normal-distribution

问题 I'd like to get an NxM matrix where numbers in each row are random samples generated from different normal distributions(same mean but different standard deviations). The following code works: import numpy as np mean = 0.0 # same mean stds = [1.0, 2.0, 3.0] # different stds matrix = np.random.random((3,10)) for i,std in enumerate(stds): matrix[i] = np.random.normal(mean, std, matrix.shape[1]) However, this code is not quite efficient as there is a for loop involved. Is there a faster way to

问题 I have a vector, in R, with 1521298 points, which have to be tested for normality. I chose the Shapiro-Wilk test, but the R function shapiro.test() says: Error in shapiro.test(z_scores) : sample size must be between 3 and 5000 Do you know any other function to test it or how to circumvent this issue? 回答1: Shapiro test cannot done using more than 5.000 records. You can try to do the shapiro test using only the first 5.000 samples. IF it can help you, use the code like this: shapiro.test

问题 I've used random.normal() in the past to generate a single number who, if called multiple times, in aggregate would create a bell curve distribution. What I'm trying to do now is to create the opposite / inverse, where the distribution is biased towards the extremes within a range? There are built in functions in excel that seem to do what I want. Is there a way to do it in python? Thank you 回答1: It appears you want a distribution with an "upside-down bell curve" compared to the normal

问题 I've used random.normal() in the past to generate a single number who, if called multiple times, in aggregate would create a bell curve distribution. What I'm trying to do now is to create the opposite / inverse, where the distribution is biased towards the extremes within a range? There are built in functions in excel that seem to do what I want. Is there a way to do it in python? Thank you 回答1: It appears you want a distribution with an "upside-down bell curve" compared to the normal

问题 I have this ggplot ggplot(data = ph, aes(x = index1)) + geom_density() and I would like to add a normal distribution with the same mean (= 2.71) and standard Deviation (= 0.61) . I created the normal distribution with: nd1 <- rnorm(n = 100000, mean = 2.71), sd = 0.61) nd1plot <- qplot(nd1, geom = "density") + theme_classic() + ggtitle("Normalverteilung") But now I don't know how to add it to my existing plot. Can anyone help me with this issue? 回答1: ggplot2 has stat_function() for this kind

问题 I have this ggplot ggplot(data = ph, aes(x = index1)) + geom_density() and I would like to add a normal distribution with the same mean (= 2.71) and standard Deviation (= 0.61) . I created the normal distribution with: nd1 <- rnorm(n = 100000, mean = 2.71), sd = 0.61) nd1plot <- qplot(nd1, geom = "density") + theme_classic() + ggtitle("Normalverteilung") But now I don't know how to add it to my existing plot. Can anyone help me with this issue? 回答1: ggplot2 has stat_function() for this kind

问题 I have this ggplot ggplot(data = ph, aes(x = index1)) + geom_density() and I would like to add a normal distribution with the same mean (= 2.71) and standard Deviation (= 0.61) . I created the normal distribution with: nd1 <- rnorm(n = 100000, mean = 2.71), sd = 0.61) nd1plot <- qplot(nd1, geom = "density") + theme_classic() + ggtitle("Normalverteilung") But now I don't know how to add it to my existing plot. Can anyone help me with this issue? 回答1: ggplot2 has stat_function() for this kind

来源： https://stackoverflow.com/questions/61091824/scipy-normal-distribution-with-scale-greater-and-less-than-1

来源： https://stackoverflow.com/questions/51136581/how-to-create-a-normal-distribution-in-pytorch

来源： https://stackoverflow.com/questions/51136581/how-to-create-a-normal-distribution-in-pytorch