Commit a0fee34040c97e5c2473ea01c3bde56fdb60e9e8 - luxu1011578034644 - (('Mo Repos',), {'htdigest_file': None, 'use_smarthttp': 0, 'require_browser_auth': 0, 'disable_push': 0, 'unauthenticated_push': 0, 'ctags

+11

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377	377	"metadata": {},
378	378	"source": [
379	379	"答案 2：（在此处填写你的答案。）\n"
	380	]
	381	},
	382	{
	383	"cell_type": "markdown",
	384	"metadata": {},
	385	"source": [
	386	"## 扩展阅读\n",
	387	"1. [Introduction to the A* Algorithm](https://www.redblobgames.com/pathfinding/a-star/introduction.html)\n",
	388	"2. [游戏开发中的人工智能：A* 路径寻找算法](https://blog.csdn.net/Jurbo/article/details/75532885)\n",
	389	"3. [A* Search Algorithm](https://www.101computing.net/a-star-search-algorithm/)\n",
	390	"4. [A star Pathfinding A星寻路算法](https://www.bilibili.com/video/av32847834/)"
380	391	]
381	392	}
382	393	],

+52

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02.01 基于搜索的问题求解.ipynb less more

77	77	" \"E\": (7, 4), \"F\": (6,6),\"G\": (11,5)}\n",
78	78	"\n",
79	79	"# 绘制无向图\n",
80		"g = SearchGraph(node_list, weighted_edges_list, 'A', 'F', max_depth=3, nodes_pos = nodes_pos)\n",
	80	"g = SearchGraph(node_list, weighted_edges_list, 'A', 'G', max_depth=3, nodes_pos = nodes_pos)\n",
81	81	"g.show_graph()"
82	82	]
83	83	},

241	241	"metadata": {},
242	242	"source": [
243	243	"需要强调的是，对于一个搜索问题，只要存在答案（即从初始节点到终止节点存在满足条件的一条路径），那么排除了回路的深度优先搜索和广度优先搜索均能找到一个答案，但是这个找到的答案不一定是最优的，例如距离最短。"
	244	]
	245	},
	246	{
	247	"cell_type": "markdown",
	248	"metadata": {},
	249	"source": [
	250	"### 扩展内容\n",
	251	" 深度优先搜索 dfs 基础代码解读\n",
	252	"\n",
	253	"```\n",
	254	"def iter_dfs(G, start, target):\n",
	255	" '''\n",
	256	" 深度优先搜索\n",
	257	" :param G: 字典，存储每个点的相邻点\n",
	258	" :param start: 初始点\n",
	259	" :param target: 目标点\n",
	260	" :return:\n",
	261	" '''\n",
	262	"\n",
	263	" # 定义已访问的点的集合\n",
	264	" S = set()\n",
	265	" # 定义一个待访问点的列表\n",
	266	" Q = []\n",
	267	" # 把初始点放进列表中\n",
	268	" Q.append(start)\n",
	269	" while Q:\n",
	270	" # 只要带访问的列表不为空，那么从列表中拿取最后一个元素，也就是一个点，记作 u\n",
	271	" u = Q.pop()\n",
	272	" # 如果当前点是目标点，则结束查找\n",
	273	" if u == target:\n",
	274	" break\n",
	275	" # 如果该点已经被访问了，则跳过此点\n",
	276	" if u in S:\n",
	277	" continue\n",
	278	" # 访问此点，将点加入已访问点的结合 S 中\n",
	279	" S.add(u)\n",
	280	" # 将点 u 相邻的点放入待访问的列表中\n",
	281	" Q.extend(G[u])\n",
	282	"```"
244	283	]
245	284	},
246	285	{

408	447	"source": [
409	448	"# 查看 dfs 的搜索过程\n",
410	449	"h_graph.animation_search_tree('dfs')"
	450	]
	451	},
	452	{
	453	"cell_type": "markdown",
	454	"metadata": {},
	455	"source": [
	456	"## 扩展阅读\n",
	457	"1. [Introduction to the A* Algorithm](https://www.redblobgames.com/pathfinding/a-star/introduction.html)\n",
	458	"2. [游戏开发中的人工智能：A* 路径寻找算法](https://blog.csdn.net/Jurbo/article/details/75532885)\n",
	459	"3. [A* Search Algorithm](https://www.101computing.net/a-star-search-algorithm/)\n",
	460	"4. [A star Pathfinding A星寻路算法](https://www.bilibili.com/video/av32847834/)\n",
	461	" "
411	462	]
412	463	}
413	464	],

