this same question has been asked several times, and most answers where related to TCP/IP . But im looking for Bluetooth related. Im trying to send information between 2 machines through bluetooth. I installed pybluez on both linux and windows, It worked fine on discovering other nearby devices on both Os. Later i used this code as an example to send information. It worked fine when, client
这个同样的问题已经被多次提出，并且大部分答案都与TCP/IP 。 但即时通讯寻找Bluetooth相关。 我试图通过蓝牙在两台机器之间发送信息。 我在linux和windows上都安装了pybluez ，它在两个Os上发现其他附近设备时工作得很好。 后来我用这个代码作为例子发送信息。 它工作得很好，当客户端是Linux机器和服务器是Linux机器。 当我在Windows7上运行服务器端代码时，我得到了错误 server_sock.bind(("",port)) File "C:Python
python如何以数学方式计算这个模数？ >>>-1%10 9 The Wikipedia article on the modulo operation provides the following constraint for a % q : a = nq + r Substituting a = -1 , q = 10 and r = 9 , we see that n must be equal -1. Plugging in -1 for n: -1 % 10 # Python evaluates this as 9 -1 = n * 10 + r -1 = -1 * 10 + r 9 = r Testing with another example (again plugging in -1 for n):
python如何以数学方式计算这个模数？ >>>-1%10 9 关于模运算的维基百科文章为a % q提供了以下约束条件： a = nq + r 代入a = -1 ， q = 10和r = 9 ，我们看到n必须等于-1。 在n中插入-1： -1 % 10 # Python evaluates this as 9 -1 = n * 10 + r -1 = -1 * 10 + r 9 = r 用另一个例子进行测试（对于n再次插入-1）： -7 % 17 # Python evaluates this as 10 -7 = n * 17 + r -7 = -17 + r 10 = r 分子为正
Using pandas I can compute simple moving average SMA using pandas.stats.moments.rolling_mean exponential moving average EMA using pandas.stats.moments.ewma But how do I compute a weighted moving average (WMA) as described in wikipedia http://en.wikipedia.org/wiki/Exponential_smoothing ... using pandas? Is there a pandas function to compute a WMA? No, there is no implementation of that e
使用我可以计算的熊猫 简单移动平均SMA使用pandas.stats.moments.rolling_mean 指数移动平均EMA使用pandas.stats.moments.ewma 但是，如何计算维基百科http://en.wikipedia.org/wiki/Exponential_smoothing中描述的使用熊猫的加权移动平均值（WMA）？ 是否有熊猫功能来计算WMA？ 不，没有具体算法的实现。 在这里创建一个关于它的GitHub问题： https://github.com/pydata/pandas/issues/886 我很乐意为此采取拉取
I'm not sure if what I want to do is possible: I'm trying to get a block in a parent template to be filled out by a file included in a child template of the parent. The best way to explain this is a test case: File t1.djhtml : <root> <block t3_container> {% block t3 %}This should be 'CONTENT'{% endblock %} </block t3_container> <block t2_cont
我不确定我想要做什么是可能的：我试图让父模板中的块被包含在父模板中的文件填充。 解释这个最好的方法是一个测试用例： 文件t1.djhtml ： <root> <block t3_container> {% block t3 %}This should be 'CONTENT'{% endblock %} </block t3_container> <block t2_container> {% block t2 %}{% endblock %} </block t2_container> </root> 文件t2.djhtml
In C# there's a null-coalescing operator (written as ?? ) that allows for easy (short) null checking during assignment: string s = null; var other = s ?? "some default value"; Is there a python equivalent? I know that I can do: s = None other = s if s else "some default value" But is there an even shorter way (where I don't need to repeat s )? other = s or "some default value" Ok,
在C＃中有一个空合并运算符（写为?? ），它允许在赋值过程中进行简单的（短）空检查： string s = null; var other = s ?? "some default value"; 有没有一个python等价物？ 我知道我可以这样做： s = None other = s if s else "some default value" 但有没有更短的路（我不需要重复s ）？ other = s or "some default value" 好吧，必须澄清如何or操作员的工作。 它是一个布尔运算符，因此它在布尔上下文中工作。 如
