├── C题题目 ├── C题.pdf └── 附件.xlsx ├── LICENSE ├── README.md ├── code ├── C1(1).ipynb ├── Quesion2.m ├── Quesion3.m ├── Question1.m ├── Qustion4.m ├── analysis.py ├── classify.m ├── drawp10.py ├── xishu.csv ├── xishu.py └── 总表_编码.xlsx ├── picture ├── lct1.png ├── lct2.png ├── lct3.png ├── lct4.png ├── sio2mn.png ├── sio2mx.png ├── xiputu.png ├── xishu.png ├── 决策树.eddx ├── 决策树.tif ├── 图片1.png ├── 图片2.tif ├── 图片3.tif ├── 斯皮尔曼系数.png ├── 类型与风化程度.png ├── 系谱图.eddx ├── 系谱图.png ├── 系谱图.tif ├── 纹饰与风化程度.png ├── 聚类结果图.eddx ├── 问题1流程图.tif ├── 问题4流程图.eddx ├── 问题4流程图.tif ├── 问题一流程图.eddx ├── 问题一流程图.png ├── 问题三流程图.eddx ├── 问题三流程图.tif ├── 问题二流程图.eddx ├── 问题二流程图.tif └── 颜色与风化程度.png ├── references ├── 不同风化程度的黄铁矿表面形貌及成分特征_李娟.pdf ├── 二级市场股票价格影响因素辨析——基于R的多元线性回归_谢淳钰.pdf ├── 因子分析法应用于人力资源配置的分析研究_贺丰年.pdf ├── 基于BP神经网络的葡萄酒质量评价_黄毅超.pdf ├── 基于CART决策树算法的服装可持续消费者画像构建_魏山森.pdf ├── 基于主成分分析和聚类分析的榴莲品质综合评价_陈妹姑.pdf ├── 广州出土汉代玻璃制品的无损分析_付强.pdf ├── 机车信号车载系统设备中载频...切换与机车信号灵敏度的分析_陈海康.pdf ├── 阀切换系统实现食品中维生素...、辅酶Q10的高灵敏度分析_周提军.pdf └── 黑云母的密度和主要成分在风...岛院格庄花岗岩体风化壳为例_党丽丽.pdf ├── result ├── SPNEARMAN.mat ├── 灰色关联度（高钾、铅钡）.xlsx ├── 第四问不同类别的化学成分之间的相关系数.xlsx ├── 铅钡未风化.xlsx ├── 铅钡风化.xlsx ├── 问题2训练集模拟结果.xlsx ├── 预测结果.mat ├── 风化点预测结果.xlsx ├── 高钾未风化.xlsx └── 高钾风化.xlsx ├── 最终论文.pdf └── 格式规范.doc /C题题目/C题.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shannany0606/2022_National_Math_Modeling_Competiotion/cc569475163d3a5d35865ec0bd400a6115787a12/C题题目/C题.pdf -------------------------------------------------------------------------------- /C题题目/附件.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shannany0606/2022_National_Math_Modeling_Competiotion/cc569475163d3a5d35865ec0bd400a6115787a12/C题题目/附件.xlsx -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Shannan Yan 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 2022_National_Math_Modeling_Competiotion 2 | 2022高教社杯全国大学生数学建模竞赛参赛项目（C题） 3 | 4 | 在2022年高教社杯全国大学生数学建模竞赛中，这个项目先是从强敌环伺的校内出线，被推荐到国赛，再被全国的专家教授们认可为全国二等奖水平。 5 | 6 | 数学建模的精妙之处，还是落在方法选择上。没拿到国一，可能就是因为没有对这道题给出的特殊数据“成分数据”使用对应的统计分析方法，如中心对数比变换等。失之毫厘，谬以千里啊。 7 | 8 | 这个项目里包含了表格结果、所用代码、参考文件、图片及其绘制文件、题目、论文规范以及最终版论文，希望后续的数模国赛追梦人能够在这个冲国一失败的项目上得到一些启发吧。 9 | 10 | 如果感觉这个项目对您有帮助，烦请您到右上角点个Star。点star的同学数模国赛都能冲到国一哈哈。 11 | -------------------------------------------------------------------------------- /code/C1(1).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 46, 6 | "id": "a8f4b8a3", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "image/png": 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", 12 | "text/plain": [ 13 | "
" 14 | ] 15 | }, 16 | "metadata": { 17 | "needs_background": "light" 18 | }, 19 | "output_type": "display_data" 20 | }, 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "
" 25 | ] 26 | }, 27 | "metadata": {}, 28 | "output_type": "display_data" 29 | } 30 | ], 31 | "source": [ 32 | "import matplotlib.pyplot as plt\n", 33 | "import numpy as np\n", 34 | "plt.rcParams['font.sans-serif'] = ['SimHei']\n", 35 | "plt.rcParams['axes.unicode_minus'] = False\n", 36 | "#条形图的绘制\n", 37 | "labels = ['浅绿', '浅蓝', '深绿', '深蓝', '紫','绿','蓝绿','黑']\n", 38 | "\n", 39 | "men_means = [2,8,3,2,2,1,6,0]\n", 40 | "women_means = [1,12,4,0,2,0,9,2]\n", 41 | "\n", 42 | "width = 0.35 # 条形图的宽度\n", 43 | "fig,ax = plt.subplots()\n", 44 | "ax.bar(labels, men_means,width,label='未风化')\n", 45 | "ax.bar(labels,women_means,width,bottom=men_means,label='风化')\n", 46 | "ax.set_ylabel('数目',size=16)\n", 47 | "ax.set_xlabel('颜色',size=16)\n", 48 | "ax.set_title('古代玻璃制品表面风化与其颜色的关系',size=16)\n", 49 | "plt.yticks(np.arange(0,22,2))\n", 50 | "\n", 51 | "#调整图片大小\n", 52 | "font = {'family':'simhei',\n", 53 | " 'weight': 'normal',\n", 54 | " 'size': 15,\n", 55 | " };\n", 56 | "ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小\n", 57 | "ax.tick_params(labelsize=16) #设置色标刻度字体大小\n", 58 | "plt.rcParams[\"figure.figsize\"] = (6, 5)\n", 59 | "\n", 60 | "plt.show()\n", 61 | "plt.savefig('C1(1).png',dpi = 1600); #保存图像，dpi可以调整图像的像素大小" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 1, 67 | "id": "55ccc241", 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "data": { 72 | "image/png": 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", 73 | "text/plain": [ 74 | "
