diff --git a/02.01 基于搜索的问题求解(学生版).ipynb b/02.01 基于搜索的问题求解(学生版).ipynb
index 6ec8a47..e1f9951 100644
--- a/02.01 基于搜索的问题求解(学生版).ipynb
+++ b/02.01 基于搜索的问题求解(学生版).ipynb
@@ -378,6 +378,17 @@
"metadata": {},
"source": [
"**答案 2**：（在此处填写你的答案。）\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "1. [Introduction to the A* Algorithm](https://www.redblobgames.com/pathfinding/a-star/introduction.html)\n",
+ "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",
+ "3. [A* Search Algorithm](https://www.101computing.net/a-star-search-algorithm/)\n",
+ "4. [A star Pathfinding A星寻路算法](https://www.bilibili.com/video/av32847834/)"
]
}
],
diff --git a/02.01 基于搜索的问题求解.ipynb b/02.01 基于搜索的问题求解.ipynb
index ae027e1..f68ebf1 100644
--- a/02.01 基于搜索的问题求解.ipynb
+++ b/02.01 基于搜索的问题求解.ipynb
@@ -78,7 +78,7 @@
" \"E\": (7, 4), \"F\": (6,6),\"G\": (11,5)}\n",
"\n",
"# 绘制无向图\n",
- "g = SearchGraph(node_list, weighted_edges_list, 'A', 'F', max_depth=3, nodes_pos = nodes_pos)\n",
+ "g = SearchGraph(node_list, weighted_edges_list, 'A', 'G', max_depth=3, nodes_pos = nodes_pos)\n",
"g.show_graph()"
]
},
@@ -242,6 +242,45 @@
"metadata": {},
"source": [
"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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 扩展内容\n",
+ "** 深度优先搜索 dfs ** 基础代码解读\n",
+ "\n",
+ "```\n",
+ "def iter_dfs(G, start, target):\n",
+ " '''\n",
+ " 深度优先搜索\n",
+ " :param G: 字典，存储每个点的相邻点\n",
+ " :param start: 初始点\n",
+ " :param target: 目标点\n",
+ " :return:\n",
+ " '''\n",
+ "\n",
+ " # 定义已访问的点的集合\n",
+ " S = set()\n",
+ " # 定义一个待访问点的列表\n",
+ " Q = []\n",
+ " # 把初始点放进列表中\n",
+ " Q.append(start)\n",
+ " while Q:\n",
+ " # 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",
+ " u = Q.pop()\n",
+ " # 如果当前点是目标点，则结束查找\n",
+ " if u == target:\n",
+ " break\n",
+ " # 如果该点已经被访问了，则跳过此点\n",
+ " if u in S:\n",
+ " continue\n",
+ " # 访问此点，将点加入已访问点的结合 S 中\n",
+ " S.add(u)\n",
+ " # 将点 u 相邻的点放入待访问的列表中\n",
+ " Q.extend(G[u])\n",
+ "```"
]
},
{
@@ -409,6 +448,18 @@
"source": [
"# 查看 dfs 的搜索过程\n",
"h_graph.animation_search_tree('dfs')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "1. [Introduction to the A* Algorithm](https://www.redblobgames.com/pathfinding/a-star/introduction.html)\n",
+ "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",
+ "3. [A* Search Algorithm](https://www.101computing.net/a-star-search-algorithm/)\n",
+ "4. [A star Pathfinding A星寻路算法](https://www.bilibili.com/video/av32847834/)\n",
+ " "
]
}
],
diff --git a/02.02 决策树(学生版).ipynb b/02.02 决策树(学生版).ipynb
index 626c938..0dd0f17 100644
--- a/02.02 决策树(学生版).ipynb
+++ b/02.02 决策树(学生版).ipynb
@@ -922,6 +922,17 @@
"source": [
"**答案 1**：（在此处填写你的答案。）"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "\n",
+ "1. [决策树与随机森林](https://www.bilibili.com/video/av26086646?from=search&seid=6716049859412037731)\n",
+ "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",
+ "3. [sklearn 决策树](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
+ ]
}
],
"metadata": {
diff --git a/02.02 决策树.ipynb b/02.02 决策树.ipynb
index 7a3176b..2d96567 100644
--- a/02.02 决策树.ipynb
+++ b/02.02 决策树.ipynb
@@ -794,6 +794,17 @@
"source": [
"ent = cal_essay_entropy(en_essay, split_by = ' ')\n"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "\n",
+ "1. [决策树与随机森林](https://www.bilibili.com/video/av26086646?from=search&seid=6716049859412037731)\n",
+ "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",
+ "3. [sklearn 决策树](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
+ ]
}
],
"metadata": {
diff --git a/02.03 回归分析(学生版).ipynb b/02.03 回归分析(学生版).ipynb
index 0f0afd1..8c10f8a 100644
--- a/02.03 回归分析(学生版).ipynb
+++ b/02.03 回归分析(学生版).ipynb
@@ -491,6 +491,16 @@
"plt.plot(x_predict, z_predict, c='b')\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "\n",
+ "1. [线性回归白板推导](https://www.bilibili.com/video/av31989606?from=search&seid=15463936019723788543)\n",
+ "2. [sklearn 线性回归](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
+ ]
}
],
"metadata": {
