ZC4ivURkXTNjFovI+yIy15up1aurJVdKJRw9xSg4zQZzETkX+C3QMcCY64EkY8ylQIaIDPJqgnWVK1qCqFTC8m8y0oBur9kDnUWkMyDAG8aY8Q5jfgnkG2PyROSbQHtjzG8ajbkduL320y8Dn7Zy7uHQHTgY6UmEkX6/8U2/39g3wBjTw+4Lza6ZG2OOAYhIoGEdgb21rw8DTU6GMMYsAhY1937RREQKnU7Cjkf6/cY3/X7jm1fVLCeA9rWvUz28r1JKqSB4FXQ3AmNrX48ASj26r1JKqSC4Lk0UkSHATGOMf9XKCmCdiJwPTAZGezO9iIupZSEP6Pcb3/T7jWPNJkCDvpFV9TIJeNcYo+lmpZQKI8+CuVJKqcjRRKVSSsUBDeYB1Ha+/iPS8wg1EWkrIrtFZE3tx7BIzykcROQ5Ebk20vMINRG50++f7cci8mKk5xRKInKuiOSJSGG8f6/+NJgH9hT1JZfxbDjwqjFmfO3HlkhPKNRE5DKgtzFmVaTnEmrGmOd9/2yBdcCvIzylULsZ+H1tjXknEUmIWnMN5g5EZCJwEkiEZO5o4BoRKajdYyc2NmBrIRFJxgpopSIyNdLzCRcR6QP0MsYURnouIXYIGCoiXYB+wL8iO53w0GBuQ0TaAY8CD0d6LmHyEXCFMSYLSAamRHg+ofYdYCuQA2SJyF0Rnk+4zAaej/QkwuA9YABwN7ANqys97mkwt/cw8JwxpiLSEwmTzcaYz2tfFwKebZQWpb4CLKotoV0CxP2WnCLSBuv7XBPhqYTD/wB3GGMeA4qB70V4PmGhwdzeFcBsEVkDXCwiL0V4PqH2ioiMEJEkYBrwSYTnE2o7gYza15lAWQTnEi6XAR+axKhFPhcYVvvv8yggEb5nrTNvjoiscdotMl6IyFBgKdbumCuNMXMiPKWQEpFOwMtAL6xlpenGmL2B/1ZsE5H5QKExZnmk5xJqIpIF/AZrqeV94DpjzInIzir0NJgrpVQc0GUWpZSKAxrMlVIqDmgwV0qpOKDBXCml4oAGc6WUigMazJVSKg78f70nQd3ca0AUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#核svm\n",
+    "svm=SVC(kernel='rbf',random_state=1,gamma=0.10,C=10.0)\n",
+    "svm.fit(x,y)\n",
+    "plot_decision_regions(x,y,classifier=svm)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "added54b",
+   "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
+}