" 75 | ] 76 | }, 77 | "metadata": { 78 | "needs_background": "light" 79 | }, 80 | "output_type": "display_data" 81 | } 82 | ], 83 | "source": [ 84 | "import seaborn as sns\n", 85 | "import matplotlib.pyplot as plt\n", 86 | "import pandas as pd\n", 87 | "pd.set_option('expand_frame_repr',False)\n", 88 | "df=pd.read_csv(r\"color.csv\")\n", 89 | "y_ticks = ['未风化','风化']\n", 90 | "x_ticks = ['铅钡', '高钾'] # 自定义横纵轴标签\n", 91 | "plt.rcParams['font.sans-serif'] = ['SimHei'] #可以正常显示中文\n", 92 | "ax = sns.heatmap(df,annot=True,fmt='.0f',cmap=\"Blues\",vmax=30, vmin=0,xticklabels=x_ticks, yticklabels=y_ticks,annot_kws={'size':16},cbar=False)\n", 93 | "# annot=True表示每一个格子显示数字;fmt='.0f'表示保留0位小数，同理fmt='.1f'表示保留一位小数\n", 94 | "# camp表示颜色，在另一个博主的文章中讲解的很清晰\n", 95 | "# vmax=350, vmin=20表示右侧颜色条的最大最小值，在最大最小值外的颜色将直接以最大或最小值的颜色显示，\n", 96 | "# 通过此设置就可以解决少数值过大从而使得大部分色块区别不明显的问题\n", 97 | "# xticklabels=x_ticks, yticklabels=y_ticks，横纵轴标签\n", 98 | "#ax.set_title('分速度报警类型分布') # 图标题\n", 99 | "ax.set_xlabel('类型',size=16) # x轴标题\n", 100 | "ax.set_ylabel('风化程度',size=16) # y轴标题\n", 101 | "\n", 102 | "#调整图片字体大小\n", 103 | "font = {'family':'simhei',\n", 104 | " 'weight': 'normal',\n", 105 | " 'size': 16,\n", 106 | " };\n", 107 | "#ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小\n", 108 | "ax.tick_params(labelsize=16) #设置色标刻度字体大小\n", 109 | "cb=ax.figure.colorbar(ax.collections[0]) #显示colorbar\n", 110 | "cb.ax.tick_params(labelsize=16) #设置colorbar刻度字体大小。\n", 111 | "plt.rcParams[\"figure.figsize\"] = (12, 10)\n", 112 | "\n", 113 | "plt.show()\n", 114 | "figure = ax.get_figure()\n", 115 | "\n", 116 | "figure.savefig('类型与风化程度.png',dpi=18*15) # 保存图片" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 5, 122 | "id": "b86237d2", 123 | "metadata": { 124 | "scrolled": false 125 | }, 126 | "outputs": [ 127 | { 128 | "data": { 129 | "image/png": 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", 130 | "text/plain": [ 131 | "
" 132 | ] 133 | }, 134 | "metadata": { 135 | "needs_background": "light" 136 | }, 137 | "output_type": "display_data" 138 | } 139 | ], 140 | "source": [ 141 | "import seaborn as sns\n", 142 | "import matplotlib.pyplot as plt\n", 143 | "import pandas as pd\n", 144 | "pd.set_option('expand_frame_repr',False)\n", 145 | "df=pd.read_csv(r\"pat.csv\")\n", 146 | "y_ticks = ['未风化','风化']\n", 147 | "x_ticks = ['A', 'B','C'] # 自定义横纵轴标签\n", 148 | "plt.rcParams['font.sans-serif'] = ['SimHei'] #可以正常显示中文\n", 149 | "ax = sns.heatmap(df,annot=True,fmt='.0f',cmap=\"Blues\",vmax=18, vmin=0,xticklabels=x_ticks, yticklabels=y_ticks,annot_kws={'size':16},cbar=False)\n", 150 | "# annot=True表示每一个格子显示数字;fmt='.0f'表示保留0位小数，同理fmt='.1f'表示保留一位小数\n", 151 | "# camp表示颜色，在另一个博主的文章中讲解的很清晰\n", 152 | "# vmax=350, vmin=20表示右侧颜色条的最大最小值，在最大最小值外的颜色将直接以最大或最小值的颜色显示，\n", 153 | "# 通过此设置就可以解决少数值过大从而使得大部分色块区别不明显的问题\n", 154 | "# xticklabels=x_ticks, yticklabels=y_ticks，横纵轴标签\n", 155 | "#ax.set_title('分速度报警类型分布') # 图标题\n", 156 | "ax.set_xlabel('纹饰',size=16) # x轴标题\n", 157 | "ax.set_ylabel('风化程度',size=16) # y轴标题\n", 158 | "\n", 159 | "#调整图片字体大小\n", 160 | "font = {'family':'simhei',\n", 161 | " 'weight': 'normal',\n", 162 | " 'size': 16,\n", 163 | " };\n", 164 | "#ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小\n", 165 | "ax.tick_params(labelsize=16) #设置色标刻度字体大小\n", 166 | "cb=ax.figure.colorbar(ax.collections[0]) #显示colorbar\n", 167 | "cb.ax.tick_params(labelsize=16) #设置colorbar刻度字体大小。\n", 168 | "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", 169 | "\n", 170 | "plt.show()\n", 171 | "figure = ax.get_figure()\n", 172 | "\n", 173 | "figure.savefig('纹饰与风化程度.png',dpi=24*12) # 保存图片" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 4, 179 | "id": "b238c57d", 180 | "metadata": {}, 181 | "outputs": [ 182 | { 183 | "data": { 184 | "image/png": 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", 185 | "text/plain": [ 186 | "
