uniform-distribution

问题 In my researches, I couldn't find any examples related to a relational value in piecewise functions. Although I received the lower and upper values with the uni_dis method, i'm stuck in the process of transferring these values to the piecewise function. What's the reason? from sympy import Symbol, Piecewise import sympy as sym import sympy.plotting as syp import math a = Symbol('a') b = Symbol('b') x = Symbol('x') function = 1 / abs(a-b) def uni_dis(lower, upper): if lower > upper: lower,

来源： https://stackoverflow.com/questions/35117014/generating-a-random-uniformly-distributed-real-number-in-c

来源： https://stackoverflow.com/questions/44328530/how-to-get-a-uniform-distribution-in-a-range-r1-r2-in-pytorch

问题 Simple test code: pop = numpy.arange(20) rng = numpy.random.default_rng(1) rng.choice(pop,p=numpy.repeat(1/len(pop),len(pop))) # yields 10 rng = numpy.random.default_rng(1) rng.choice(pop) # yields 9 The numpy documentation says: The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a. I don't know of any other way to create a uniform distribution, but numpy.repeat(1/len(pop),len(pop)) . Is numpy using something else? Why

问题 I need to add quantization noise to my input data. I read often these kinds of noises are modeled as noise with uniform distribution. I have an encoding/decoding network implemented with Keras (input data is time series raw data), there is a layer implemented in Keras with which you can add Gaussian noise (GaussianNoise layer), can I use this layer to create uniform noise? If not, are there other implemented layers that I can use? 回答1: You can create your own layer as such, import tensorflow

问题 I am trying to find the maximum likelihood estimators a_hat and b_hat for a given uniform distribution X ~ UNIF(1,3) using R. Below is my code and its output: ##Example: Uniform Distribution x<-runif(100,1,3) n<-length(x) ll<-function(a,b){ -sum(1/(b-a)^n,log=TRUE) } m0<-mle2(ll,start=list(a=1,b=2)) summary(m0) > summary(m0) Maximum likelihood estimation Call: mle2(minuslogl = ll, start = list(a = 1, b = 2)) Coefficients: Estimate Std. Error z value Pr(z) a 1.5159 NA NA NA b 1.4841 NA NA NA

问题 I am trying to find the maximum likelihood estimators a_hat and b_hat for a given uniform distribution X ~ UNIF(1,3) using R. Below is my code and its output: ##Example: Uniform Distribution x<-runif(100,1,3) n<-length(x) ll<-function(a,b){ -sum(1/(b-a)^n,log=TRUE) } m0<-mle2(ll,start=list(a=1,b=2)) summary(m0) > summary(m0) Maximum likelihood estimation Call: mle2(minuslogl = ll, start = list(a = 1, b = 2)) Coefficients: Estimate Std. Error z value Pr(z) a 1.5159 NA NA NA b 1.4841 NA NA NA

问题 I am trying to find the maximum likelihood estimators a_hat and b_hat for a given uniform distribution X ~ UNIF(1,3) using R. Below is my code and its output: ##Example: Uniform Distribution x<-runif(100,1,3) n<-length(x) ll<-function(a,b){ -sum(1/(b-a)^n,log=TRUE) } m0<-mle2(ll,start=list(a=1,b=2)) summary(m0) > summary(m0) Maximum likelihood estimation Call: mle2(minuslogl = ll, start = list(a = 1, b = 2)) Coefficients: Estimate Std. Error z value Pr(z) a 1.5159 NA NA NA b 1.4841 NA NA NA

问题 I'm trying to convert this line of matlab in C++: rp = randperm(p); Following the randperm documentation: randperm uses the same random number generator as rand And in rand page: rand returns a single uniformly distributed random number So rand follows an uniform distribution. My C++ code is based on: std::random_device rd; std::mt19937 g(rd()); std::shuffle(... , ... ,g); My question is: the code above follows an uniform distribution? If not, how to do so? 回答1: The different classes from the

问题 I need to reproduce the normalized density p(x) below, but the code given does not generate a normalized PDF. clc, clear % Create three distribution objects with different parameters pd1 = makedist('Uniform','lower',2,'upper',6); pd2 = makedist('Uniform','lower',2,'upper',4); pd3 = makedist('Uniform','lower',5,'upper',6); % Compute the pdfs x = -1:.01:9; pdf1 = pdf(pd1,x); pdf2 = pdf(pd2,x); pdf3 = pdf(pd3,x); % Sum of uniforms pdf = (pdf1 + pdf2 + pdf3); % Plot the pdfs figure; stairs(x,pdf,