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02.02 决策树(学生版).ipynb less more

921	921	"source": [
922	922	"答案 1：（在此处填写你的答案。）"
923	923	]
	924	},
	925	{
	926	"cell_type": "markdown",
	927	"metadata": {},
	928	"source": [
	929	"## 扩展阅读\n",
	930	"\n",
	931	"1. [决策树与随机森林](https://www.bilibili.com/video/av26086646?from=search&seid=6716049859412037731)\n",
	932	"2. [从决策树到随机森林：树型算法的原理与实现](https://www.jiqizhixin.com/articles/2017-07-31-3)\n",
	933	"3. [sklearn 决策树](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
	934	]
924	935	}
925	936	],
926	937	"metadata": {

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02.02 决策树.ipynb less more

793	793	"source": [
794	794	"ent = cal_essay_entropy(en_essay, split_by = ' ')\n"
795	795	]
	796	},
	797	{
	798	"cell_type": "markdown",
	799	"metadata": {},
	800	"source": [
	801	"## 扩展阅读\n",
	802	"\n",
	803	"1. [决策树与随机森林](https://www.bilibili.com/video/av26086646?from=search&seid=6716049859412037731)\n",
	804	"2. [从决策树到随机森林：树型算法的原理与实现](https://www.jiqizhixin.com/articles/2017-07-31-3)\n",
	805	"3. [sklearn 决策树](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
	806	]
796	807	}
797	808	],
798	809	"metadata": {

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02.03 回归分析(学生版).ipynb less more

490	490	"plt.plot(x_predict, z_predict, c='b')\n",
491	491	"plt.show()"
492	492	]
	493	},
	494	{
	495	"cell_type": "markdown",
	496	"metadata": {},
	497	"source": [
	498	"## 扩展阅读\n",
	499	"\n",
	500	"1. [线性回归白板推导](https://www.bilibili.com/video/av31989606?from=search&seid=15463936019723788543)\n",
	501	"2. [sklearn 线性回归](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
	502	]
493	503	}
494	504	],
495	505	"metadata": {

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02.03 回归分析.ipynb less more

511	511	"plt.plot(x_predict, z_predict, c='b')\n",
512	512	"plt.show()"
513	513	]
	514	},
	515	{
	516	"cell_type": "markdown",
	517	"metadata": {},
	518	"source": [
	519	"## 扩展阅读\n",
	520	"\n",
	521	"1. [线性回归白板推导](https://www.bilibili.com/video/av31989606?from=search&seid=15463936019723788543)\n",
	522	"2. [sklearn 线性回归](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
	523	]
514	524	}
515	525	],
516	526	"metadata": {

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02.04 贝叶斯分析(学生版).ipynb less more

481	481	"source": [
482	482	"答案 1：（在此处填写你的答案。）"
483	483	]
	484	},
	485	{
	486	"cell_type": "markdown",
	487	"metadata": {},
	488	"source": [
	489	"## 扩展阅读\n",
	490	"1. [一文读懂概率论学习：贝叶斯理论](https://www.jiqizhixin.com/articles/2019-11-21)\n",
	491	"2. [朴素贝叶斯法讲解](https://www.bilibili.com/video/av57126177?from=search&seid=1588787263892359481)\n",
	492	"3. [sklearn 贝叶斯方法](https://scikit-learn.org/stable/modules/naive_bayes.html)"
	493	]
484	494	}
485	495	],
486	496	"metadata": {

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02.04 贝叶斯分析.ipynb less more

467	467	"imgs = get_imgs(test_images, test_labels, test_predict_BNB, 9, 4)\n",
468	468	"plot_images(imgs)"
469	469	]
	470	},
	471	{
	472	"cell_type": "markdown",
	473	"metadata": {},
	474	"source": [
	475	"## 扩展阅读\n",
	476	"1. [一文读懂概率论学习：贝叶斯理论](https://www.jiqizhixin.com/articles/2019-11-21)\n",
	477	"2. [朴素贝叶斯法讲解](https://www.bilibili.com/video/av57126177?from=search&seid=1588787263892359481)\n",
	478	"3. [sklearn 贝叶斯方法](https://scikit-learn.org/stable/modules/naive_bayes.html)"
	479	]
470	480	}
471	481	],
472	482	"metadata": {

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02.05 神经网络学习(学生版).ipynb less more