" 187 | ] 188 | }, 189 | "metadata": { 190 | "needs_background": "light" 191 | }, 192 | "output_type": "display_data" 193 | } 194 | ], 195 | "source": [ 196 | "import seaborn as sns\n", 197 | "import matplotlib.pyplot as plt\n", 198 | "import pandas as pd\n", 199 | "pd.set_option('expand_frame_repr',False)\n", 200 | "df=pd.read_csv(r\"color1.csv\")\n", 201 | "y_ticks = ['未风化','风化']\n", 202 | "x_ticks = ['浅绿', '浅蓝','深绿','深蓝','紫','绿','蓝绿','黑'] # 自定义横纵轴标签\n", 203 | "plt.rcParams['font.sans-serif'] = ['SimHei'] #可以正常显示中文\n", 204 | "ax = sns.heatmap(df,annot=True,fmt='.0f',cmap=\"Blues\",vmax=15, vmin=0,xticklabels=x_ticks, yticklabels=y_ticks,annot_kws={'size':16},cbar=False)\n", 205 | "# annot=True表示每一个格子显示数字;fmt='.0f'表示保留0位小数，同理fmt='.1f'表示保留一位小数\n", 206 | "# camp表示颜色，在另一个博主的文章中讲解的很清晰\n", 207 | "# vmax=350, vmin=20表示右侧颜色条的最大最小值，在最大最小值外的颜色将直接以最大或最小值的颜色显示，\n", 208 | "# 通过此设置就可以解决少数值过大从而使得大部分色块区别不明显的问题\n", 209 | "# xticklabels=x_ticks, yticklabels=y_ticks，横纵轴标签\n", 210 | "#ax.set_title('分速度报警类型分布') # 图标题\n", 211 | "ax.set_xlabel('纹饰',size=16) # x轴标题\n", 212 | "ax.set_ylabel('风化程度',size=16) # y轴标题\n", 213 | "\n", 214 | "#调整图片字体大小\n", 215 | "font = {'family':'simhei',\n", 216 | " 'weight': 'normal',\n", 217 | " 'size': 16,\n", 218 | " };\n", 219 | "#ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小\n", 220 | "ax.tick_params(labelsize=16) #设置色标刻度字体大小\n", 221 | "cb=ax.figure.colorbar(ax.collections[0]) #显示colorbar\n", 222 | "cb.ax.tick_params(labelsize=16) #设置colorbar刻度字体大小。\n", 223 | "plt.rcParams[\"figure.figsize\"] = (16, 8)\n", 224 | "\n", 225 | "plt.show()\n", 226 | "figure = ax.get_figure()\n", 227 | "\n", 228 | "figure.savefig('颜色与风化程度.png',dpi=36*6) # 保存图片" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 1, 234 | "id": "fc3d346a", 235 | "metadata": {}, 236 | "outputs": [ 237 | { 238 | "data": { 239 | "image/png": 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", 240 | "text/plain": [ 241 | "
" 242 | ] 243 | }, 244 | "metadata": { 245 | "needs_background": "light" 246 | }, 247 | "output_type": "display_data" 248 | } 249 | ], 250 | "source": [ 251 | "import matplotlib\n", 252 | "import matplotlib.pyplot as plt\n", 253 | "import numpy as np\n", 254 | "\n", 255 | "def drawHistogram():\n", 256 | " matplotlib.rc(\"font\", family='MicroSoft YaHei')\n", 257 | " list1 = np.array([5, 2, 1, 0, 8, 0, 6]) # 柱状图第一组数据\n", 258 | " list2 = np.array([9, 5, 1, 2, 9, 2, 0]) # 柱状图第二组数据\n", 259 | " length = len(list1)\n", 260 | " x = np.arange(length) # 横坐标范围\n", 261 | " listDate = [\"星期一\", \"星期二\", \"星期三\", \"星期四\", \"星期五\", \"星期六\", \"星期天\"]\n", 262 | "\n", 263 | " plt.figure()\n", 264 | " total_width, n = 0.8, 2 # 柱状图总宽度，有几组数据\n", 265 | " width = total_width / n # 单个柱状图的宽度\n", 266 | " x1 = x - width / 2 # 第一组数据柱状图横坐标起始位置\n", 267 | " x2 = x1 + width # 第二组数据柱状图横坐标起始位置\n", 268 | "\n", 269 | " plt.title(\"一周每天吃悠哈软糖颗数柱状图\") # 柱状图标题\n", 270 | " # plt.xlabel(\"星期\") # 横坐标label 此处可以不添加\n", 271 | " plt.ylabel(\"吃悠哈软糖颗数(个)\") # 纵坐标label\n", 272 | " plt.bar(x1, list1, width=width, label=\"小s吃糖数\")\n", 273 | " plt.bar(x2, list2, width=width, label=\"小y吃糖数\")\n", 274 | " plt.xticks(x, listDate) # 用星期几替换横坐标x的值\n", 275 | " plt.legend() # 给出图例\n", 276 | " plt.show()\n", 277 | "\n", 278 | "if __name__ == '__main__':\n", 279 | " drawHistogram()" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 3, 285 | "id": "e65f504a", 286 | "metadata": {}, 287 | "outputs": [ 288 | { 289 | "data": { 290 | "image/png": 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", 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" 293 | ] 294 | }, 295 | "metadata": { 296 | "needs_background": "light" 297 | }, 298 | "output_type": "display_data" 299 | } 300 | ], 301 | "source": [ 302 | "import seaborn as sns\n", 303 | "import matplotlib.pyplot as plt\n", 304 | "import pandas as pd\n", 305 | "pd.set_option('expand_frame_repr',False)\n", 306 | "df=pd.read_csv(r\"xishu.csv\")\n", 307 | "y_ticks = ['表面风化','颜色','类型','纹饰']\n", 308 | "x_ticks = ['纹饰','类型','颜色','表面风化'] # 自定义横纵轴标签\n", 309 | "plt.rcParams['font.sans-serif'] = ['SimHei'] #可以正常显示中文\n", 310 | "plt.rcParams['axes.unicode_minus'] = False #正常显示负号\n", 311 | "ax = sns.heatmap(df,annot=True,fmt='.4f',cmap=\"Reds\",vmax=1, vmin=-1,xticklabels=x_ticks, yticklabels=y_ticks,annot_kws={'size':16},cbar=False)\n", 312 | "# annot=True表示每一个格子显示数字;fmt='.0f'表示保留0位小数，同理fmt='.1f'表示保留一位小数\n", 