# Updating a vector inside a for loop crashes the script

**URL:** <https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331>\
**Category:** General Usage\
**Created:** [April 21, 2017, 7:20pm UTC](https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331 "2017-04-21T19:20:05Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![DWSchulze](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dwschulze/32/35968_2.png) [@DWSchulze](https://discourse.julialang.org/u/DWSchulze)\
**Post date:** [April 21, 2017, 7:20pm UTC](https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331/1 "2017-04-21T19:20:05Z")

</div>

I converted [this .m file](http://openclassroom.stanford.edu/MainFolder/courses/MachineLearning/exercises/ex4materials/ex4.m) into Julia. When a vector gets updated inside a for loop and Julia crashes with this (incorrect) error message:

> ERROR: LoadError: MethodError: no method matching diagm(::Array{Float64,2})  
> Closest candidates are:  
> diagm(::Union{BitArray{1},BitArray{2}}) at linalg/bitarray.jl:90  
> diagm{Tv,Ti}(::SparseMatrixCSC{Tv,Ti}) at sparse/sparsematrix.jl:3571  
> diagm{T}(::AbstractArray{T,1}) at linalg/dense.jl:121  
> …  
> in macro expansion; at /home/dean/src/ml/logistic.regression/ng/ex.04/ex4.jl:27 [inlined]  
> in anonymous at ./:?  
> in include\_from\_node1(::String) at ./loading.jl:488  
> while loading /home/dean/src/ml/logistic.regression/ng/ex.04/ex4.jl, in expression starting on line 17

The line that causes the problem is

> theta = theta - H\grad

If I comment this line out the code runs. If I remove the loop and just execute the code in the loop once it runs. It’s not a problem with the diagm() function

I’m using Julia 0.5.1.

Have I done something wrong, or is this a bug? Here’s the code and data files:

> x = readdlm(“ex4x.dat”)  
> y = readdlm(“ex4y.dat”)  
> xWithInt = hcat(ones(80), x)  
> nrows,ncols = size(xWithInt)  
> theta = zeros(ncols)  
> g(z) = 1.0 ./ (1.0 + exp(-z))

> MAX\_ITR = 7  
> J = zeros(MAX\_ITR)

> for i = 1:MAX\_ITR

> # hypothesis
> 
> z = xWithInt \* theta  
> h = g(z)

> grad = (1/nrows) .\* xWithInt’ \* (h-y)  
> H = (1/nrows) .\* xWithInt’ \* diagm(h) \* diagm(1-h) \* xWithInt

> # Calculate J (for testing convergence)
> 
> J[i] =(1/nrows)_sum(-y ._ log(h) - (1-y) .\* log(1-h))  
> #J[1] =(1/nrows)_sum(-y ._ log(h) - (1-y) .\* log(1-h))

