Yes, sorry, seems I'm still on vacation... ;) Ruby parses this another way
so I confused the two languages.
Actually, by changing my model in the LsqFit example I now also get the
multivariate curve fitting to work; code below. I guess in general
convergence might be a problem unless the initial guess is relatively close.
If there are faster/simpler ways to accomplish this (with LsqFit) please
share.
Regards, Robert
using LsqFit
# Try to use curve_fit for this multivariate model:
mvmodel(x, p) = begin
n = abs(x[1,:]) .^ p[2]
d = abs(x[2,:]) .^ p[3]
p[1] .* (n ./ d)
end
N = 10
M = 2
X = randn(M, N)
Params = [1.0, 2.0, 2.0] # Actual params that we are looking for
errors = 0.01*randn(N)
# Dependent var with small error term
Y = mvmodel(X, Params)[:] .+ errors
# We need a vector to submit to curve_fit:
xv = X[:] # reshape(X, (N*M, 1))[:]
# Reshape to get back the original matrix.
orig_matrix(xv, M) = reshape(xv, (M, int(length(xv)/M)))
# The model function we supply to curve_fit needs to take a vector...
model(xv, p) = mvmodel(orig_matrix(xv, M), p)[:]
fit = curve_fit(model, xv, Y, [0.5, 1.0, 1.0])
julia> fit.param
3-element Array{Float64,1}:
0.999694
1.99672
1.99926
On Tue, Aug 12, 2014 at 3:15 PM, Hans W Borchers <[email protected]>
wrote:
> Robert,
>
> your first expression is really
>
> julia> -(0.11292333772901704 ^ 1.0000060554544523)
> -(0.11292333772901704 ^ 1.0000060554544523)
>
> while the second one changes it to
>
> julia> (-0.11292333772901704) ^ 1.0000060554544523
>
> ERROR: DomainError
> in ^ at math.jl:252
>
> which is indeed mathematically undefined.
>
>
> On Tuesday, August 12, 2014 2:24:39 PM UTC+2, Robert Feldt wrote:
>>
>> Actually this might be a bug. I really don't understand this behavior
>> (latest 0.3.0-rc3):
>>
>> feldt:~/tmp$ julia03
>> _
>> _ _ _(_)_ | A fresh approach to technical computing
>> (_) | (_) (_) | Documentation: http://docs.julialang.org
>> _ _ _| |_ __ _ | Type "help()" for help.
>> | | | | | | |/ _` | |
>> | | |_| | | | (_| | | Version 0.3.0-rc3 (2014-08-10 02:54 UTC)
>> _/ |\__'_|_|_|\__'_| | Official http://julialang.org/ release
>> |__/ | x86_64-apple-darwin13.3.0
>>
>> julia> -0.11292333772901704 ^ 1.0000060554544523
>> -0.11292184633488864
>>
>> julia> x = -0.11292333772901704
>> -0.11292333772901704
>>
>> julia> p = 1.0000060554544523
>> 1.0000060554544523
>>
>> julia> x ^ p
>> ERROR: DomainError
>> in ^ at math.jl:252
>>
>>
>>
>> Den tisdagen den 12:e augusti 2014 kl. 14:04:58 UTC+2 skrev Robert Feldt:
>>>
>>> Can LsqFit.jl be used to fit multivariate model? I tried this but must
>>> have missed something (maybe xv and Y need to have same length?):
>>>
>>> using LsqFit
>>>
>>> # We want to try to use curve_fit to fit the parameters of this
>>> multivariate model:
>>> mvmodel(x, p) = begin
>>> p[1] .* ((x[1,:] .^ p[2]) ./ (x[2,:] .^ p[3]))
>>> end
>>> N = 10
>>> M = 2
>>> X = randn(M, N)
>>> Params = [1.0, 2.0, 2.0] # Actual params that we are looking for
>>>
>>> # Dependent var with small error term
>>> Y = mvmodel(X, Params)[:] + 0.01*randn(N)
>>>
>>> # We need a vector to submit to curve_fit:
>>> xv = X[:]
>>>
>>> # Reshape to get back the original matrix.
>>> orig_matrix(xv, M) = reshape(xv, (M, int(length(xv)/M)))
>>>
>>> # The model function we supply to curve_fit needs to take a vector...
>>> model(xv, p) = mvmodel(orig_matrix(xv, M), p)
>>>
>>> fit = curve_fit(model, xv, Y, [0.5, 0.5, 0.5])
>>>
>>>
>>> Den måndagen den 14:e juli 2014 kl. 20:10:02 UTC+2 skrev Blake Johnson:
>>>>
>>>> The curve fitting functionality in Optim.jl is being moved into its own
>>>> package: LsqFit.jl:
>>>>
>>>> https://github.com/JuliaOpt/LsqFit.jl
>>>>
>>>> Installable via Pkg.add("LsqFit").
>>>>
>>>
--
Best regards,
/Robert Feldt
--
Tech. Dr. (PhD), Professor of Software Engineering
Blekinge Institute of Technology, Software Engineering Research Lab, and
Chalmers, Software Engineering Dept
Explanea.com - Igniting your Software innovation
robert.feldt (a) bth.se or robert.feldt (a) chalmers.se or
robert.feldt (a) gmail.com
Mobile phone: +46 (0) 733 580 580
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