distribution | 易学教程

Questions with using boost for generating normal random numbers

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问题 I was hoping to learning how to generate numbers from normal distribution in C++ when I saw This Post. It gives a very good example, but still I am not sure what the & in boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > var_nor(rng, nd); means. What effect will it produce if I did not include this & here? Also, when reading the tutorial on Boost's official website, I found that after generating a distribution object with boost::random::uniform_int_distribution<> dist(1

What does it mean to “break symmetry”? in the context of neural network programming? [duplicate]

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问题 This question already has answers here : Why should weights of Neural Networks be initialized to random numbers? [closed] (9 answers) Closed last year . I have heard a lot about "breaking the symmetry" within the context of neural network programming and initialization. Can somebody please explain what this means? As far as I can tell, it has something to do with neurons performing similarly during forward and backward propagation if the weight matrix is filled with identical values during

Select one element from a list using python following the normal distribution

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问题 I would like to select one element from a list using python following the normal distribution. I have a list, e.g., alist = ['an', 'am', 'apple', 'cool', 'why'] For example, according to the probability density function (PDF) of normal distribution, the 3rd element in the given list should have the largest probability to be chosen.Any suggestions? 回答1: from random import normalvariate def normal_choice(lst, mean=None, stddev=None): if mean is None: # if mean is not specified, use center of

Setting up a Discrete Distribution in C++

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问题 After hours of struggling with this issue, I cannot find any explanations for my error. I want the computer to pick a random number (weighted) between 0 and 120 (inclusive). I have an array, interval[], which holds the numbers from 0 to 120 (inclusive). I have another array, weights[], which holds the probabilities for choosing the ith element in the array. I want to define a probability distribution function for these data. Here is what I tried. I get an error saying that no instance of

The reverse/inverse of the normal distribution function in R

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问题 To plot a normal distribution curve in R we can use: (x = seq(-4,4, length=100)) y = dnorm(x) plot(x, y) If dnorm calculates y as a function of x, does R have a function that calculates x as a function of y? If not what is the best way to approach this? 回答1: I'm not sure if the inverse of the density function is built in -- it's not used nearly as often as the inverse of the cumulative distribution function. I can't think offhand of too many situation where the inverse density function is

分布式限流

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前言本文接着上文应用限流进行讨论。之前谈到的限流方案只能针对于单个 JVM 有效，也就是单机应用。而对于现在普遍的分布式应用也得有一个分布式限流的方案。基于此尝试写了这个组件： https://github.com/crossoverJie/distributed-redis-tool DEMO 以下采用的是 https://github.com/crossoverJie/springboot-cloud 来做演示。在 Order 应用提供的接口中采取了限流。首先是配置了限流工具的 Bean: @Configuration public class RedisLimitConfig { @Value("${redis.limit}") private int limit; @Autowired private JedisConnectionFactory jedisConnectionFactory; @Bean public RedisLimit build() { RedisClusterConnection clusterConnection = jedisConnectionFactory.getClusterConnection(); JedisCluster jedisCluster = (JedisCluster) clusterConnection

How to implement 1D Kalman filter with other distribution?

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问题 I have been through the concept of 1D Kalman filter, but, they mostly concentrate on the equations formed from Gaussian distributions where they used the equations in the picture Gaussian Distribution equations (they can be found in the following links: Pyata 1D Kalman Filter, 1D Kalman Filter, Sensor Fusion). I have several questions: Question 1: How can I form predict and update states with other distributions? (for example, Bradford distribution) I looked into Bradford distribution and

Sample from custom distribution in R

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问题 I have implemented an alternate parameterization of the negative binomial distribution in R, like so (also see here): nb = function(n, l, a){ first = choose((n + a - 1), a-1) second = (l/(l+a))^n third = (a/(l+a))^a return(first*second*third) } Where n is the count, lambda is the mean, and a is the overdispersion term. I would like to draw random samples from this distribution in order to validate my implementation of a negative binomial mixture model, but am not sure how to go about doing

scipy rv_continuous very slow

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问题 I am using a custom function f(x) to define a custom distribution using copy 's rv_continuous class. My code is class my_pdf_gen(rv_continuous): def _pdf(self, x, integral): return f(x)/integral where integral ensure the normalisation. I am able to create an instance of it with my_pdf = my_pdf_gen(my_int,a = a, b = b, name = 'my pdf') with a,b the upper and lower limit of the value's range, and my_int= scipy.integrate.quad(f, a, b)[0] . I am also able to create a random sample of data using

Fitting a linear combination of distributions

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问题 I have 5 arrays (columns of a pandas data frame) and I want calculate the best fit for a linear combination of the distributions to an exponential distribution. for example: a*(d1)+b*(d2)+c*(d3)+d*(d4)+e*(d5)=Y where Y has an exponential distribution (which i know) and a,b,c,d,e are the coefficients to fit. I tried using curve_fit or lmfit python libraries but didn't get how to do it effectively. 回答1: What you're describing is a linear model. Use the package scikit-learn: from sklearn.linear