313 | "# camp表示颜色，在另一个博主的文章中讲解的很清晰\n", 314 | "# vmax=350, vmin=20表示右侧颜色条的最大最小值，在最大最小值外的颜色将直接以最大或最小值的颜色显示，\n", 315 | "# 通过此设置就可以解决少数值过大从而使得大部分色块区别不明显的问题\n", 316 | "# xticklabels=x_ticks, yticklabels=y_ticks，横纵轴标签\n", 317 | "#ax.set_title('分速度报警类型分布') # 图标题\n", 318 | "ax.set_xlabel('纹饰',size=16) # x轴标题\n", 319 | "ax.set_ylabel('风化程度',size=16) # y轴标题\n", 320 | "\n", 321 | "#调整图片字体大小\n", 322 | "font = {'family':'simhei',\n", 323 | " 'weight': 'normal',\n", 324 | " 'size': 16,\n", 325 | " };\n", 326 | "#ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小\n", 327 | "ax.tick_params(labelsize=16) #设置色标刻度字体大小\n", 328 | "cb=ax.figure.colorbar(ax.collections[0]) #显示colorbar\n", 329 | "cb.ax.tick_params(labelsize=16) #设置colorbar刻度字体大小。\n", 330 | "plt.rcParams[\"figure.figsize\"] = (12, 6)\n", 331 | "\n", 332 | "plt.show()\n", 333 | "figure = ax.get_figure()\n", 334 | "\n", 335 | "figure.savefig('xishu.png',dpi=24*12) # 保存图片" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 43, 341 | "id": "619c911a", 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "image/png": 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", 347 | "text/plain": [ 348 | "
" 349 | ] 350 | }, 351 | "metadata": { 352 | "needs_background": "light" 353 | }, 354 | "output_type": "display_data" 355 | } 356 | ], 357 | "source": [ 358 | "# encoding=utf-8\n", 359 | "import matplotlib.pyplot as plt\n", 360 | "from pylab import * #支持中文\n", 361 | "mpl.rcParams['font.sans-serif'] = ['SimHei']\n", 362 | "\n", 363 | "def remove_edge(plt):\n", 364 | " fig, ax = plt.subplots()\n", 365 | " ax.spines[\"top\"].set_visible(False)\n", 366 | " #ax.spines[\"bottom\"].set_visible(False)\n", 367 | " ax.spines[\"right\"].set_visible(False)\n", 368 | " #ax.spines[\"left\"].set_visible(False)\n", 369 | " # 改变长宽比\n", 370 | "remove_edge(plt);\n", 371 | "names = ['-30%', '-25%', '-20%', '-15%', '-10%','-5%','+5%','+10%','+15%','+20%','+25%','+30%']\n", 372 | "x = range(len(names))\n", 373 | "y = [92.5, 92.5, 100, 100, 100,100,100,100,100,100,100,100]\n", 374 | "#y1= [100, 100, 100, 100, 100,100,100,100,100,100,92.5,92.5]\n", 375 | "#plt.plot(x, y, 'ro-')\n", 376 | "#plt.plot(x, y1, 'bo-')\n", 377 | "plt.xlim(-1, 12) # 限定横轴的范围\n", 378 | "plt.ylim(75, 101) # 限定纵轴的范围\n", 379 | "plt.plot(x, y, marker='o', mec='r', mfc='w',label=u'二氧化硅最大值')\n", 380 | "#plt.plot(x, y1, marker='*', ms=10,label=u'二氧化硅最小值')\n", 381 | "#plt.legend() # 让图例生效\n", 382 | "plt.xticks(x, names, rotation=45)\n", 383 | "plt.margins(0)\n", 384 | "plt.subplots_adjust(bottom=0.15)\n", 385 | "plt.xlabel(u\"扰动幅度\",size=24) #X轴标签\n", 386 | "plt.ylabel(\"准确率（%）\",size=24) #Y轴标签\n", 387 | "#plt.title(\"A simple plot\") #标题\n", 388 | "plt.rcParams[\"figure.figsize\"] = (10, 6)\n", 389 | "plt.tick_params(labelsize=24)\n", 390 | "plt.savefig('sio2mx.png',dpi=20*12) # 保存图片\n", 391 | "#plt.savefig('sio2mn.png',dpi=20*12) # 保存图片\n", 392 | "plt.show()\n", 393 | "\n" 394 | ] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": 41, 399 | "id": "593bb4d7", 400 | "metadata": {}, 401 | "outputs": [ 402 | { 403 | "data": { 404 | "image/png": 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", 405 | "text/plain": [ 406 | "
" 407 | ] 408 | }, 409 | "metadata": { 410 | "needs_background": "light" 411 | }, 412 | "output_type": "display_data" 413 | }, 414 | { 415 | "ename": "AttributeError", 416 | "evalue": "'function' object has no attribute 'savefig'", 417 | "output_type": "error", 418 | "traceback": [ 419 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 420 | "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", 421 | "\u001b[1;32mc:\\Users\\ysn\\Desktop\\2022数模国赛国一\\C1(1).ipynb Cell 8\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[39m#plt.title(\"A simple plot\") #标题\u001b[39;00m\n\u001b[0;32m 33\u001b[0m plt\u001b[39m.\u001b[39mshow()\n\u001b[1;32m---> 34\u001b[0m figure\u001b[39m.