# DataInterpolations.jl - defining function without broadcasting syntax

**URL:** <https://discourse.julialang.org/t/datainterpolations-jl-defining-function-without-broadcasting-syntax/83366>\
**Category:** General Usage\
**Tags:** interpolations, sciml\
**Created:** [June 26, 2022, 6:27pm UTC](https://discourse.julialang.org/t/datainterpolations-jl-defining-function-without-broadcasting-syntax/83366 "2022-06-26T18:27:57Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Alec\_Loudenback](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/alec_loudenback/32/278_2.png) [@Alec\_Loudenback](https://discourse.julialang.org/u/Alec_Loudenback)\
**Post date:** [June 26, 2022, 6:27pm UTC](https://discourse.julialang.org/t/datainterpolations-jl-defining-function-without-broadcasting-syntax/83366/1 "2022-06-26T18:27:57Z")

</div>

I’m trying to fit a curve using [DataInterpolations.jl](https://github.com/PumasAI/DataInterpolations.jl) and want to fit a curve with some fixed and variable parameters. The fixed number of terms can vary, which is why I’m starting with a sum of a generator (below, take `c` being `[0.2,0.4,0.6]` as the fixed parameters). I’m trying to figure out how to get this to work without needing to write out the full expression for different lengths of fixed `c` vectors.

```julia
m(x,params) = sum(params[i+1]*exp(-c*x) for (i,c) in enumerate([0.2,0.4,0.6])) + params[1]
rates =[0.01, 0.01, 0.03, 0.05, 0.07, 0.16, 0.35, 0.92, 1.40, 1.74, 2.31, 2.41] ./ 100
mats = [1/12, 2/12, 3/12, 6/12, 1, 2, 3, 5, 7, 10, 20, 30]

```

Results in an error:

```julia-repl
julia> Curvefit(rates,mats,m,ones(4),LBFGS())
ERROR: MethodError: no method matching exp(::Vector{Float64})

```

If I write out the whole function explicitly with broadcasting syntax it works:

```julia
m(x, params) = @. params[1] + params[2]*exp(-0.2*x) + params[3] * exp(-0.4*x) + params[4] * exp(-0.8*x)

```

```julia-repl
julia> A = Curvefit(rates,mats,m,ones(4),LBFGS())
24-element DataInterpolations.CurvefitCache{Vector{Float64}, Vector{Float64}, typeof(m), Vector{Float64}, Nothing, Nothing, LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, Vector{Float64}, true, Float64}:
  0.0001
  0.0001
  0.0003
  0.0005
  0.0007000000000000001
  0.0016
  0.0034999999999999996
  ⋮
  2.0
  3.0
  5.0
  7.0
 10.0
 20.0
 30.0

```

I tried writing a function that would return an array like I think the broadcasting version would do, but got an error as well:

```julia
m(x,params) = [sum(params[i+1]*exp(-c*x) for (i,c) in enumerate([0.2,0.4,0.6])) + params[1] for xi in x]

```

And the assocated error:

```julia-repl
julia> Curvefit(rates,mats,m,ones(4),LBFGS())
ERROR: MethodError: no method matching exp(::Vector{Float64})

```

Are there suggested ways to do this?

---

<div class="post-metadata">

**Author:** ![Alec\_Loudenback](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/alec_loudenback/32/278_2.png) [@Alec\_Loudenback](https://discourse.julialang.org/u/Alec_Loudenback)\
**Post date:** [June 27, 2022, 1:35am UTC](https://discourse.julialang.org/t/datainterpolations-jl-defining-function-without-broadcasting-syntax/83366/2 "2022-06-27T01:35:05Z")

</div>

This worked:

```julia
m(x,params) = sum(params[i+1]*exp.(-c.*x) for (i,c) in enumerate([0.2,0.4,0.6])) .+ params[1]

```