How do I import other files in Python? How exactly can I import a specific python file like import file.py ? How can I import a folder instead of a specific file? I want to load a Python file dynamically at runtime, based on user input. I want to know how to load just one specific part from the file. For example, in main.py I have: from extra import * Although this gives me all the d
我如何使用Python导入其他文件？ 我怎样才能导入一个特定的Python文件，如import file.py ？ 我如何导入文件夹而不是特定的文件？ 我想根据用户输入在运行时动态加载Python文件。 我想知道如何从文件中加载一个特定的部分。 例如，在main.py我有： from extra import * 尽管这给了我extra.py所有定义，但当我想要的只是一个单一定义时： def gap(): print print 我为什么要添加到import语句才能从extra.p
In my Django model, I've got a very simple model which represents a single occurrence of an event (such as a server alert occurring): class EventOccurrence: event = models.ForeignKey(Event) time = models.DateTimeField() My end goal is to produce a table or graph that shows how many times an event occurred over the past n weeks. So my question has two parts: How can I group_by th
在我的Django模型中，我有一个非常简单的模型，它表示一个事件（例如发生服务器警报）的单个事件： class EventOccurrence: event = models.ForeignKey(Event) time = models.DateTimeField() 我的最终目标是制作一张表格或图表，显示过去n周内事件发生的次数。 所以我的问题有两个部分： 我怎么能group_by time字段的一周？ 我怎样才能“填补”这个group_by的结果，为任何缺失的星期添加一个零值？ 例如，对于
My project is to extract keywords from a given text and tell to which domain that keyword belongs to. For example if the keyword is deadlock it should tell the keyword belongs to operating system. I have done till extracting the keywords part. But I am new to machine learning and I need to know "how to tell the domain/subject of that keyword.". Domains include os, networks, dbms...
我的项目是从给定的文本中提取关键字，并告诉关键字属于哪个域。 例如，如果关键字是死锁，它应该告诉关键字属于操作系统。 我已经完成，直到提取关键字部分。 但我是机器学习的新手，我需要知道“如何说出关键字的域/主题”。 域包括os，网络，dbms ...（计算机科学英语相关术语..）。
I have a custom vocabulary with approx. 1M rows in a SQL table. Each row has a UID and a corresponding phrase that can be many words in length. This table rarely changes. I need tag, extract, chunk or recognize (NER ?) entity phrases in a free-text document against the above mentioned custom vocabulary. So for a phrase found in the free text, I can pull its UID. It would be nice if partia
我有一个自定义词汇约。 SQL表中的1M行。 每行都有一个UID和一个对应的短语，长度可以是多个单词。 这张表很少改变。 我需要在自由文本文档中对上述自定义词汇表进行标记，提取，块或识别（NER？）实体短语。 所以对于在自由文本中找到的短语，我可以拉它的UID。 如果部分匹配以及以不同顺序出现的词组标记将根据某些阈值/算法设置进行标记/提取，那将会很好。 哪一种NLP工具，最好是基于Python的工具，可以在自由文本
I am working in Text Mining and my work is focused on biomedical entities (genes, proteins, drugs and diseases). I would like to share with you some questions. Now, my goal is to find biomedical entities in biomedical text (from Medline) and through of dictionaries of terms, I can identify each entity found with its unique identifier. To store text, dicitionaries and results, I am using Mong
我正在从事文本挖掘工作，我的工作重点是生物医学实体（基因，蛋白质，药物和疾病）。 我想与你分享一些问题。 现在，我的目标是找到生物医学文本（来自Medline）以及词典词典中的生物医学实体，我可以通过其唯一标识符来识别找到的每个实体。 为了存储文本，dicitionaries和结果，我正在使用MongoDB（一个非SQL数据库）。 每个摘要被分割成句子，并且每个句子都存储在一个新记录中（带有令牌，块和词性标签列表）。 为了