> # backslash operator is left division
> 
> theta = theta - H\grad

> end

ex4x.dat

> 5.5500000e+01 6.9500000e+01  
> 4.1000000e+01 8.1500000e+01  
> 5.3500000e+01 8.6000000e+01  
> 4.6000000e+01 8.4000000e+01  
> 4.1000000e+01 7.3500000e+01  
> 5.1500000e+01 6.9000000e+01  
> 5.1000000e+01 6.2500000e+01  
> 4.2000000e+01 7.5000000e+01  
> 5.3500000e+01 8.3000000e+01  
> 5.7500000e+01 7.1000000e+01  
> 4.2500000e+01 7.2500000e+01  
> 4.1000000e+01 8.0000000e+01  
> 4.6000000e+01 8.2000000e+01  
> 4.6000000e+01 6.0500000e+01  
> 4.9500000e+01 7.6000000e+01  
> 4.1000000e+01 7.6000000e+01  
> 4.8500000e+01 7.2500000e+01  
> 5.1500000e+01 8.2500000e+01  
> 4.4500000e+01 7.0500000e+01  
> 4.4000000e+01 6.6000000e+01  
> 3.3000000e+01 7.6500000e+01  
> 3.3500000e+01 7.8500000e+01  
> 3.1500000e+01 7.2000000e+01  
> 3.3000000e+01 8.1500000e+01  
> 4.2000000e+01 5.9500000e+01  
> 3.0000000e+01 6.4000000e+01  
> 6.1000000e+01 4.5000000e+01  
> 4.9000000e+01 7.9000000e+01  
> 2.6500000e+01 6.4500000e+01  
> 3.4000000e+01 7.1500000e+01  
> 4.2000000e+01 8.3500000e+01  
> 2.9500000e+01 7.4500000e+01  
> 3.9500000e+01 7.0000000e+01  
> 5.1500000e+01 6.6000000e+01  
> 4.1500000e+01 7.1500000e+01  
> 4.2500000e+01 7.9500000e+01  
> 3.5000000e+01 5.9500000e+01  
> 3.8500000e+01 7.3500000e+01  
> 3.2000000e+01 8.1500000e+01  
> 4.6000000e+01 6.0500000e+01  
> 3.6500000e+01 5.3000000e+01  
> 3.6500000e+01 5.3500000e+01  
> 2.4000000e+01 6.0500000e+01  
> 1.9000000e+01 5.7500000e+01\> 5.5500000e+01 6.9500000e+01  
> 4.1000000e+01 8.1500000e+01  
> 5.3500000e+01 8.6000000e+01  
> 4.6000000e+01 8.4000000e+01  
> 4.1000000e+01 7.3500000e+01  
> 5.1500000e+01 6.9000000e+01  
> 5.1000000e+01 6.2500000e+01  
> 4.2000000e+01 7.5000000e+01  
> 5.3500000e+01 8.3000000e+01  
> 5.7500000e+01 7.1000000e+01  
> 4.2500000e+01 7.2500000e+01  
> 4.1000000e+01 8.0000000e+01  
> 4.6000000e+01 8.2000000e+01  
> 4.6000000e+01 6.0500000e+01  
> 4.9500000e+01 7.6000000e+01  
> 4.1000000e+01 7.6000000e+01  
> 4.8500000e+01 7.2500000e+01  
> 5.1500000e+01 8.2500000e+01  
> 4.4500000e+01 7.0500000e+01  
> 4.4000000e+01 6.6000000e+01  
> 3.3000000e+01 7.6500000e+01  
> 3.3500000e+01 7.8500000e+01  
> 3.1500000e+01 7.2000000e+01  
> 3.3000000e+01 8.1500000e+01  
> 4.2000000e+01 5.9500000e+01  
> 3.0000000e+01 6.4000000e+01  
> 6.1000000e+01 4.5000000e+01  
> 4.9000000e+01 7.9000000e+01  
> 2.6500000e+01 6.4500000e+01  
> 3.4000000e+01 7.1500000e+01  
> 4.2000000e+01 8.3500000e+01  
> 2.9500000e+01 7.4500000e+01  
> 3.9500000e+01 7.0000000e+01  
> 5.1500000e+01 6.6000000e+01  
> 4.1500000e+01 7.1500000e+01  
> 4.2500000e+01 7.9500000e+01  
> 3.5000000e+01 5.9500000e+01  
> 3.8500000e+01 7.3500000e+01  
> 3.2000000e+01 8.1500000e+01  
> 4.6000000e+01 6.0500000e+01  
> 3.6500000e+01 5.3000000e+01  
> 3.6500000e+01 5.3500000e+01  
> 2.4000000e+01 6.0500000e+01  
> 1.9000000e+01 5.7500000e+01  
> 3.4500000e+01 6.0000000e+01  
> 3.7500000e+01 6.4500000e+01  
> 3.5500000e+01 5.1000000e+01  
> 3.7000000e+01 5.0500000e+01  
> 2.1500000e+01 4.2000000e+01  
> 3.5500000e+01 5.8500000e+01  
> 2.6500000e+01 6.8500000e+01  
> 2.6500000e+01 5.5500000e+01  
> 1.8500000e+01 6.7000000e+01  
> 4.0000000e+01 6.7000000e+01  
> 3.2500000e+01 7.1500000e+01  
> 3.9000000e+01 7.1500000e+01  
> 4.3000000e+01 5.5500000e+01  
> 2.2000000e+01 5.4000000e+01  
> 3.6000000e+01 6.2500000e+01  
> 3.1000000e+01 5.5500000e+01  
> 3.8500000e+01 7.6000000e+01  
> 4.0000000e+01 7.5000000e+01  
> 3.7500000e+01 6.3000000e+01  
> 2.4500000e+01 5.8000000e+01  
> 3.0000000e+01 6.7000000e+01  
> 3.3000000e+01 5.6000000e+01  
> 5.6500000e+01 6.1000000e+01  
> 4.1000000e+01 5.7000000e+01  
> 4.9500000e+01 6.3000000e+01  
> 3.4500000e+01 7.2500000e+01  
> 3.2500000e+01 6.9000000e+01  
> 3.6000000e+01 7.3000000e+01  
> 2.7000000e+01 5.3500000e+01  
> 4.1000000e+01 6.3500000e+01  
> 2.9500000e+01 5.2500000e+01  
