fastmath.regression
elastic-net
(elastic-net x y)(elastic-net {:keys [lambda1 lambda2 tolerance max-iters], :or {lambda1 0.1, lambda2 0.1, tolerance 1.0E-4, max-iters 1000}} x y)elastic-net regression. Backend library: smile
gaussian-process
(gaussian-process x y)(gaussian-process {:keys [kernel lambda], :or {kernel (k/kernel :gaussian), lambda 0.5}} x y)gaussian-process regression. Backend library: smile
gaussian-process+
(gaussian-process+ x y)(gaussian-process+ {:keys [kscale kernel noise normalize?], :or {kscale 1.0, kernel (k/kernel :gaussian 1.0), normalize? false}} xs y)gradient-tree-boost
(gradient-tree-boost x y)(gradient-tree-boost {:keys [loss number-of-trees shrinkage max-nodes subsample], :or {loss :least-squares, number-of-trees 500, shrinkage 0.005, max-nodes 6, subsample 0.7}} x y)gradient-tree-boost regression. Backend library: smile
lasso
(lasso x y)(lasso {:keys [lambda tolerance max-iters], :or {lambda 10.0, tolerance 0.001, max-iters 1000}} x y)lasso regression. Backend library: smile
neural-net
(neural-net x y)(neural-net {:keys [activation-function layers learning-rate momentum weight-decay number-of-epochs], :or {error-function :logistic-sigmoid, learning-rate 0.1, momentum 0.0, weight-decay 0.0, number-of-epochs 25}} x y)neural-net regression. Backend library: smile
posterior-samples
(posterior-samples gp vs)(posterior-samples gp vs stddev?)Gaussian process - draw samples from posterior for given vs
predict
(predict model v)(predict model v info?)Predict for given vector. If info? is true returns also additional information (default false).
predict-all
(predict-all model v)(predict-all model v info?)Predict for given sequence of vectors. If info? is true returns also additional information (default false).
prior-samples
(prior-samples gp vs)Gaussian process - draw samples from prior for given vs
random-forest
(random-forest x y)(random-forest {:keys [number-of-trees mtry node-size max-nodes subsample], :or {number-of-trees 500, node-size 2, max-nodes 100, subsample 1.0}} x y)random-forest regression. Backend library: smile
rbf-network
(rbf-network x y)(rbf-network {:keys [distance rbf number-of-basis normalize?], :or {distance dist/euclidean, number-of-basis 10, normalize? false}} x y)rbf-network regression. Backend library: smile
regression-tree
(regression-tree x y)(regression-tree {:keys [max-nodes node-size], :or {max-nodes 100, node-size 2}} x y)regression-tree regression. Backend library: smile
ridge
(ridge x y)(ridge {:keys [lambda], :or {lambda 0.1}} x y)ridge regression. Backend library: smile
svr
(svr x y)(svr {:keys [kernel C eps tolerance], :or {kernel (k/kernel :linear), C 1.0, eps 0.001, tolerance 0.001}} x y)svr regression. Backend library: smile
train
(train model)(train model xs ys)Train another set of data for given regression model or force training already given data.