\u001b[39;49msavefig(\u001b[39m'\u001b[39m\u001b[39msio2mn.png\u001b[39m\u001b[39m'\u001b[39m)\n", 422 | "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'savefig'" 423 | ] 424 | } 425 | ], 426 | "source": [ 427 | "# encoding=utf-8\n", 428 | "import matplotlib.pyplot as plt\n", 429 | "from pylab import * #支持中文\n", 430 | "mpl.rcParams['font.sans-serif'] = ['SimHei']\n", 431 | "\n", 432 | "def remove_edge(plt):\n", 433 | " fig, ax = plt.subplots()\n", 434 | " ax.spines[\"top\"].set_visible(False)\n", 435 | " #ax.spines[\"bottom\"].set_visible(False)\n", 436 | " ax.spines[\"right\"].set_visible(False)\n", 437 | " #ax.spines[\"left\"].set_visible(False)\n", 438 | " # 改变长宽比\n", 439 | "remove_edge(plt);\n", 440 | "names = ['-30%', '-25%', '-20%', '-15%', '-10%','-5%','+5%','+10%','+15%','+20%','+25%','+30%']\n", 441 | "x = range(len(names))\n", 442 | "#y = [92.5, 92.5, 100, 100, 100,100,100,100,100,100,100,100]\n", 443 | "y1= [100, 100, 100, 100, 100,100,100,100,100,100,92.5,92.5]\n", 444 | "#plt.plot(x, y, 'ro-')\n", 445 | "#plt.plot(x, y1, 'bo-')\n", 446 | "plt.xlim(-1, 12) # 限定横轴的范围\n", 447 | "plt.ylim(75, 101) # 限定纵轴的范围\n", 448 | "#plt.plot(x, y, marker='o', mec='r', mfc='w',label=u'二氧化硅最大值')\n", 449 | "plt.plot(x, y1, marker='*', ms=10,label=u'二氧化硅最小值')\n", 450 | "#plt.legend() # 让图例生效\n", 451 | "plt.xticks(x, names, rotation=45)\n", 452 | "plt.margins(0)\n", 453 | "plt.subplots_adjust(bottom=0.15)\n", 454 | "plt.xlabel(u\"扰动幅度\") #X轴标签\n", 455 | "plt.ylabel(\"准确率（%）\") #Y轴标签\n", 456 | "#plt.title(\"A simple plot\") #标题\n", 457 | "\n", 458 | "\n", 459 | "plt.show()\n", 460 | "figure.savefig('sio2mn.png') # 保存图片" 461 | ] 462 | } 463 | ], 464 | "metadata": { 465 | "kernelspec": { 466 | "display_name": "Python 3.10.5 64-bit", 467 | "language": "python", 468 | "name": "python3" 469 | }, 470 | "language_info": { 471 | "codemirror_mode": { 472 | "name": "ipython", 473 | "version": 3 474 | }, 475 | "file_extension": ".py", 476 | "mimetype": "text/x-python", 477 | "name": "python", 478 | "nbconvert_exporter": "python", 479 | "pygments_lexer": "ipython3", 480 | "version": "3.10.5" 481 | }, 482 | "vscode": { 483 | "interpreter": { 484 | "hash": "c384c7fb7f70f778458b7c9c1e517077db5f8e2f529c88a113a5c9f95fcd9f85" 485 | } 486 | } 487 | }, 488 | "nbformat": 4, 489 | "nbformat_minor": 5 490 | } 491 | -------------------------------------------------------------------------------- /code/Quesion2.m: -------------------------------------------------------------------------------- 1 | %决策树 2 | Total=xlsread('总表_编码.xlsx'); 3 | t=fitctree(Total(1:55,[5 7:20]),Total(1:55,3)); 4 | view(t,'Mode','graph') 5 | t=fitctree(Total(1:55,[5 7:14 16:20]),Total(1:55,3)); 6 | view(t,'Mode','graph') 7 | t=fitctree(Total(1:55,[5 7:14 17:20]),Total(1:55,3)); 8 | view(t,'Mode','graph') 9 | t=fitctree(Total(1:55,[5 7:14 17 19 20]),Total(1:55,3)); 10 | view(t,'Mode','graph') 11 | t=fitctree(Total(1:55,[5 7:8 10:14 17 19 20]),Total(1:55,3)); 12 | view(t,'Mode','graph') 13 | 14 | %K均值聚类 15 | NOT_FH_K=xlsread('高钾未风化.xlsx'); 16 | NOT_FH_K(isnan(NOT_FH_K))=0; 17 | FH_K=xlsread('高钾风化.xlsx'); 18 | FH_K(isnan(FH_K))=0; 19 | NOT_FH_Pb=xlsread('铅钡未风化.xlsx'); 20 | NOT_FH_Pb(isnan(NOT_FH_Pb))=0; 21 | FH_Pb=xlsread('铅钡风化.xlsx'); 22 | FH_Pb(isnan(FH_Pb))=0; 23 | NOT_FH_K(12,:)=[];NOT_FH_K(10,:)=[]; 24 | %一级亚类划分 25 | K=[FH_K;NOT_FH_K]; 26 | Pb=[FH_Pb;NOT_FH_Pb]; 27 | C_Pb=Pb(:,[1 3 4 9 10 11 12]); 28 | [IDX,C]=kmeans(C_Pb,2); 29 | C_K=K(:,[1 3 4 9 10 11 12]); 30 | [IDX,C]=kmeans(C_K,2); 31 | %二级亚类划分 32 | Classify_Pb=NOT_FH_Pb(:,[1 3 4 9 10 11 12]); 33 | [IDX,C]=kmeans(Classify_Pb,2); 34 | [IDX,C]=kmeans(Classify_Pb,3); 35 | [IDX,C]=kmeans(Classify_Pb,4); 36 | [IDX,C]=kmeans(Classify_Pb,5); 37 | 38 | Classify_K=NOT_FH_K(:,[1 3 4 9 10 11 12]); 39 | [IDX,C]=kmeans(Classify_K,2); 40 | [IDX,C]=kmeans(Classify_K,3); 41 | [IDX,C]=kmeans(Classify_K,4); 42 | [IDX,C]=kmeans(Classify_K,5); 43 | %敏感性 44 | y=[92.85 92.85 100*ones(1,10)]; 45 | plot(1:12,y,'-o') 46 | ylim([85 100]) 47 | xlim(1:12) 48 | ax = gca; 49 | ax.XTickLabel = {'','-30%','-25%','-20%','-15%','-10%','-5%','5%','10%','15%','20%','25%','30%'} 50 | ylabel('准确看(%)') 51 | title('二氧化硅最大值变化与准确率的关系') 52 | 53 | y=[100*ones(1,10) 92.85 92.85]; 54 | bar(1:12,y) 55 | ylim([85 100]) 56 | ax = gca; 57 | ax.XTickLabel = {'-30%','-25%','-20%','-15%','-10%','-5%','5%','10%','15%','20%','25%','30%'} 58 | ylabel('准确看(%)') 