diff --git a/02.03 回归分析.ipynb b/02.03 回归分析.ipynb
index 78eaf07..575cc82 100644
--- a/02.03 回归分析.ipynb
+++ b/02.03 回归分析.ipynb
@@ -512,6 +512,16 @@
"plt.plot(x_predict, z_predict, c='b')\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "\n",
+ "1. [线性回归白板推导](https://www.bilibili.com/video/av31989606?from=search&seid=15463936019723788543)\n",
+ "2. [sklearn 线性回归](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
+ ]
}
],
"metadata": {
diff --git a/02.04 贝叶斯分析(学生版).ipynb b/02.04 贝叶斯分析(学生版).ipynb
index ab31e33..7f6068c 100644
--- a/02.04 贝叶斯分析(学生版).ipynb
+++ b/02.04 贝叶斯分析(学生版).ipynb
@@ -482,6 +482,16 @@
"source": [
"**答案 1**：（在此处填写你的答案。）"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "1. [一文读懂概率论学习：贝叶斯理论](https://www.jiqizhixin.com/articles/2019-11-21)\n",
+ "2. [朴素贝叶斯法讲解](https://www.bilibili.com/video/av57126177?from=search&seid=1588787263892359481)\n",
+ "3. [sklearn 贝叶斯方法](https://scikit-learn.org/stable/modules/naive_bayes.html)"
+ ]
}
],
"metadata": {
diff --git a/02.04 贝叶斯分析.ipynb b/02.04 贝叶斯分析.ipynb
index 954441f..0acba03 100644
--- a/02.04 贝叶斯分析.ipynb
+++ b/02.04 贝叶斯分析.ipynb
@@ -468,6 +468,16 @@
"imgs = get_imgs(test_images, test_labels, test_predict_BNB, 9, 4)\n",
"plot_images(imgs)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 扩展阅读\n",
+ "1. [一文读懂概率论学习：贝叶斯理论](https://www.jiqizhixin.com/articles/2019-11-21)\n",
+ "2. [朴素贝叶斯法讲解](https://www.bilibili.com/video/av57126177?from=search&seid=1588787263892359481)\n",
+ "3. [sklearn 贝叶斯方法](https://scikit-learn.org/stable/modules/naive_bayes.html)"
+ ]
}
],
"metadata": {
diff --git a/02.05 神经网络学习(学生版).ipynb b/02.05 神经网络学习(学生版).ipynb
index 70f5f2a..c29cf61 100644
--- a/02.05 神经网络学习(学生版).ipynb
+++ b/02.05 神经网络学习(学生版).ipynb
@@ -25,14 +25,60 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![](\"http://imgbed.momodel.cn//20200103102429.png\")
"
+ "\n",
+ " \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114133721.png\"/) | \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114133731.png\"/) | \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114133746.png\"/) | \n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114133800.png\") | \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114133808.png\") | \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"http://imgbed.momodel.cn/微信图片_20200114133755.png\"/)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
]
},
{
@@ -48,7 +94,13 @@
"source": [
"**感知机模型**：\n",
"\n",
- "![](\"http://imgbed.momodel.cn/感知器模型.png\")
"
+ "\n",
+ " \n",
+ " | \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114135559.png\") \n",
+ " ![](\"http://imgbed.momodel.cn/微信图片_20200114135643.png\") | \n",
+ "
\n",
+ "
"
]
},
{
@@ -215,7 +267,15 @@
"\n",
"与感知机的不同，神经网络：\n",
"+ 输入层和输出层之间存在若干隐藏层。\n",
- "+ 每个隐藏层中包含若干神经元。\n"
+ "+ 每个隐藏层中包含若干神经元。\n",
+ "\n",
+ "神经网络流程：\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
]
},
{
diff --git a/02.05 神经网络学习.ipynb b/02.05 神经网络学习.ipynb
index d0e99bd..404a236 100644
--- a/02.05 神经网络学习.ipynb
+++ b/02.05 神经网络学习.ipynb
@@ -25,14 +25,60 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "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。"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
+ "\n",
+ " \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
]
},
{
@@ -48,7 +94,13 @@
"source": [
"**感知机模型**：\n",
"\n",
- ""
+ "\n",
+ " \n",
+ " | \n",
+ " \n",
+ " | \n",
+ "
\n",
+ "
"
]
},
{
@@ -217,7 +269,15 @@
"\n",
"与感知机的不同，神经网络：\n",
"+ 输入层和输出层之间存在若干隐藏层。\n",
- "+ 每个隐藏层中包含若干神经元。\n"
+ "+ 每个隐藏层中包含若干神经元。\n",
+ "\n",
+ "神经网络流程：\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
]
},
{
diff --git a/search.py b/search.py
index e9a5f67..934479e 100644
--- a/search.py
+++ b/search.py
@@ -401,7 +401,7 @@
def generate_greedy_help_text(self,path):
if path[-1] == self.target_node:
return '抵达目标节点' + str(self.target_node)
- elif len(path) == self.max_depth+1:
+ elif path not in self.search_scores:
return '抵达最大搜索深度，未找到目标节点'
base_text = '当前可选的子节点及其信息值为 \n'+ \