水质检测
Signed-off-by: zhang117228 <13198271+zhang117228@user.noreply.gitee.com>
This commit is contained in:
zhang117228
@@ -0,0 +1,564 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "78c67687",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['images/1_1.jpg',\n",
|
||||
" 'images/1_10.jpg',\n",
|
||||
" 'images/1_11.jpg',\n",
|
||||
" 'images/1_12.jpg',\n",
|
||||
" 'images/1_13.jpg',\n",
|
||||
" 'images/1_14.jpg',\n",
|
||||
" 'images/1_15.jpg',\n",
|
||||
" 'images/1_16.jpg',\n",
|
||||
" 'images/1_17.jpg',\n",
|
||||
" 'images/1_18.jpg',\n",
|
||||
" 'images/1_19.jpg',\n",
|
||||
" 'images/1_2.jpg',\n",
|
||||
" 'images/1_20.jpg',\n",
|
||||
" 'images/1_21.jpg',\n",
|
||||
" 'images/1_22.jpg',\n",
|
||||
" 'images/1_23.jpg',\n",
|
||||
" 'images/1_24.jpg',\n",
|
||||
" 'images/1_25.jpg',\n",
|
||||
" 'images/1_26.jpg',\n",
|
||||
" 'images/1_27.jpg',\n",
|
||||
" 'images/1_28.jpg',\n",
|
||||
" 'images/1_3.jpg',\n",
|
||||
" 'images/1_30.jpg',\n",
|
||||
" 'images/1_31.jpg',\n",
|
||||
" 'images/1_32.jpg',\n",
|
||||
" 'images/1_33.jpg',\n",
|
||||
" 'images/1_34.jpg',\n",
|
||||
" 'images/1_35.jpg',\n",
|
||||
" 'images/1_36.jpg',\n",
|
||||
" 'images/1_37.jpg',\n",
|
||||
" 'images/1_38.jpg',\n",
|
||||
" 'images/1_39.jpg',\n",
|
||||
" 'images/1_4.jpg',\n",
|
||||
" 'images/1_40.jpg',\n",
|
||||
" 'images/1_41.jpg',\n",
|
||||
" 'images/1_42.jpg',\n",
|
||||
" 'images/1_43.jpg',\n",
|
||||
" 'images/1_44.jpg',\n",
|
||||
" 'images/1_45.jpg',\n",
|
||||
" 'images/1_46.jpg',\n",
|
||||
" 'images/1_47.jpg',\n",
|
||||
" 'images/1_48.jpg',\n",
|
||||
" 'images/1_49.jpg',\n",
|
||||
" 'images/1_5.jpg',\n",
|
||||
" 'images/1_51.jpg',\n",
|
||||
" 'images/1_6.jpg',\n",
|
||||
" 'images/1_7.jpg',\n",
|
||||
" 'images/1_8.jpg',\n",
|
||||
" 'images/1_9.jpg',\n",
|
||||
" 'images/2_1.jpg',\n",
|
||||
" 'images/2_10.jpg',\n",
|
||||
" 'images/2_12.jpg',\n",
|
||||
" 'images/2_13.jpg',\n",
|
||||
" 'images/2_14.jpg',\n",
|
||||
" 'images/2_15.jpg',\n",
|
||||
" 'images/2_17.jpg',\n",
|
||||
" 'images/2_18.jpg',\n",
|
||||
" 'images/2_19.jpg',\n",
|
||||
" 'images/2_2.jpg',\n",
|
||||
" 'images/2_20.jpg',\n",
|
||||
" 'images/2_21.jpg',\n",
|
||||
" 'images/2_22.jpg',\n",
|
||||
" 'images/2_23.jpg',\n",
|
||||
" 'images/2_24.jpg',\n",
|
||||
" 'images/2_26.jpg',\n",
|
||||
" 'images/2_27.jpg',\n",
|
||||
" 'images/2_28.jpg',\n",
|
||||
" 'images/2_29.jpg',\n",
|
||||
" 'images/2_3.jpg',\n",
|
||||
" 'images/2_30.jpg',\n",
|
||||
" 'images/2_31.jpg',\n",
|
||||
" 'images/2_32.jpg',\n",
|
||||
" 'images/2_33.jpg',\n",
|
||||
" 'images/2_34.jpg',\n",
|
||||
" 'images/2_35.jpg',\n",
|
||||
" 'images/2_36.jpg',\n",
|
||||
" 'images/2_37.jpg',\n",
|
||||
" 'images/2_38.jpg',\n",
|
||||
" 'images/2_39.jpg',\n",
|
||||
" 'images/2_4.jpg',\n",
|
||||
" 'images/2_40.jpg',\n",
|
||||
" 'images/2_41.jpg',\n",
|
||||
" 'images/2_42.jpg',\n",
|
||||
" 'images/2_43.jpg',\n",
|
||||
" 'images/2_44.jpg',\n",
|
||||
" 'images/2_5.jpg',\n",
|
||||
" 'images/2_6.jpg',\n",
|
||||
" 'images/2_7.jpg',\n",
|
||||
" 'images/2_8.jpg',\n",
|
||||
" 'images/2_9.jpg',\n",
|
||||
" 'images/3_10.jpg',\n",
|
||||
" 'images/3_11.jpg',\n",
|
||||
" 'images/3_12.jpg',\n",
|
||||
" 'images/3_13.jpg',\n",
|
||||
" 'images/3_14.jpg',\n",
|
||||
" 'images/3_15.jpg',\n",
|
||||
" 'images/3_16.jpg',\n",
|
||||
" 'images/3_17.jpg',\n",
|
||||
" 'images/3_18.jpg',\n",
|
||||
" 'images/3_19.jpg',\n",
|
||||
" 'images/3_20.jpg',\n",
|
||||
" 'images/3_21.jpg',\n",
|
||||
" 'images/3_22.jpg',\n",
|
||||
" 'images/3_23.jpg',\n",
|
||||
" 'images/3_25.jpg',\n",
|
||||
" 'images/3_26.jpg',\n",
|
||||
" 'images/3_27.jpg',\n",
|
||||
" 'images/3_28.jpg',\n",
|
||||
" 'images/3_29.jpg',\n",
|
||||
" 'images/3_3.jpg',\n",
|
||||
" 'images/3_30.jpg',\n",
|
||||
" 'images/3_31.jpg',\n",
|
||||
" 'images/3_32.jpg',\n",
|
||||
" 'images/3_34.jpg',\n",
|
||||
" 'images/3_38.jpg',\n",
|
||||
" 'images/3_40.jpg',\n",
|
||||
" 'images/3_42.jpg',\n",
|
||||
" 'images/3_43.jpg',\n",
|
||||
" 'images/3_44.jpg',\n",
|
||||
" 'images/3_45.jpg',\n",
|
||||
" 'images/3_46.jpg',\n",
|
||||
" 'images/3_47.jpg',\n",
|
||||
" 'images/3_48.jpg',\n",
|
||||
" 'images/3_49.jpg',\n",
|
||||
" 'images/3_5.jpg',\n",
|
||||
" 'images/3_50.jpg',\n",
|
||||
" 'images/3_51.jpg',\n",
|
||||
" 'images/3_52.jpg',\n",
|
||||
" 'images/3_53.jpg',\n",
|
||||
" 'images/3_55.jpg',\n",
|
||||
" 'images/3_56.jpg',\n",
|
||||
" 'images/3_57.jpg',\n",
|
||||
" 'images/3_58.jpg',\n",
|
||||
" 'images/3_59.jpg',\n",
|
||||
" 'images/3_65.jpg',\n",
|
||||
" 'images/3_66.jpg',\n",
|
||||
" 'images/3_7.jpg',\n",
|
||||
" 'images/3_71.jpg',\n",
|
||||
" 'images/3_72.jpg',\n",
|
||||
" 'images/3_73.jpg',\n",
|
||||
" 'images/3_74.jpg',\n",
|
||||
" 'images/3_75.jpg',\n",
|
||||
" 'images/3_76.jpg',\n",
|
||||
" 'images/3_77.jpg',\n",
|
||||
" 'images/3_78.jpg',\n",
|
||||
" 'images/3_8.jpg',\n",
|
||||
" 'images/3_9.jpg',\n",
|
||||
" 'images/4_10.jpg',\n",
|
||||
" 'images/4_11.jpg',\n",
|
||||
" 'images/4_13.jpg',\n",
|
||||
" 'images/4_14.jpg',\n",
|
||||
" 'images/4_21.jpg',\n",
|
||||
" 'images/4_6.jpg',\n",
|
||||
" 'images/4_7.jpg',\n",
|
||||
" 'images/4_9.jpg',\n",
|
||||
" 'images/5_1.jpg',\n",
|
||||
" 'images/5_2.jpg',\n",
|
||||
" 'images/5_3.jpg',\n",
|
||||
" 'images/5_4.jpg',\n",
|
||||
" 'images/5_5.jpg',\n",
|
||||
" 'images/5_6.jpg']"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"import cv2\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
|
||||
"plt.rcParams['axes.unicode_minus'] = False\n",
|
||||
"\n",
|
||||
"def getimgnames(path=None):\n",
|
||||
" \"\"\"\n",
|
||||
" 获取指定文件夹中的JPG图片名称（含路径）\n",
|
||||
" :param path: 指定文件夹\n",
