privoxy and proxifier privoxy - 把socks proxy 暴露成http proxy proxifier - Let All system flow go through proxy

Self-hosted Cloud Storage: Nextcloud vs. ownCloud vs. Seafile Updated at 2020-07-05 11:00: If you meet usability issues, consider Alternatives: VerySync(微力同步), Resilio Sync, Syncthing, Tresorit, Pydio, ownCloud, rsync, GoodSync. Why do you need to ditch nextcloud, owncloud, seafile, blabla… Updated at 2020-05-19 07:36: I recently used seafile and nextCloud. tried deploying them. But what I’ve found: the advantage of nextCloud is that the developers seem to be more responsible, the disadvantage is that it doesn’t allow users to register their own accounts and requires administrators to open new ones.

常用数学符号的 LaTeX 表示方法 My notes: 空格间隙设置表： ** 两个quad空格** a \qquad b $a \qquad b$ 两个m的宽度 quad空格 a \quad b $a \quad b$ 一个m的宽度 大空格 a\ b $a\ b$ 1/3m宽度 中等空格 a;b $a;b$ 2/7m宽度 小空格 a,b $a,b$ 1/6m宽度 没有空格 ab $ab,$ 紧贴 a!b $a!b$ 缩进1/6m宽度 正下方的下标 $\min \limits_{a,b} a x^2 + 2b$ : $\min \limits_{a,b} a x^2 + 2b$ ** Mathematical Fonts ** Let $ \mathcal{T} $ be a topological space, a basis is defined as \mathcal{B} = \{B_{\alpha} \in \mathcal{T}\, |\, U = \bigcup B_{\alpha} \forall U \in \mathcal{T} \} Let $ \mathcal{T} $ be a topological space, a basis is defined as $ \mathcal{B} = {B_{\alpha} \in \mathcal{T}, |, U = \bigcup B_{\alpha} \forall U \in \mathcal{T} } $

中国计算机学会推荐国际学术会议和期刊目录 （2019年）中国计算机学会推荐国际学术会议和期刊目录 中国计算机学会推荐国际学术期刊 (数据库/数据挖掘/内容检索) 一、A类 序号 刊物简称 刊物全称 出版社 网址 1 TODS ACM Transactions on Database Systems ACM http://dblp.uni-trier.de/db/journals/tods/ 2 TOIS ACM Transactions on Information Systems ACM http://dblp.uni-trier.de/db/journals/tois/ 3 TKDE IEEE Transactions on Knowledge and Data Engineering IEEE http://dblp.uni-trier.de/db/journals/tkde/ 4 VLDBJ The VLDB Journal Springer http://dblp.uni-trier.de/db/journals/vldb/ 二、B类 序号 刊物简称 刊物全称 出版社 网址 (^1) TKDD ACM Transactions on Knowledge Discovery from Data ACM http://dblp.uni-trier.de/db/journals/tkdd/ (^2) TWEB ACM Transactions on the Web ACM http://dblp.uni-trier.de/db/journals/tweb/ (^3) AEI Advanced Engineering Informatics Elsevier http://dblp.uni-trier.de/db/journals/aei/ (^4) DKE Data and Knowledge Engineering Elsevier http://dblp.

检查条件，如果不满足，则提示。 assert os.path.isdir(my_Path), 'dir path error' refer python 异常: 8. Errors and Exceptionshttps://docs.python.org/3/tutorial/errors.html . . . . . . . . .

in my impression, the gradient descent is for finding the independent variable that can get the minimum/maximum value of an objective function. So we need an obj. function: $\mathcal{L}$ an obj. function: $\mathcal{L}$ The gradient of $\mathcal{L}: 2x+2$ $\Delta x$ , The value of idependent variable needs to be updated: $x \leftarrow x+\Delta x$ 1. the $\mathcal{L}$ is a context function: $f(x)=x^2+2x+1$ how to find the $x_0$ that makes the $f(x)$ has the minimum value, via gradient descent? Start with an arbitrary $x$, calculate the value of $f(x)$ :