24	24	"cell_type": "markdown",
25	25	"metadata": {},
26	26	"source": [
27		"人眼在辨识图片时，会先提取边缘特征，再识别部件，最后再得到最高层的模式。也就是说，高层的特征是低层特征的组合，从低层到高层的特征表示越来越抽象，越来越能表现语义。"
28		]
29		},
30		{
31		"cell_type": "markdown",
32		"metadata": {},
33		"source": [
34		"<img src=\"http://imgbed.momodel.cn//20200103102429.png\" width=300>"
	27	"<table>\n",
	28	" <tr>\n",
	29	" <td ><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133721.png\"/></center></td>\n",
	30	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133731.png\"/></center></td>\n",
	31	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133746.png\"/></center></td>\n",
	32	" </tr>\n",
	33	"</table>\n"
	34	]
	35	},
	36	{
	37	"cell_type": "markdown",
	38	"metadata": {},
	39	"source": [
	40	"高层的特征是低层特征的组合，从低层到高层的特征表示越来越抽象，越来越能表现语义。"
	41	]
	42	},
	43	{
	44	"cell_type": "markdown",
	45	"metadata": {},
	46	"source": [
	47	"<table>\n",
	48	" <tr>\n",
	49	" <td ><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133800.png\" width=600px/></center></td>\n",
	50	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133808.png\" width=600px/></center></td>\n",
	51	" </tr>\n",
	52	"</table>"
	53	]
	54	},
	55	{
	56	"cell_type": "markdown",
	57	"metadata": {},
	58	"source": [
	59	"人工智能中神经网络正是体现“逐层抽象、渐进学习”机制的学习模型。"
	60	]
	61	},
	62	{
	63	"cell_type": "markdown",
	64	"metadata": {},
	65	"source": [
	66	"<img src=\"http://imgbed.momodel.cn/微信图片_20200114133755.png\"/>"
	67	]
	68	},
	69	{
	70	"cell_type": "markdown",
	71	"metadata": {},
	72	"source": [
	73	"人眼在辨识图片时，会先提取边缘特征，再识别部件，最后再得到最高层的模式。"
	74	]
	75	},
	76	{
	77	"cell_type": "markdown",
	78	"metadata": {},
	79	"source": [
	80	"<img src=\"http://imgbed.momodel.cn//20200103102429.png\" width=500>"
35	81	]
36	82	},
37	83	{

47	93	"source": [
48	94	"感知机模型：\n",
49	95	"\n",
50		"<img src=\"http://imgbed.momodel.cn/感知器模型.png\" width=300/>"
	96	"<table>\n",
	97	" <tr>\n",
	98	" <td ><center><img src=\"http://imgbed.momodel.cn/感知器模型.png\" width=400/></center></td>\n",
	99	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114135559.png\" width=400/>\n",
	100	" <img src=\"http://imgbed.momodel.cn/微信图片_20200114135643.png\" width=400/></center></td>\n",
	101	" </tr>\n",
	102	"</table>"
51	103	]
52	104	},
53	105	{

214	266	"\n",
215	267	"与感知机的不同，神经网络：\n",
216	268	"+ 输入层和输出层之间存在若干隐藏层。\n",
217		"+ 每个隐藏层中包含若干神经元。\n"
	269	"+ 每个隐藏层中包含若干神经元。\n",
	270	"\n",
	271	"神经网络流程：\n",
	272	"<center><video src=\"https://files.momodel.cn/media1.mp4\" controls=\"controls\" width=800px></center>\n",
	273	"<center><video src=\"https://files.momodel.cn/media2.mp4\" controls=\"controls\" width=800px></center>\n",
	274	"<center><video src=\"https://files.momodel.cn/media3.mp4\" controls=\"controls\" width=800px></center>\n",
	275	"<center><video src=\"https://files.momodel.cn/media4.mp4\" controls=\"controls\" width=800px></center>\n",
	276	"<center><video src=\"https://files.momodel.cn/media5.mp4\" controls=\"controls\" width=800px></center>\n",
	277	"<center><video src=\"https://files.momodel.cn/media6.mp4\" controls=\"controls\" width=800px></center>\n"
218	278	]
219	279	},
220	280	{

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02.05 神经网络学习.ipynb less more

