# Generate line function data with noise
**URL:** https://discourse.julialang.org/t/generate-line-function-data-with-noise/88548
**Category:** General Usage
**Created:** [October 11, 2022, 2:32am UTC](https://discourse.julialang.org/t/generate-line-function-data-with-noise/88548 "2022-10-11T02:32:44Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@JosieG](https://discourse.julialang.org/u/JosieG)
#### Post date: [October 11, 2022, 2:32am UTC](https://discourse.julialang.org/t/generate-line-function-data-with-noise/88548/1 "2022-10-11T02:32:44Z")
I would like to assess one dimension reduction method on simulation data. Besides totally random data, I want to simulate a partial random dataset. Right now I can think of one method to generate such data
```julia
rand2 = collect(1:85)
for m in 1:20
v = []
for i in 1:85
ai = i/m
bi = rand(Float64)
pi = i/85
push!(v, ai+bi*pi)
end
rand2=hcat(rand2,v)
end
rand2 =rand2[:, (1:end) .!= 1]
```
Is there a better way to generate partially random data? Like data points follow line functions but there are noises. Thanks in advance!
---
### Author:  [@uniment](https://discourse.julialang.org/u/uniment)
#### Post date: [October 11, 2022, 3:20am UTC](https://discourse.julialang.org/t/generate-line-function-data-with-noise/88548/2 "2022-10-11T03:20:17Z")
List comprehensions provide this functionality quite nicely. You can easily generate a 2-dimensional matrix by specifying two dimensions to loop over (iterating over the `i`th row and `j`th column):
```julia
rand2 = [i*(1/j+rand()/85) for i=1:85, j=1:20]
```
This doesn’t work so well when one value in the matrix depends on the value of another, but that’s not the case here.