distribution | 易学教程

Creating a redistributable django app

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问题 In Java world, a common way to distribute a web app is to package it along with Apache Tomcat. What is the appropriate way to achieve something like that with Django (or any WSGI application for that matter)? 回答1: There are two camps for distributing apps in django. It also depends on what you mean by apps . If you mean an apps is in django terms as in 'pluggable apps' that is separated from the project, then it would be best to use setuptools to package those 'pluggable apps'. Many django

Can Shoes.rb Create Self-Contained Applications?

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问题 It's encouraging that Shoes (the Ruby GUI framework) has excellent packaging functionality, but I'm concerned that it doesn't actually 'wrap' itself around created applications. Packaging for OSX outputs a shoes installer and a shoes file in a .app system. Is there any way that shoes could create a .app that contains shoes - that doesn't make you install shoes to run it? I'm working on creating a tiny application - just a start button and a stop button for a subprocess, etc. 回答1: Hey, sorry I

possible distributions and their probabilities after putting identical items into anonymous buckets

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问题 Apologies if the answer to this is readily found elsewhere. My math and stats are weak and thus I don't even know the search terms for what I'm trying to do . . . I have b anonymous indistinguishable buckets into which I'm putting i identical items. I want to know all possible distributions and their probabilities. For example, if I have 3 buckets and 3 items, the answer I want is: [3,0,0] -> 1/9 [2,1,0] -> 6/9 [1,1,1] -> 2/9 Notice that the buckets are anonymous and thus I want identical

How to get parameter arguments from a frozen spicy.stats distribution?

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问题 Frozen Distribution In scipy.stats you can create a frozen distribution that allows the parameterization (shape, location & scale) of the distribution to be permanently set for that instance. For example, you can create an gamma distribution (scipy.stats.gamma) with a , loc and scale parameters and freeze them so they do not have to be passed around every time that distribution is needed. import scipy.stats as stats # Parameters for this particular gamma distribution a, loc, scale = 3.14, 5.0

R's t-distribution says “full precision may not have been achieved”

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问题 I am working with a problem that routinely needs to compute the density of the t distribution rather far in the tails in R. For example, using R's t distribution function, dt(1.424781, 1486, -5) returns [1] 2.75818e-10 . Some of my final outputs (using this density as an input) do not match a reference value from analogous computations performed in MATLAB by my colleague, which I think may be due to imprecision in the t distribution's tails in R. If I compare to MATLAB's t distribution

Algorithm of boost::math::erf

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问题 are there any details available for the algorithm behind the erf-function of boost? The documentation of the module is not very precise. All I found out is that several methods are mixed. For me it looks like variations of Abramowitz and Stegun. Which methods are mixed? How are the methods mixed? What is the complexity of the erf-function (constant time)? Sebastian 回答1: The docs for Boost Math Toolkit has a long list of references, among which Abramowitz and Stegun. The erf-function interface

Sampling a specific age distribution from a dataset

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Suppose I have a dataset with 1,000,000 observations. Variables are age, race, gender. This dataset represents the whole US. How can I draw a sample of 1,000 people from this dataset, given a certain age distribution? E.g. I want this datset with 1000 people distributed like this: 0.3 * Age 0 - 30 0.3 * Age 31 - 50 0.2 * Age 51 - 69 0.2 * Age 70 - 100 Is there a quick way to do it? I already created a sample of 1000 people with the desired age distribution, but how do I combine that now with my original dataset? As an example, this is how I have created the population distribution of Maine:

Need help generating discrete random numbers from distribution

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问题 I searched the site but did not find exactly what I was looking for... I wanted to generate a discrete random number from normal distribution. For example, if I have a range from a minimum of 4 and a maximum of 10 and an average of 7. What code or function call ( Objective C preferred ) would I need to return a number in that range. Naturally, due to normal distribution more numbers returned would center round the average of 7. As a second example, can the bell curve/distribution be skewed

How to distribute multiple versions of my app?

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问题 I want to distribute two versions of my app, the stable branch as well as the current development trunk, using TestFlight. And, if possible, I want to invite the testers only once. Can I have two versions of one app in one TestFlight team? Or maybe two app with different namens? Or can I create a second team and link it to the first one or copy the testers over? 回答1: Unfortunately I think there is no nice way to do that. Your options are: Two different TestFlight teams. You'll have to invite

Visualize histograms in seaborn

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I have a code that create 16 histograms. My problems are: I think the code is too much repetative and that there are ways to write it shorter. 2.I have 13 fields to create histograms, and as 13 is prime number, I face a problem how to show all of them nicely but without blank plots. I want to show the distribution but I get only 1 bar, even though I have change dit to 0, 100 and 500 (I have more than 1000 observations). some columns are float and have too many 0 after the dot and I can't change it. This is my code: f, axes = plt.subplots(4, 4, figsize=(20,20), sharex=True) sns.distplot(data[