24	24	"cell_type": "markdown",
25	25	"metadata": {},
26	26	"source": [
27		"人眼在辨识图片时，会先提取边缘特征，再识别部件，最后再得到最高层的模式。也就是说，高层的特征是低层特征的组合，从低层到高层的特征表示越来越抽象，越来越能表现语义。"
28		]
29		},
30		{
31		"cell_type": "markdown",
32		"metadata": {},
33		"source": [
34		"<img src=\"http://imgbed.momodel.cn//20200103102429.png\" width=300>"
	27	"<table>\n",
	28	" <tr>\n",
	29	" <td ><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133721.png\"/></center></td>\n",
	30	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133731.png\"/></center></td>\n",
	31	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133746.png\"/></center></td>\n",
	32	" </tr>\n",
	33	"</table>\n"
	34	]
	35	},
	36	{
	37	"cell_type": "markdown",
	38	"metadata": {},
	39	"source": [
	40	"<table>\n",
	41	" <tr>\n",
	42	" <td ><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133800.png\" width=600px/></center></td>\n",
	43	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114133808.png\" width=600px/></center></td>\n",
	44	" </tr>\n",
	45	"</table>"
	46	]
	47	},
	48	{
	49	"cell_type": "markdown",
	50	"metadata": {},
	51	"source": [
	52	"高层的特征是低层特征的组合，从低层到高层的特征表示越来越抽象，越来越能表现语义。\n"
	53	]
	54	},
	55	{
	56	"cell_type": "markdown",
	57	"metadata": {},
	58	"source": [
	59	"人工智能中神经网络正是体现“逐层抽象、渐进学习”机制的学习模型。"
	60	]
	61	},
	62	{
	63	"cell_type": "markdown",
	64	"metadata": {},
	65	"source": [
	66	"<img src=\"http://imgbed.momodel.cn/微信图片_20200114133755.png\"/>\n"
	67	]
	68	},
	69	{
	70	"cell_type": "markdown",
	71	"metadata": {},
	72	"source": [
	73	"人眼在辨识图片时，会先提取边缘特征，再识别部件，最后再得到最高层的模式。"
	74	]
	75	},
	76	{
	77	"cell_type": "markdown",
	78	"metadata": {},
	79	"source": [
	80	"<img src=\"http://imgbed.momodel.cn//20200103102429.png\" width=500>"
35	81	]
36	82	},
37	83	{

47	93	"source": [
48	94	"感知机模型：\n",
49	95	"\n",
50		"<img src=\"http://imgbed.momodel.cn/感知器模型.png\" width=300/>"
	96	"<table>\n",
	97	" <tr>\n",
	98	" <td ><center><img src=\"http://imgbed.momodel.cn/感知器模型.png\" width=400/></center></td>\n",
	99	" <td><center><img src=\"http://imgbed.momodel.cn/微信图片_20200114135559.png\" width=400/>\n",
	100	" <img src=\"http://imgbed.momodel.cn/微信图片_20200114135643.png\" width=400/></center></td>\n",
	101	" </tr>\n",
	102	"</table>"
51	103	]
52	104	},
53	105	{

216	268	"\n",
217	269	"与感知机的不同，神经网络：\n",
218	270	"+ 输入层和输出层之间存在若干隐藏层。\n",
219		"+ 每个隐藏层中包含若干神经元。\n"
	271	"+ 每个隐藏层中包含若干神经元。\n",
	272	"\n",
	273	"神经网络流程：\n",
	274	"<center><video src=\"https://files.momodel.cn/media1.mp4\" controls=\"controls\" width=800px></center>\n",
	275	"<center><video src=\"https://files.momodel.cn/media2.mp4\" controls=\"controls\" width=800px></center>\n",
	276	"<center><video src=\"https://files.momodel.cn/media3.mp4\" controls=\"controls\" width=800px></center>\n",
	277	"<center><video src=\"https://files.momodel.cn/media4.mp4\" controls=\"controls\" width=800px></center>\n",
	278	"<center><video src=\"https://files.momodel.cn/media5.mp4\" controls=\"controls\" width=800px></center>\n",
	279	"<center><video src=\"https://files.momodel.cn/media6.mp4\" controls=\"controls\" width=800px></center>\n"
220	280	]
221	281	},
222	282	{

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search.py less more

400	400	def generate_greedy_help_text(self,path):
401	401	if path[-1] == self.target_node:
402	402	return '抵达目标节点' + str(self.target_node)
403		elif len(path) == self.max_depth+1:
	403	elif path not in self.search_scores:
404	404	return '抵达最大搜索深度，未找到目标节点'
405	405
406	406	base_text = '当前可选的子节点及其信息值为 \n'+ \