> 2.0000000e+01 6.5500000e+01  
> 3.8000000e+01 6.5000000e+01  
> 1.8500000e+01 7.4500000e+01  
> 1.6000000e+01 7.2500000e+01  
> 3.3500000e+01 6.8000000e+01  
> 3.4500000e+01 6.0000000e+01  
> 3.7500000e+01 6.4500000e+01  
> 3.5500000e+01 5.1000000e+01  
> 3.7000000e+01 5.0500000e+01  
> 2.1500000e+01 4.2000000e+01  
> 3.5500000e+01 5.8500000e+01  
> 2.6500000e+01 6.8500000e+01  
> 2.6500000e+01 5.5500000e+01  
> 1.8500000e+01 6.7000000e+01  
> 4.0000000e+01 6.7000000e+01  
> 3.2500000e+01 7.1500000e+01  
> 3.9000000e+01 7.1500000e+01  
> 4.3000000e+01 5.5500000e+01  
> 2.2000000e+01 5.4000000e+01  
> 3.6000000e+01 6.2500000e+01  
> 3.1000000e+01 5.5500000e+01  
> 3.8500000e+01 7.6000000e+01  
> 4.0000000e+01 7.5000000e+01  
> 3.7500000e+01 6.3000000e+01  
> 2.4500000e+01 5.8000000e+01  
> 3.0000000e+01 6.7000000e+01  
> 3.3000000e+01 5.6000000e+01  
> 5.6500000e+01 6.1000000e+01  
> 4.1000000e+01 5.7000000e+01  
> 4.9500000e+01 6.3000000e+01  
> 3.4500000e+01 7.2500000e+01  
> 3.2500000e+01 6.9000000e+01  
> 3.6000000e+01 7.3000000e+01  
> 2.7000000e+01 5.3500000e+01  
> 4.1000000e+01 6.3500000e+01  
> 2.9500000e+01 5.2500000e+01  
> 2.0000000e+01 6.5500000e+01  
> 3.8000000e+01 6.5000000e+01  
> 1.8500000e+01 7.4500000e+01  
> 1.6000000e+01 7.2500000e+01\> 5.5500000e+01 6.9500000e+01  
> 4.1000000e+01 8.1500000e+01  
> 5.3500000e+01 8.6000000e+01  
> 4.6000000e+01 8.4000000e+01  
> 4.1000000e+01 7.3500000e+01  
> 5.1500000e+01 6.9000000e+01  
> 5.1000000e+01 6.2500000e+01  
> 4.2000000e+01 7.5000000e+01  
> 5.3500000e+01 8.3000000e+01  
> 5.7500000e+01 7.1000000e+01  
> 4.2500000e+01 7.2500000e+01  
> 4.1000000e+01 8.0000000e+01  
> 4.6000000e+01 8.2000000e+01  
> 4.6000000e+01 6.0500000e+01  
> 4.9500000e+01 7.6000000e+01  
> 4.1000000e+01 7.6000000e+01  
> 4.8500000e+01 7.2500000e+01  
> 5.1500000e+01 8.2500000e+01  
> 4.4500000e+01 7.0500000e+01  
> 4.4000000e+01 6.6000000e+01  
> 3.3000000e+01 7.6500000e+01  
> 3.3500000e+01 7.8500000e+01  
> 3.1500000e+01 7.2000000e+01  
> 3.3000000e+01 8.1500000e+01  
> 4.2000000e+01 5.9500000e+01  
> 3.0000000e+01 6.4000000e+01  
> 6.1000000e+01 4.5000000e+01  
> 4.9000000e+01 7.9000000e+01  
> 2.6500000e+01 6.4500000e+01  
> 3.4000000e+01 7.1500000e+01  
> 4.2000000e+01 8.3500000e+01  
> 2.9500000e+01 7.4500000e+01  
> 3.9500000e+01 7.0000000e+01  
> 5.1500000e+01 6.6000000e+01  
> 4.1500000e+01 7.1500000e+01  
> 4.2500000e+01 7.9500000e+01  
> 3.5000000e+01 5.9500000e+01  
> 3.8500000e+01 7.3500000e+01  
> 3.2000000e+01 8.1500000e+01  
> 4.6000000e+01 6.0500000e+01  
> 3.6500000e+01 5.3000000e+01  
> 3.6500000e+01 5.3500000e+01  
> 2.4000000e+01 6.0500000e+01  
> 1.9000000e+01 5.7500000e+01  
> 3.4500000e+01 6.0000000e+01  
> 3.7500000e+01 6.4500000e+01  
> 3.5500000e+01 5.1000000e+01  
> 3.7000000e+01 5.0500000e+01  
> 2.1500000e+01 4.2000000e+01  
> 3.5500000e+01 5.8500000e+01  
> 2.6500000e+01 6.8500000e+01  
> 2.6500000e+01 5.5500000e+01  
> 1.8500000e+01 6.7000000e+01  
> 4.0000000e+01 6.7000000e+01  
> 3.2500000e+01 7.1500000e+01  
> 3.9000000e+01 7.1500000e+01  
> 4.3000000e+01 5.5500000e+01  
> 2.2000000e+01 5.4000000e+01  
> 3.6000000e+01 6.2500000e+01  
> 3.1000000e+01 5.5500000e+01  
> 3.8500000e+01 7.6000000e+01  
> 4.0000000e+01 7.5000000e+01  
> 3.7500000e+01 6.3000000e+01  
> 2.4500000e+01 5.8000000e+01  
> 3.0000000e+01 6.7000000e+01  
> 3.3000000e+01 5.6000000e+01  
> 5.6500000e+01 6.1000000e+01  
> 4.1000000e+01 5.7000000e+01  
> 4.9500000e+01 6.3000000e+01  
> 3.4500000e+01 7.2500000e+01  
> 3.2500000e+01 6.9000000e+01  
> 3.6000000e+01 7.3000000e+01  
> 2.7000000e+01 5.3500000e+01  
> 4.1000000e+01 6.3500000e+01  
> 2.9500000e+01 5.2500000e+01  
> 2.0000000e+01 6.5500000e+01  
> 3.8000000e+01 6.5000000e+01  
> 1.8500000e+01 7.4500000e+01  
> 1.6000000e+01 7.2500000e+01  
> 3.3500000e+01 6.8000000e+01  
> 3.3500000e+01 6.8000000e+01

