190 lines
145 KiB
Plaintext
190 lines
145 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6b53c4ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 绘制一个图像\n",
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"# 在一个xy坐标轴上，x轴表示运行时间，y轴表示染色数量\n",
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"# 一个种群中有8个点，这8个点对应的两个指标互不相同，我们在图上会展示5个种群\n",
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"# 我们要展示这五个种群逐渐向坐标轴的左下方收敛的过程\n",
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"# 首先构造这五个种群的数据然后完成绘图"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "655d0bbf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 600x500 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 手动指定每个种群的数据点\n",
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"# 第一个种群\n",
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"population1_x = np.array([0.80,0.003,0.31,0.185,0.006,0.002,0.212,0.295])\n",
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"population1_y = np.array([30,29,26,26,28,25,24,26])\n",
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"\n",
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"# 第二个种群\n",
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"population2_x = np.array([0.002, 0.015, 0.003, 0.35, 1.24, 0.96, 0.23, 0.12]) \n",
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"population2_y = np.array([26,26,26,26,24,23,24,26])\n",
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"\n",
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"# 第三个种群\n",
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"population3_x = np.array([0.002, 0.56, 0.003, 0.85, 1.24, 0.96, 0.24, 0.15])\n",
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"population3_y = np.array([26,24,26,22,24,23,24,22])\n",
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"\n",
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"# 第四个种群\n",
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"population4_x = np.array([0.48, 0.56, 0.23, 0.85, 1.24, 0.85, 0.24, 0.15])\n",
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"population4_y = np.array([22,24,23,22,24,23,24,22])\n",
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"\n",
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"# 第五个种群\n",
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"population5_x = np.array([0.01, 0.48, 0.18, 0.55, 0.12, 0.85, 0.24, 0.15])\n",
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"population5_y = np.array([22,24,22,22,24,23,24,22])\n",
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"\n",
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"# 整合数据\n",
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"populations_x = [population1_x, population2_x, population3_x, population4_x, population5_x]\n",
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"populations_y = [population1_y, population2_y, population3_y, population4_y, population5_y]\n",
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"\n",
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"# 设置颜色和标记\n",
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"colors = ['red', 'orange', 'green', 'blue', 'purple']\n",
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"labels = ['Generation 1', 'Generation 2', 'Generation 3', 'Generation 4', 'Generation 5']\n",
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"\n",
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"# 创建图表\n",
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"plt.figure(figsize=(6, 5))\n",
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"\n",
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"# 绘制所有种群\n",
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"for i in range(5):\n",
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" plt.scatter(populations_x[i], populations_y[i], color=colors[i], alpha=0.7, s=100, label=labels[i])\n",
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"\n",
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"# 设置图表标题和标签\n",
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"plt.xlabel('Runtime', fontsize=18)\n",
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"plt.ylabel('Number of color', fontsize=18)\n",
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"plt.grid(True, linestyle='--', alpha=0.7)\n",
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"plt.legend(loc='upper right', fontsize=16)\n",
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"\n",
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"# 调整坐标轴范围，确保所有点都可见\n",
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"plt.xlim(-0.2, 1.3)\n",
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"plt.ylim(20,31)\n",
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"\n",
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"# 保存结果(可选)\n",
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"plt.savefig('population_convergence.png', dpi=300, bbox_inches='tight')\n",
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"\n",
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"plt.tight_layout()\n",
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"# 显示图表\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "d3358507",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 手动指定每个种群的数据点\n",
|
|||
|
|
"# 第一个种群\n",
|
|||
|
|
"population1_x = np.array([0.32,0.003,0.24,0.152,0.006,0.002,0.472,0.145])\n",
|
|||
|
|
"population1_y = np.array([28,26,29,26,28,25,26,26])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 第二个种群\n",
|
|||
|
|
"population2_x = np.array([0.002, 0.015, 0.003, 0.35, 0.63, 0.46, 0.23, 0.12]) \n",
|
|||
|
|
"population2_y = np.array([26,26,26,26,24,23,24,26])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 第三个种群\n",
|
|||
|
|
"population3_x = np.array([0.002, 0.56, 0.003, 0.45, 1.23, 0.72, 0.33, 0.15])\n",
|
|||
|
|
"population3_y = np.array([26,24,26,22,24,23,24,22])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 第四个种群\n",
|
|||
|
|
"population4_x = np.array([0.48, 0.56, 0.23, 0.85, 1.19, 0.85, 0.37, 0.14])\n",
|
|||
|
|
"population4_y = np.array([22,24,23,22,24,23,24,22])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 第五个种群\n",
|
|||
|
|
"population5_x = np.array([0.62, 0.65, 0.48, 0.55, 1.12, 0.85, 0.74, 0.68])\n",
|
|||
|
|
"population5_y = np.array([22,24,22,22,24,23,24,22])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 整合数据\n",
|
|||
|
|
"populations_x = [population1_x, population2_x, population3_x, population4_x, population5_x]\n",
|
|||
|
|
"populations_y = [population1_y, population2_y, population3_y, population4_y, population5_y]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 设置颜色和标记\n",
|
|||
|
|
"colors = ['red', 'orange', 'green', 'blue', 'purple']\n",
|
|||
|
|
"labels = ['Generation 1', 'Generation 2', 'Generation 3', 'Generation 4', 'Generation 5']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 创建图表\n",
|
|||
|
|
"plt.figure(figsize=(6, 5))\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 绘制所有种群\n",
|
|||
|
|
"for i in range(5):\n",
|
|||
|
|
" plt.scatter(populations_x[i], populations_y[i], color=colors[i], alpha=0.7, s=100, label=labels[i])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 设置图表标题和标签\n",
|
|||
|
|
"plt.xlabel('Runtime', fontsize=18)\n",
|
|||
|
|
"plt.ylabel('Number of color', fontsize=18)\n",
|
|||
|
|
"plt.grid(True, linestyle='--', alpha=0.7)\n",
|
|||
|
|
"plt.legend(loc='upper right', fontsize=16)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 调整坐标轴范围，确保所有点都可见\n",
|
|||
|
|
"plt.xlim(-0.2, 1.3)\n",
|
|||
|
|
"plt.ylim(20,31)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 保存结果(可选)\n",
|
|||
|
|
"plt.savefig('population_convergence1.png', dpi=300, bbox_inches='tight')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.tight_layout()\n",
|
|||
|
|
"# 显示图表\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "lead",
|
|||
|
|
"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": "3.11.11"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|