|
||||
" :return: path中的所有JPG图片名称（含路径，例如：./path/image1.jpg）\n",
|
||||
" \"\"\"\n",
|
||||
" imgnames = []\n",
|
||||
" filenames = os.listdir(path) # 获取path中的所有文件名\n",
|
||||
" for i in filenames:\n",
|
||||
" if re.findall('\\.jpg$', i) != []: # 在所有文件名中找出JPG图片名称\n",
|
||||
" imgnames.append(os.path.join(path, i)) # 将图片名称和路径合并、保存\n",
|
||||
" return imgnames\n",
|
||||
"\n",
|
||||
"imglist = getimgnames('images/')\n",
|
||||
"imglist"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "356b558c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\ndef cut_image(img, imgFile):\\n cx = int(np.size(img, 1))/2\\n cy = int(np.size(img, 0))/2\\n \\n plt.figure(figsize=(8,8))\\n plt.imshow(img)\\n\\n plt.plot([cx-50, cx+50], [cy+50, cy+50], \\'r\\', linewidth=2)\\n plt.plot([cx+50, cx+50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n plt.plot([cx-50, cx+50], [cy-50, cy-50], \\'r\\', linewidth=2)\\n plt.plot([cx-50, cx-50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n plt.annotate(\\'选取的水样窗口\\', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\\n arrowprops=dict(facecolor=\\'black\\', shrink=0.1))\\n\\n plt.title(\\'水色样本 \\'+imgFile+\\' 分辨率为\\'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\\n plt.show()\\n \\nfor i in range(len(imglist)):\\n img = cv2.imread(imglist[i])\\n cut_image(img, imglist[i])\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
"def cut_image(img, imgFile):\n",
|
||||
" cx = int(np.size(img, 1))/2\n",
|
||||
" cy = int(np.size(img, 0))/2\n",
|
||||
" \n",
|
||||
" plt.figure(figsize=(8,8))\n",
|
||||
" plt.imshow(img)\n",
|
||||
"\n",
|
||||
" plt.plot([cx-50, cx+50], [cy+50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx+50, cx+50], [cy-50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx-50, cx+50], [cy-50, cy-50], 'r', linewidth=2)\n",
|
||||
" plt.plot([cx-50, cx-50], [cy-50, cy+50], 'r', linewidth=2)\n",
|
||||
" plt.annotate('选取的水样窗口', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\n",
|
||||
" arrowprops=dict(facecolor='black', shrink=0.1))\n",
|
||||
"\n",
|
||||
" plt.title('水色样本 '+imgFile+' 分辨率为'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
"for i in range(len(imglist)):\n",
|
||||
" img = cv2.imread(imglist[i])\n",
|
||||
" cut_image(img, imglist[i])\n",
|
||||
"\"\"\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "85e7fe5e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 加载图像统计信息模块（注：也可以直接通过颜色通道来计算）\n",
|
||||
"from PIL import ImageStat,Image\n",
|
||||
"\n",
|
||||
"# 遍历全体图像进行快速检查\n",
|
||||
"size = 100\n",
|
||||
"imgPath = './images'\n",
|
||||
"imgWidth = [] # 图像宽度\n",
|
||||
"imgHeight = [] # 图像高度\n",
|
||||
"imgRrange = [] # 图像红色通道极差\n",
|
||||
"imgGrange = [] # 图像绿色通道极差\n",
|
||||
"imgBrange = [] # 图像蓝色通道极差\n",
|
||||
"\n",
|
||||
"newImgs = [] # 获得选取后的图像作为模型训练和验证数据\n",
|
||||
"\n",
|