问题背景：在大量文本中，对每个单词的出现次数进行统计，可以得到一张单词出现次数的表格。如： word #existance #of word with same existance a 100,000 20 an 100,000 20 boy 99,888 19 cat 99,877 18 … … … dog 5000 3000 eat 4000 3000 … … … folkloristic 20 80000 wacky 10 80000 zannichelliaceae 1 90009 后面2列，可据此形成二维坐标中的某个点。比如出现次数为100,000的单词，有20个。则对应坐标的横轴100,000，纵轴20. 将所有的行都在坐标中标注，并去掉重复的标注点，即可得到如下的图： 文本中的幂率分布即如下现象： 可以看到具有低出现次数的单词（横坐标较小），数量很大（纵坐标较大）。反之，具有高出现次数的单词，比如a, of, with, for … 数量并不多。而且在幂坐标系里，基本呈现线性，这就是所谓的幂率分布。 可看成是一排学生，出现的次数相当于学生的身高。那些身高很高的学生个数并不多，而身高不高的学生数量庞大。大量单词仅仅出现少量的几次。 对于Random Walk爬取的context， 其节点出现的频率与具有此频率的节点个数之间，也服从类似word 的幂率分布，这是DeepWalk拿来说事儿的根基。如图： 。 网络节点的度分布与power law 看一个netGAN 论文（ICML2018）中的图： 度数小的节点，位于x轴左侧，数量较大，y轴上侧。度数大的节点，位于x轴右侧，在y轴上处于值较小的位置，及数量较少。

有向图变无向图并存储 Transform directed graph into undirected graph. ''' ''' import networkx as nx edgelist_path = 'fq_following.number' edgelist=[] with open(edgelist_path, 'r') as edgelistX_reader: # input anchor for bind for line in edgelistX_reader.readlines(): temp_array = line.strip().split(' ') # edgelist.append(list(map(int, temp_array))) # for netX, directly append edgelistX_reader.close() nodeID_set = set() # set for store the nodes for i in edgelist: nodeID_set.add(i[0]) # nodeID_set.add(i[1]) # edges = [tuple(e) for e in edgelist] node_names = [n for n in nodeID_set] G = nx.DiGraph() G.add_nodes_from(node_names) G.add_edges_from(edges) print(nx.

python 处理标签常常需要将一组标签映射到一组数字，数字还要求连续。 比如 [‘a’, ‘b’, ‘c’, ‘a’, ‘a’, ‘b’, ‘c’] ==(a->0, b->1, c->2)=> [0, 1, 2, 0, 0, 1, 2]。 为了便于本文被搜索，加个关键词：重新编码 可以用sklearn.preprocessing.LabelEncoder()这个函数。 以数字标签为例： from sklearn import preprocessing le = preprocessing.LabelEncoder() le.fit([1,2,2,6,3]) #获取标签值# In [2]: le.classes_ Out[2]: array([1, 2, 3, 6]) #将标签值标准化# In [3]: le.transform([1,1,3,6,2]) Out[3]: array([0, 0, 2, 3, 1], dtype=int64) #将标准化的标签值反转# 即“反向编码”： In [4]: le.inverse_transform([0, 0, 2, 3, 1]) Out[4]: array([1, 1, 3, 6, 2]) 非数字型标签值标准化： In [5]: from sklearn import preprocessing ...: le =preprocessing.LabelEncoder() ...: le.fit(["paris", "paris", "tokyo", "amsterdam"]) .

为什么在windows里，首选的conda命令行工具是Anaconda command prompt？ In windows, what’s the difference between command prompt and anaconda prompt Anaconda command prompt is just like command prompt, but it makes sure that you are able to use anaconda and conda commands from the prompt, without having to change directories or your path. When you start Anaconda command prompt, you’ll notice that it adds/(“prepends”) a bunch of locations to your PATH. These locations contain commands and scripts that you can run. So as long as you’re in the Anaconda command prompt, you know you can use these commands.