Support Vector Regression for Unsupervised Machine Learning
SVR ================
Support Vector Regression (SVR) analysis in Julia utilizing the libSVM library.
SVR is a module of MADS (Model Analysis & Decision Support).
Installation
import Pkg; Pkg.add("SVR")Examples
Matching sine function:
import SVR
import Mads
X = sort(rand(40) * 5) y = sin.(X)
Predict y based on X using RBF
Mads.plotseries([y SVR.fit(y, permutedims(X); kernel_type=SVR.RBF)], "figures/rbf.png"; title="RBF", names=["Truth", "Prediction"])
Predict y based on X using LINEAR
Mads.plotseries([y SVR.fit(y, permutedims(X); kernel_type=SVR.LINEAR)], "figures/linear.png"; title="Linear", names=["Truth", "Prediction"])
Predict y based on X using POLY
Mads.plotseries([y SVR.fit(y, permutedims(X); kernel_type=SVR.POLY, coef0=1.)], "figures/poly.png"; title="Polynomial", names=["Truth", "Prediction"])
libSVM test example:
import SVR
x, y = SVR.readlibsvmfile(joinpath(dirname(pathof(SVR)), "..", "test", "mg.libsvm")) # read a libSVM input file
pmodel = SVR.train(y, permutedims(x)) # train a libSVM model
y_pr = SVR.predict(pmodel, permutedims(x)); # predict based on the libSVM model
SVR.savemodel(pmodel, "mg.model") # save the libSVM model
SVR.freemodel(pmodel) # free the memory allocation of the libSVM model
Projects using SVR
Publications, Presentations, Projects
