A regression example is explained for the SALSA package by the
sinc(x) = sin(x)./x function.
This package provides a function
salsa and explanation on
SALSAModel for the regression case. This use case is supported by the Fixed-Size approach [FS2010] and Nyström approximation with the specific
LEAST_SQUARES() loss function and cross-validation criterion
mse() (mean-squared error).
using SALSA, Base.Test srand(1234) sinc(x) = sin(x)./x X = linspace(0.1,20,100)'' Xtest = linspace(0.11,19.9,100)'' y = sinc(X) model = SALSAModel(NONLINEAR, SIMPLE_SGD(), LEAST_SQUARES, validation_criterion=MSE(), process_labels=false) model = salsa(X, y, model, Xtest) @test_approx_eq_eps mse(sinc(Xtest), model.output.Ytest) 0.05 0.01
By taking a look at the code snippet above we can notice a major difference with the Classification example. The model is equipped with the
LEAST_SQUARES loss function while the cross-validation criterion is given by
MSE. Another important model-related parameter is
process_labels which should be set to
false in order to switch into regression mode. These four essential components unambiguously define a regression problem solved stochastically by the
|[FS2010]||De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., “Optimized Fixed-Size Kernel Models for Large Data Sets”, Computational Statistics & Data Analysis, vol. 54, no. 6, Jun. 2010, pp. 1484-1504.|