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wildart
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wildart
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Great. Now we need to shape it into an acceptable API style.
src/MultivariateStats.jl
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| using LinearAlgebra | ||
| using SparseArrays | ||
| using Statistics: middle | ||
| using StatsAPI: RegressionModel | ||
| using StatsBase: | ||
| SimpleCovariance, | ||
| CovarianceEstimator, | ||
| AbstractDataTransform, | ||
| ConvergenceException, | ||
| pairwise, | ||
| pairwise!, | ||
| CoefTable | ||
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| import Statistics: mean, var, cov, covm, cor | ||
| import Base: length, size, show | ||
| import StatsAPI: fit, predict, coef, weights, dof, r2 | ||
| import LinearAlgebra: eigvals, eigvecs | ||
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| export |
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Was it automatic styling? Why the format change?
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| # whiten | ||
| Whitening, # Type: Whitening transformation | ||
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These new lines helped to separate (visually) various algorithms & their methods.
src/mca.jl
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| """ | ||
| ca | ||
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| Fit a correspondence analysis using the data array `X` whose rows are | ||
| the objects and columns are the variables. The first `d` components | ||
| are retained. | ||
| """ | ||
| function ca(X, d) | ||
| c = fit(CA, X, d) | ||
| return c | ||
| end |
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There is no need for this high level function, standard API presume fit call for construction a model.
src/mca.jl
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| ca.G = Wc \ Q * Dq | ||
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| # Get the eigenvalues | ||
| ca.I = D .^ 2 |
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fit call should always return model
src/mca.jl
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| function CA(X) | ||
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| # Convert to proportions | ||
| X = X ./ sum(X) | ||
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| # Calculate row and column means | ||
| r = sum(X, dims = 2)[:] | ||
| c = sum(X, dims = 1)[:] | ||
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| # Center the data matrix to create residuals | ||
| R = X - r * c' | ||
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| # Standardize the data matrix to create standardized residuals | ||
| Wr = Diagonal(sqrt.(r)) | ||
| Wc = Diagonal(sqrt.(c)) | ||
| SR = Wr \ R / Wc | ||
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| T = eltype(X) | ||
| return CA(X, R, r, c, SR, zeros(T, 0, 0), zeros(T, 0, 0), zeros(T, 0)) | ||
| end |
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The algorithm code should be in the fit function. There is no need for special constructor of the model.
src/mca.jl
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| # Recoding dictionary, maps each distinct value in z to | ||
| # an offset | ||
| rd = Dict{eltype(z),Int}() |
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Use function parametrized types for arguments.
src/mca.jl
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| end | ||
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| # Reverse the recoding dictionary | ||
| rdi = Dict{Int,eltype(z)}() |
src/mca.jl
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| # to levels for each variable. | ||
| function make_indicators(Z) | ||
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| rd, rdr = Dict[], Dict[] |
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You need to specify types for these dicts.
test/mca.jl
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| :V3 => ["D", "D", "D", "C", "D", "C", "D", "C"], | ||
| ) | ||
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| m = mca(da, 3; vnames = names(da)) |
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Use standard API for construction of the model - fit call.
| function inertia(mca::MCA) | ||
| inr = ( | ||
| Raw = mca.C.I, | ||
| Unadjusted = mca.unadjusted_eigs, | ||
| Benzecri = mca.benzecri_eigs, | ||
| Greenacre = mca.greenacre_eigs, | ||
| ) | ||
| return inr | ||
| end |
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You may want to define multiple getter function for various inertia types.
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Thanks, I have resolved most of these. The CI fails are due to importation of DataFrames. If you don't want a DataFrames dependency we can probably work around it, otherwise I can update the project file to include this dependency. |
pick f57ef58 check for repeated non-zero signular values
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This is an implementation of Correspondence Analysis and Multiple Correspondence Analysis. I think it would be useful for this to be included in the MultivariateStats package.