frequency-distribution

问题 I have been trying to optimize the following function, but without success: parametros <- data.frame(ap=c(11.1,7.07,6.3,4.75,4,3.35), fx=c(41.2012,39.3732,25.2912,10.3455,1.2253,0.4017)) xm<-11.2 fxcalc <- function(s,t){(1-(1-(parametros$ap/xm)^(s))^t)*100} suma <- function(s,t){(parametros$fx-fxcalc(s,t))^2} func <- function(s,t){sum(suma(s,t))} Being "func()" the function I am trying to minimize for "s" and "t". Apparently the function "optim()" wouldn't work with more than one variable.

问题 I have been trying to optimize the following function, but without success: parametros <- data.frame(ap=c(11.1,7.07,6.3,4.75,4,3.35), fx=c(41.2012,39.3732,25.2912,10.3455,1.2253,0.4017)) xm<-11.2 fxcalc <- function(s,t){(1-(1-(parametros$ap/xm)^(s))^t)*100} suma <- function(s,t){(parametros$fx-fxcalc(s,t))^2} func <- function(s,t){sum(suma(s,t))} Being "func()" the function I am trying to minimize for "s" and "t". Apparently the function "optim()" wouldn't work with more than one variable.

问题 I am trying to calculate the standard deviation of a set of values in PowerBI and I am stuck. There are two columns in a table (days and count). This is a frequency distribution of a transportation lane. Days goes from 1 to 100, count is the number of shipments that took those number of days. The formula to calculate the standard deviation of a frequency distribution is pretty straight forward: sqrt(sum(fx * (x - avgx)^2))/sum(fx)) But the Dax is giving me a massive headache. Any help would

问题 Is there a fast/simple way to calculate the frequency distribution of a .Net collection using Linq or otherwise? For example: An arbitrarily long List contains many repetitions. What's a clever way of walking the list and counting/tracking repetitions? 回答1: The easiest way is to use a hashmap and either use the value as the key and increment the value, or pick a bucket size (bucket 1 = 1 - 10, bucket 2 = 11 - 20, etc), and increment each bucket by the value. Then you can go through and

问题 My goal is to obtain a plot with the spatial frequencies of an image - kind of like doing a fourier transformation on it. I don't care about the position on the image of features with the frequency f (for instance); I'd just like to have a graphic which tells me how much of every frequency I have (the amplitude for a frequency band could be represented by the sum of contrasts with that frequency). I am trying to do this via the numpy.fft.fft2 function. Here is a link to a minimal example

问题 I need to calculate the frequency of individuals by age and marital status so normally I'd use: table(age, marital_status) However each individual has a different weight after the sampling of the data. How do I incorporate this into my frequency table? 回答1: You can use function svytable from package survey , or wtd.table from rgrs . EDIT : rgrs is now called questionr : df <- data.frame(var = c("A", "A", "B", "B"), wt = c(30, 10, 20, 40)) library(questionr) wtd.table(x = df$var, weights = df

问题 How can I, in R calculate the overall variance and the variance for each group from a dataset that looks like this (for example): Group Count Value A 3 5 A 2 8 B 1 11 B 3 15 I know to calculate the variance as a whole, ignoring the groups I would do: var(rep(x$Value, x$Count)), but how do I automatically calculate the variance for each group accounting for the frequency? E.g., the variance for group A, group B, etc.,.. I would like my output to have the following headers: Group, Total Count,

问题 I need some help. I have the following table: country_code=c(1,1,1,1,1,1,2,2,2,2,2,2) target=c('V1','V1','V2','V2','V3','V3','V1','V1','V2','V2','V3','V3') M1=c('X7','X7','X14','X14','X8','X8','X29','X22','X2','X22','X22','X22') M2=c('X1','X1','X17','X11','X21','X21','X1','X29','X8','X18','X24','X24') M3=c('NA','NA','NA','X1','NA','NA','NA','NA','NA','NA','NA','NA') CountofRun=c(1,2,1,2,1,2,1,2,1,2,1,2) df<-data.frame(country_code,target,M1,M2,M3,CountofRun) and I would like to get a

问题 From a function that i run in matlab i get a 225x400 matrix. I want to count the frequency of each element in this matrix, meaning that i need to calculate how many times each elements appears on the the matrix. My matrix name is "Idiff" I am using: B=unique(Idiff); to find the unique elements in the Idiff matrix. I receive a column of 1138 elements, so i understand that these elements are unique and all the other elements in the Idiff matrix are these elements repeated. Now i try to count

问题 I'd like to generate random numbers that follow a dropping linear frequency distribution, take n=1-x for an example. The numpy library however seems to offer only more complex distributions. 回答1: So, it turns out you can totally use random.triangular(0,1,0) for this. See documentation here: https://docs.python.org/2/library/random.html random.triangular(low, high, mode) Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds.