test_that("svm regression works", { library(dplyr) df_reg <- MASS::Boston colnames(df_reg)[1] <- "some non-syntactic name" df_cls3 <- iris df_cls2 <- iris %>% filter(Species != "setosa") m_reg <- m("svm", medv ~ ., df_reg, cost = 1) expect_equal(nrow(coef(m_reg)), 14) expect_equal(nrow(predict(m_reg, df_reg)), 506) expect_equal(nrow(fitted(m_reg)), 506) expect_equal(nrow(resid(m_reg)), 506) expect_equal(nrow(predict(m_reg, select(df_reg, -"medv"))), 506) }) test_that("svm classification works", { library(dplyr) df_cls3 <- iris df_cls2 <- iris %>% filter(Species != "setosa") m_reg <- classify(df_cls3, Species ~ ., m("svm", cost = c(1))) expect_equal(nrow(predict(m_reg, df_cls3)), 450) expect_equal(nrow(fitted(m_reg)), 450) expect_equal(nrow(predict(m_reg, df_cls3 %>% select(-"Species"))), 450) }) test_that("svm classification (2class) works", { library(dplyr) df_cls3 <- iris df_cls2 <- iris %>% filter(Species != "virginica") m_reg <- classify(df_cls2, Species ~ ., m("svm", cost = c(1))) expect_equal(nrow(coef(m_reg)), 5) expect_equal(nrow(predict(m_reg, df_cls2)), 100) expect_equal(nrow(fitted(m_reg)), 100) expect_equal(nrow(predict(m_reg, df_cls2 %>% select(-"Species"))), 100) })