ex4y.dat

> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 1.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00  
> 0.0000000e+00

---

<div class="post-metadata">

**Author:** ![Michael\_Eastwood](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/michael_eastwood/32/669_2.png) [@Michael\_Eastwood](https://discourse.julialang.org/u/Michael_Eastwood)\
**Post date:** [April 21, 2017, 8:55pm UTC](https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331/2 "2017-04-21T20:55:51Z")

</div>

I haven’t gone through your code in detail, but I think the error message is telling you that at some point a variable that you think is a vector has become a matrix. You should double check the dimensions of each of your variables to make sure it matches your expectation.

---

<div class="post-metadata">

**Author:** ![DWSchulze](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dwschulze/32/35968_2.png) [@DWSchulze](https://discourse.julialang.org/u/DWSchulze)\
**Post date:** [April 21, 2017, 9:34pm UTC](https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331/3 "2017-04-21T21:34:26Z")

</div>

You’re correct. My problem is the line

> y = readdlm(“ex4y.dat”)

readdlm creates a 3x1 Array of 80 elements instead of an 80-element array (vector). This coerces other vectors into being 3 x 1 arrays instead of vectors. The fix is to use

> y = vec(readdlm(“ex4y.dat”))

which forces y to be a vector instead of a 3 x 1 Array.

The difference between a 3x1 matrix and a vector is subtle but important. Do you know if it is documented anywhere?

Thanks for your help.

---

<div class="post-metadata">

**Author:** ![Michael\_Eastwood](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/michael_eastwood/32/669_2.png) [@Michael\_Eastwood](https://discourse.julialang.org/u/Michael_Eastwood)\
**Post date:** [April 22, 2017, 5:28am UTC](https://discourse.julialang.org/t/updating-a-vector-inside-a-for-loop-crashes-the-script/3331/4 "2017-04-22T05:28:43Z")

</div>

The documentation for `readdlm` is here: [https://docs.julialang.org/en/stable/stdlib/io-network/#Base.readdlm](https://docs.julialang.org/en/stable/stdlib/io-network/#Base.readdlm)

I think it could definitely be made more clear that `readdlm` always returns a `Matrix` even when there is only one column. If you’d like to improve the documentation, the relevant file to change is:

[https://github.com/JuliaLang/julia/blob/5482f911c3d2f8c0dfd7102932cfece31fc4fd24/base/datafmt.jl#L41-L113](https://github.com/JuliaLang/julia/blob/5482f911c3d2f8c0dfd7102932cfece31fc4fd24/base/datafmt.jl#L41-L113)

I think a pull request that improves the wording here would be well received.