59 | title('二氧化硅最小值变化与准确率的关系') 60 | 61 | y=100*ones(1,12); 62 | bar(1:12,y) 63 | ylim([85 100]) 64 | ax = gca; 65 | ax.XTickLabel = {'-30%','-25%','-20%','-15%','-10%','-5%','5%','10%','15%','20%','25%','30%'} 66 | ylabel('准确看(%)') 67 | title('氧化钾最小值变化与准确率的关系') -------------------------------------------------------------------------------- /code/Quesion3.m: -------------------------------------------------------------------------------- 1 | Percent=xlsread('附件.xlsx','表单3'); 2 | [m,~]=size(Percent); 3 | Predict_Q3=zeros(m,1); 4 | for i=1:m 5 | Predict_Q3(i)=classify(Percent(i,:)); 6 | end 7 | Percent_ep=[]; 8 | for i=1:9 9 | Percent_ep=Percent; 10 | for j=1:8 11 | Percent_ep(j,11)=Percent_ep(j,11)*(1+(i-5)*0.05); 12 | C(j,i)=classify(Percent_ep(j,:)); 13 | end 14 | end -------------------------------------------------------------------------------- /code/Question1.m: -------------------------------------------------------------------------------- 1 | NOT_FH_K=xlsread('高钾未风化.xlsx'); 2 | NOT_FH_K(isnan(NOT_FH_K))=0; 3 | FH_K=xlsread('高钾风化.xlsx'); 4 | FH_K(isnan(FH_K))=0; 5 | NOT_FH_Pb=xlsread('铅钡未风化.xlsx'); 6 | NOT_FH_Pb(isnan(NOT_FH_Pb))=0; 7 | FH_Pb=xlsread('铅钡风化.xlsx'); 8 | FH_Pb(isnan(FH_Pb))=0; 9 | NOT_FH_K(12,:)=[];NOT_FH_K(10,:)=[]; 10 | 11 | for j=1:14 12 | Percent_compare(1,j)=sum(NOT_FH_K(:,j))./12; 13 | end 14 | for j=1:14 15 | Percent_compare(2,j)=sum(FH_K(:,j))./6; 16 | end 17 | for j=1:14 18 | Percent_compare(3,j)=sum(NOT_FH_Pb(:,j))./23; 19 | end 20 | for j=1:14 21 | Percent_compare(4,j)=sum(FH_Pb(:,j))./26; 22 | end 23 | 24 | Percent_K=zeros(1,14); 25 | Percent_Pb=zeros(1,14); 26 | for i=1:14 27 | Percent_K(i)=(Percent_compare(2,i)-Percent_compare(1,i))./Percent_compare(1,i); 28 | Percent_Pb(i)=(Percent_compare(4,i)-Percent_compare(3,i))./Percent_compare(3,i); 29 | end 30 | feature_K=[1 3 4 6 8 11]; 31 | feature_Pb=[1 4 8 9 10 11]; 32 | Predict_K=[];Predict_Pb=[]; 33 | for i=1:6 34 | Predict_K=[Predict_K;Percent_compare(1,:)]; 35 | end 36 | for i=1:26 37 | Predict_Pb=[Predict_Pb;Percent_compare(3,:)]; 38 | end 39 | for i=1:6 40 | for j=1:6 41 | Predict_K(i,feature_K(j))=FH_K(i,feature_K(j))./(1+Percent_K(feature_K(j))); 42 | end 43 | end 44 | for i=1:26 45 | for j=1:6 46 | Predict_Pb(i,feature_Pb(j))=FH_Pb(i,feature_Pb(j))./(1+Percent_Pb(feature_Pb(j))); 47 | end 48 | end 49 | 50 | P1=corr([NOT_FH_K ones(12,1);FH_K 2.*ones(6,1)],'type','Spearman'); 51 | P2=corr([NOT_FH_Pb ones(23,1);FH_Pb 2.*ones(26,1)],'type','Spearman'); 52 | [~,id1]=sort(abs(P1(1:14,15))); 53 | [~,id2]=sort(abs(P2(1:14,15))); 54 | 55 | 56 | figure 57 | title('高钾、铅钡风化前后各种化学成分含量的对比') 58 | i=1; 59 | subplot(4,7,i) 60 | b=bar([Percent_compare(1,i) Percent_compare(2,i)]); 61 | b.FaceColor = 'flat'; 62 | b.CData(1,:) = [1,0.38,0.27]; 63 | b.CData(2,:) = [0,0.75,1]; 64 | ylim([0 100]) 65 | title(Name(i)) 66 | ylabel('高钾') 67 | 68 | for i=2:14 69 | subplot(4,7,i) 70 | b=bar([Percent_compare(1,i) Percent_compare(2,i)]); 71 | b.FaceColor = 'flat'; 72 | b.CData(1,:) = [1,0.38,0.27]; 73 | b.CData(2,:) = [0,0.75,1]; 74 | ylim([0 2*max(Percent_compare(1,i),Percent_compare(2,i))]) 75 | title(Name(i)) 76 | end 77 | i=15; 78 | subplot(4,7,i) 79 | b=bar([Percent_compare(3,i-14) Percent_compare(4,i-14)]); 80 | b.FaceColor = 'flat'; 81 | b.CData(1,:) = [1,0.38,0.27]; 82 | b.CData(2,:) = [0,0.75,1]; 83 | ylim([0 100]) 84 | title(Name(i-14)) 85 | ylabel('铅钡') 86 | for i=16:28 87 | subplot(4,7,i) 88 | b=bar([Percent_compare(3,i-14) Percent_compare(4,i-14)]); 89 | b.FaceColor = 'flat'; 90 | b.CData(1,:) = [1,0.38,0.27]; 91 | b.CData(2,:) = [0,0.75,1]; 92 | ylim([0 2*max(Percent_compare(3,i-14),Percent_compare(4,i-14))]) 93 | title(Name(i-14)) 94 | end 95 | 96 | -------------------------------------------------------------------------------- /code/Qustion4.m: -------------------------------------------------------------------------------- 1 | subplot(1,2,1) 2 | P_FH_K=corr(FH_K,'type','Spearman'); 3 | P_FH_K(isnan(P_FH_K))=0; 4 | xlabel_name = Name; 5 | ylabel_name = Name; 6 | hot_figure = heatmap(xlabel_name,ylabel_name,P_FH_K,'FontSize',10); 7 | hot_figure.GridVisible = 'off'; 8 | hot_figure.Title = '高钾风化'; 9 | 10 | P_FH_Pb=corr(FH_Pb,'type','Spearman'); 11 | xlabel_name = Name; 12 | ylabel_name = Name; 13 | hot_figure = heatmap(xlabel_name,ylabel_name,P_FH_Pb,'FontSize',10); 14 | hot_figure.GridVisible = 'off'; 15 | hot_figure.Title = '铅钡风化'; 16 | subplot(1,2,2) 17 | P_NOT_FH_K=corr(NOT_FH_K,'type','Spearman'); 18 | xlabel_name = Name; 19 | ylabel_name = Name; 20 | hot_figure = heatmap(xlabel_name,ylabel_name,P_NOT_FH_K,'FontSize',10); 21 | hot_figure.GridVisible = 'off'; 22 | hot_figure.Title = '高钾未风化'; 23 | 24 | P_NOT_FH_Pb=corr(NOT_FH_Pb,'type','Spearman'); 25 | xlabel_name = Name; 26 | ylabel_name = Name; 27 | hot_figure = heatmap(xlabel_name,ylabel_name,P_NOT_FH_Pb,'FontSize',10); 28 | hot_figure.GridVisible = 'off'; 29 | hot_figure.Title = '铅钡未风化'; 30 | 31 | P_NOT_FH=abs(abs(P_NOT_FH_K)-abs(P_NOT_FH_Pb)); 32 | xlabel_name = Name; 33 | ylabel_name = Name; 34 | hot_figure = heatmap(xlabel_name,ylabel_name,P_NOT_FH,'FontSize',10); 35 | hot_figure.GridVisible = 'off'; 36 | hot_figure.Title = '未风化'; 37 | 38 | P_FH=abs(abs(P_FH_K)-abs(P_FH_Pb)); 39 | xlabel_name = Name; 40 | ylabel_name = Name; 41 | hot_figure = heatmap(xlabel_name,ylabel_name,P_FH,'FontSize',10); 42 | hot_figure.GridVisible = 'off'; 43 | hot_figure.Title = '风化'; 44 | 45 | P_K=abs(abs(P_FH_K)-abs(P_NOT_FH_K)); 46 | xlabel_name = Name; 47 | ylabel_name = Name; 48 | hot_figure = heatmap(xlabel_name,ylabel_name,P_K,'FontSize',10); 49 | hot_figure.GridVisible = 'off'; 50 | hot_figure.Title = '高钾'; 51 | 52 | P_Pb=abs(abs(P_FH_Pb)-abs(P_NOT_FH_Pb)); 53 | xlabel_name = Name; 54 | ylabel_name = Name; 55 | hot_figure = heatmap(xlabel_name,ylabel_name,P_Pb,'FontSize',10); 56 | hot_figure.GridVisible = 'off'; 57 | hot_figure.Title = '铅钡'; -------------------------------------------------------------------------------- /code/analysis.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | plt.rcParams['font.sans-serif'] = ['SimHei'] 4 | plt.rcParams['axes.unicode_minus'] = False 5 | #条形图的绘制 6 | labels = ['浅绿', '浅蓝', '深绿', '深蓝', '紫','绿','蓝绿','黑'] 7 | 8 | men_means = [2,8,3,2,2,1,6,0] 9 | women_means = [1,12,4,0,2,0,9,2] 10 | 11 | width = 0.35 # 条形图的宽度 12 | fig,ax = plt.subplots() 13 | ax.bar(labels, men_means,width,label='未风化') 14 | ax.bar(labels,women_means,width,bottom=men_means,label='风化') 15 | ax.set_ylabel('数目',size=16) 16 | ax.set_xlabel('颜色',size=16) 17 | ax.set_title('古代玻璃制品表面风化与其颜色的关系',size=16) 18 | plt.yticks(np.arange(0,22,2)) 19 | 20 | #调整图片大小 21 | font = {'family':'simhei', 22 | 'weight': 'normal', 23 | 'size': 15, 24 | }; 25 | ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小 26 | ax.tick_params(labelsize=16) #设置色标刻度字体大小 27 | plt.rcParams["figure.figsize"] = (6, 5) 28 | 29 | plt.show() 30 | plt.savefig('C1(1).png',dpi = 1600); #保存图像，dpi可以调整图像的像素大小 31 | -------------------------------------------------------------------------------- /code/classify.m: -------------------------------------------------------------------------------- 1 | function C=classify(Percent) 2 | if ~isnan(Percent(9)) 3 | if Percent(9)>=5.46 4 | C=2; 5 | return 6 | end 7 | if Percent(9)<5.46 8 | C=1; 9 | return 10 | end 11 | end 12 | if ~isnan(Percent(10)) 13 | if Percent(10)>=3.06 14 | C=2; 15 | return 16 | end 17 | if Percent(10)<3.06 18 | C=1; 19 | return 20 | end 21 | end 22 | if ~isnan(Percent(12)) 23 | if Percent(12)>=0.155 24 | C=2; 25 | return 26 | end 27 | if Percent(12)<0.155 28 | C=1; 29 | return 30 | end 31 | end 32 | if Percent(1)<57.11 33 | C=1; 34 | return 35 | end 36 | if Percent(1)>=57.11 37 | if ~isnan(Percent(3)) 38 | if Percent(3)>=0.445 39 | C=1; 40 | return 41 | end 42 | if Percent(3)<0.445 43 | C=2; 44 | return 45 | end 46 | end 47 | if ~isnan(Percent(8)) 48 | if Percent(8)>=0.76 49 | C=1; 50 | return 51 | end 52 | if Percent(8)<0.76 53 | C=2; 54 | return 55 | end 56 | end 57 | end 58 | end -------------------------------------------------------------------------------- /code/drawp10.py: -------------------------------------------------------------------------------- 1 | # encoding=utf-8 2 | import matplotlib.pyplot as plt 3 | from pylab import * #支持中文 4 | mpl.rcParams['font.sans-serif'] = ['SimHei'] 5 | 6 | def remove_edge(plt): 7 | fig, ax = plt.subplots() 8 | ax.spines["top"].set_visible(False) 9 | #ax.spines["bottom"].set_visible(False) 10 | ax.spines["right"].set_visible(False) 11 | #ax.spines["left"].set_visible(False) 12 | # 改变长宽比 13 | remove_edge(plt); 14 | names = ['-30%', '-25%', '-20%', '-15%', '-10%','-5%','+5%','+10%','+15%','+20%','+25%','+30%'] 15 | x = range(len(names)) 16 | 17 | y = [92.5, 92.5, 100, 100, 100,100,100,100,100,100,100,100] 18 | #y1= [100, 100, 100, 100, 100,100,100,100,100,100,92.5,92.5] 19 | 20 | #plt.plot(x, y, 'ro-') 21 | #plt.plot(x, y1, 'bo-') 22 | plt.xlim(-1, 12) # 限定横轴的范围 23 | plt.ylim(75, 101) # 限定纵轴的范围 24 | 25 | plt.plot(x, y, marker='o', mec='r', mfc='w',label=u'二氧化硅最大值') 26 | #plt.plot(x, y1, marker='*', ms=10,label=u'二氧化硅最小值') 27 | 28 | #plt.legend() # 让图例生效 29 | plt.xticks(x, names, rotation=45) 30 | plt.margins(0) 31 | plt.subplots_adjust(bottom=0.15) 32 | plt.xlabel(u"扰动幅度",size=24) #X轴标签 33 | plt.ylabel("准确率（%）",size=24) #Y轴标签 34 | #plt.title("A simple plot") #标题 35 | plt.rcParams["figure.figsize"] = (10, 6) 36 | plt.tick_params(labelsize=24) 37 | 38 | plt.savefig('sio2mx.png',dpi=20*12) # 保存图片 39 | #plt.savefig('sio2mn.png',dpi=20*12) # 保存图片 40 | 41 | plt.show() 42 | -------------------------------------------------------------------------------- /code/xishu.csv: -------------------------------------------------------------------------------- 1 | 1,2,3,4 2 | 0.1276,0.3162,-0.1119,1 3 | -0.4809,0.5411,1,-0.1119 4 | -0.37,1,0.5411,0.3162 5 | 1,-0.37,-0.4809,0.1276 6 | -------------------------------------------------------------------------------- /code/xishu.py: -------------------------------------------------------------------------------- 1 | import seaborn as sns 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | pd.set_option('expand_frame_repr',False) 5 | df=pd.read_csv(r"xishu.csv") 6 | y_ticks = ['表面风化','颜色','类型','纹饰'] 7 | x_ticks = ['纹饰','类型','颜色','表面风化'] # 自定义横纵轴标签 8 | plt.rcParams['font.sans-serif'] = ['SimHei'] #可以正常显示中文 9 | plt.rcParams['axes.unicode_minus'] = False #正常显示负号 10 | ax = sns.heatmap(df,annot=True,fmt='.4f',cmap="Reds",vmax=1, vmin=-1,xticklabels=x_ticks, yticklabels=y_ticks,annot_kws={'size':16},cbar=False) 11 | # annot=True表示每一个格子显示数字;fmt='.0f'表示保留0位小数，同理fmt='.1f'表示保留一位小数 12 | # camp表示颜色，在另一个博主的文章中讲解的很清晰 13 | # vmax=350, vmin=20表示右侧颜色条的最大最小值，在最大最小值外的颜色将直接以最大或最小值的颜色显示， 14 | # 通过此设置就可以解决少数值过大从而使得大部分色块区别不明显的问题 15 | # xticklabels=x_ticks, yticklabels=y_ticks，横纵轴标签 16 | #ax.set_title('分速度报警类型分布') # 图标题 17 | ax.set_xlabel('纹饰',size=16) # x轴标题 18 | ax.set_ylabel('风化程度',size=16) # y轴标题 19 | 20 | #调整图片字体大小 21 | font = {'family':'simhei', 22 | 'weight': 'normal', 23 | 'size': 16, 24 | }; 25 | #ax.legend(['未风化','风化'],prop=font,labelspacing=1);#这样就可以设置标签字体大小 26 | ax.tick_params(labelsize=16) #设置色标刻度字体大小 27 | cb=ax.figure.colorbar(ax.collections[0]) #显示colorbar 28 | cb.ax.tick_params(labelsize=16) #设置colorbar刻度字体大小。 29 | plt.rcParams["figure.figsize"] = (12, 6) 30 | 31 | plt.show() 32 | figure = ax.get_figure() 33 | 34 | figure.savefig('xishu.png',dpi=24*12) # 保存图片 35 | -------------------------------------------------------------------------------- /code/总表_编码.xlsx: 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/references/机车信号车载系统设备中载频...切换与机车信号灵敏度的分析_陈海康.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shannany0606/2022_National_Math_Modeling_Competiotion/cc569475163d3a5d35865ec0bd400a6115787a12/references/机车信号车载系统设备中载频...切换与机车信号灵敏度的分析_陈海康.pdf -------------------------------------------------------------------------------- /references/阀切换系统实现食品中维生素...、辅酶Q10的高灵敏度分析_周提军.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shannany0606/2022_National_Math_Modeling_Competiotion/cc569475163d3a5d35865ec0bd400a6115787a12/references/阀切换系统实现食品中维生素...、辅酶Q10的高灵敏度分析_周提军.pdf -------------------------------------------------------------------------------- /references/黑云母的密度和主要成分在风...岛院格庄花岗岩体风化壳为例_党丽丽.pdf: -------------------------------------------------------------------------------- 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