||||
"imgFiles = os.listdir(imgPath)\n",
|
||||
"for imgFile in imgFiles:\n",
|
||||
" img = Image.open(os.path.join(imgPath,imgFile))\n",
|
||||
" imgWidth.append(img.size[0])\n",
|
||||
" imgHeight.append(img.size[1])\n",
|
||||
" \n",
|
||||
" # 获得图像中心区域大小为size的图像块\n",
|
||||
" cx, cy = (int(i/2) for i in img.size)\n",
|
||||
" box = (cx-50, cy-50, cx+50, cy+50)\n",
|
||||
" region = img.crop(box)\n",
|
||||
" \n",
|
||||
" # 计算选取图像块的标准差 分为红绿蓝三种\n",
|
||||
" stat = ImageStat.Stat(region)\n",
|
||||
" imgRrange.append(stat.extrema[0][1]-stat.extrema[0][0])\n",
|
||||
" imgGrange.append(stat.extrema[1][1]-stat.extrema[1][0])\n",
|
||||
" imgBrange.append(stat.extrema[2][1]-stat.extrema[2][0])\n",
|
||||
" \n",
|
||||
" newImgs.append(region)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "b5ad4f91",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 161 entries, 0 to 160\n",
|
||||
"Data columns (total 9 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 0 161 non-null float64\n",
|
||||
" 1 1 161 non-null float64\n",
|
||||
" 2 2 161 non-null float64\n",
|
||||
" 3 3 161 non-null float64\n",
|
||||
" 4 4 161 non-null float64\n",
|
||||
" 5 5 161 non-null float64\n",
|
||||
" 6 6 161 non-null float64\n",
|
||||
" 7 7 161 non-null float64\n",
|
||||
" 8 8 161 non-null float64\n",
|
||||
"dtypes: float64(9)\n",
|
||||
"memory usage: 11.4 KB\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
" <th>3</th>\n",
|
||||
" <th>4</th>\n",
|
||||
" <th>5</th>\n",
|
||||
" <th>6</th>\n",
|
||||
" <th>7</th>\n",
|
||||
" <th>8</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>148.6041</td>\n",
|
||||
" <td>138.6381</td>\n",
|
||||
" <td>64.3694</td>\n",
|
||||
" <td>3.625902</td>\n",
|
||||
" <td>4.125497</td>\n",
|
||||
" <td>10.485931</td>\n",
|
||||
" <td>4.242425</td>\n",
|
||||
" <td>4.798916</td>\n",
|
||||
" <td>12.044228</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>163.6788</td>\n",
|
||||
" <td>145.5487</td>\n",
|
||||
" <td>54.4581</td>\n",
|
||||
" <td>3.923370</td>\n",
|
||||
" <td>2.835001</td>\n",
|
||||
" <td>3.478972</td>\n",
|
||||
" <td>4.439465</td>\n",
|
||||
" <td>3.239916</td>\n",
|
||||
" <td>4.039823</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>153.9485</td>\n",
|
||||
" <td>147.0810</td>\n",
|
||||
" <td>71.9576</td>\n",
|
||||
" <td>2.208766</td>\n",
|
||||
" <td>1.803397</td>\n",
|
||||
" <td>3.115478</td>\n",
|
||||
" <td>2.623796</td>\n",
|
||||
" <td>2.139289</td>\n",
|
||||
" <td>3.621357</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>150.3755</td>\n",
|
||||
" <td>151.3985</td>\n",
|
||||
" <td>64.3118</td>\n",
|
||||
" <td>2.015167</td>\n",
|
||||
" <td>1.514034</td>\n",
|
||||
" <td>2.698922</td>\n",
|
||||
" <td>2.344658</td>\n",
|
||||
" <td>1.809065</td>\n",
|
||||
" <td>3.136727</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>150.7380</td>\n",
