LDA on Julia using MultivariateStats
I'm learning classification methods using the book of Brunton & Kutz "Data-Driven Science and Engineering", but instead of use only the MATLAB and Python code resources, i rewriting the textbook examples using Julia, because is my main programming language.
I can't find why fitting a MulticlassLDA model to the data doesn't work, it returns a
DimensionMismatch("Inconsistent array sizes."), but as far as i can tell my arrays are dispatched to the fit function as indicated in the documentation.
This is my code:
using MAT, LinearAlgebra, Statistics, MultivariateStats # Load data in MATLAB format. Abailible in http://www.databookuw.com/ dogs = read(matopen("../DATA/dogData_w.mat"),"dog_wave") cats = read(matopen("../DATA/catData_w.mat"),"cat_wave") CD = hcat(dogs,cats) u, s, v = svd(CD .- mean(CD)) #SVD decomposition xtrain = vcat(v[1:60,2:2:4],v[81:140,2:2:4]) #training data array, dims 120x2 label = Int.(vcat(ones(60),-ones(60))) #label's vector, length 120 xtest = vcat(v[61:80,2:2:4],v[141:160,2:2:4]) classf= fit(MulticlassLDA,2,xtrain,label)
You have two issues which are fixed this way:
label = [fill(1, 60); fill(2, 60)] # labels must range from 1 to n fit(MulticlassLDA,2,permutedims(xtrain),label) # observations in xtrain must be stored in columns (not rows)
See the comment in https://multivariatestatsjl.readthedocs.io/en/stable/index.html:
All methods implemented in this package adopt the column-major convention of JuliaStats: in a data matrix, each column corresponds to a sample/observation, while each row corresponds to a feature (variable or attribute).
And an explanation about
yargument to fit https://multivariatestatsjl.readthedocs.io/en/stable/mclda.html#data-analysis:
y– the vector of class labels, of length
n. Each element of
ymust be an integer between
I hope this helps.