|
||||
" <td>150.9738</td>\n",
|
||||
" <td>64.6246</td>\n",
|
||||
" <td>1.902934</td>\n",
|
||||
" <td>1.658045</td>\n",
|
||||
" <td>3.098044</td>\n",
|
||||
" <td>2.242270</td>\n",
|
||||
" <td>1.952067</td>\n",
|
||||
" <td>3.593836</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4 5 6 \\\n",
|
||||
"0 148.6041 138.6381 64.3694 3.625902 4.125497 10.485931 4.242425 \n",
|
||||
"1 163.6788 145.5487 54.4581 3.923370 2.835001 3.478972 4.439465 \n",
|
||||
"2 153.9485 147.0810 71.9576 2.208766 1.803397 3.115478 2.623796 \n",
|
||||
"3 150.3755 151.3985 64.3118 2.015167 1.514034 2.698922 2.344658 \n",
|
||||
"4 150.7380 150.9738 64.6246 1.902934 1.658045 3.098044 2.242270 \n",
|
||||
"\n",
|
||||
" 7 8 \n",
|
||||
"0 4.798916 12.044228 \n",
|
||||
"1 3.239916 4.039823 \n",
|
||||
"2 2.139289 3.621357 \n",
|
||||
"3 1.809065 3.136727 \n",
|
||||
"4 1.952067 3.593836 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"# 构建训练数据集和分类标签\n",
|
||||
"data = []\n",
|
||||
"dy = []\n",
|
||||
"for i, img in enumerate(newImgs):\n",
|
||||
" r, g, b = np.split(np.array(img), 3, axis = 2)\n",
|
||||
" \n",
|
||||
" #计算一阶矩\n",
|
||||
" r_m1 = np.mean(r)\n",
|
||||
" g_m1 = np.mean(g)\n",
|
||||
" b_m1 = np.mean(b)\n",
|
||||
" \n",
|
||||
" #二阶矩\n",
|
||||
" r_m2 = np.std(r)\n",
|
||||
" g_m2 = np.std(g)\n",
|
||||
" b_m2 = np.std(b)\n",
|
||||
" \n",
|
||||
" #三阶矩\n",
|
||||
" r_m3 = np.mean(abs(r - r.mean())**3)**(1/3)\n",
|
||||
" g_m3 = np.mean(abs(g - g.mean())**3)**(1/3)\n",
|
||||
" b_m3 = np.mean(abs(b - b.mean())**3)**(1/3)\n",
|
||||
" \n",
|
||||
" # 构造新数据集\n",
|
||||
" df = np.array([r_m1,g_m1,b_m1,r_m2,g_m2,b_m2,r_m3,g_m3,b_m3])\n",
|
||||
" data.append(df)\n",
|
||||
" \n",
|
||||
" # 保存对应的分类标签\n",
|
||||
" dy.append(int(imgFiles[i][0]))\n",
|
||||
"\n",
|
||||
"dy = np.array(dy)\n",
|
||||
"data = pd.DataFrame(np.array(data))\n",
|
||||
"data.info()\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "da56ab61",
|
||||
"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": [
|
||||
"((128, 9), (33, 9), (128,))"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train,x_test,y_train,y_test=train_test_split(data,dy,test_size=0.2,random_state=0)\n",
|
||||
"x_train.shape,x_test.shape,y_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "8c6be264",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.7575757575757576\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"D:\\anaconda\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.neural_network import MLPClassifier\n",
|
||||
"mlp=MLPClassifier()\n",
|
||||
"mlp.fit(x_train,y_train)\n",
|
||||
"y_pred=mlp.predict(x_test)\n",
|
||||
"print(accuracy_score(y_pred,y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d7c33fc8",
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user