test_that("Classify with random forest - single core and multicore", {
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor(num_trees = 40))
expect_type(rfor_model, "closure")
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
class_ndvi <- sits_classify(
data = point_ndvi,
ml_model = rfor_model,
progress = FALSE
)
expect_true(nrow(class_ndvi$predicted[[1]]) == 17)
expect_true(all(class_ndvi$predicted[[1]]$class %in%
sits_labels(samples_modis_ndvi)))
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
class_ndvi <- sits_classify(
data = point_ndvi,
ml_model = rfor_model,
multicores = 2,
progress = FALSE
)
expect_true(nrow(class_ndvi$predicted[[1]]) == 17)
expect_true(all(class_ndvi$predicted[[1]]$class %in%
sits_labels(samples_modis_ndvi)))
})
test_that("Classify a set of time series with svm + filter", {
# single core
samples_filt <- sits_apply(cerrado_2classes,
NDVI = sits_sgolay(NDVI),
EVI = sits_sgolay(EVI),
)
svm_model <- sits_train(samples_filt, sits_svm())
class1 <- sits_classify(cerrado_2classes,
ml_model = svm_model,
filter_fn = sits_sgolay(),
multicores = 2,
progress = FALSE,
)
expect_true(class1$predicted[[1]]$class %in%
sits_labels(cerrado_2classes))
})
test_that("Classify error bands 1", {
model <- sits_train(samples_modis_ndvi, sits_svm())
point <- sits_select(point_mt_6bands, "EVI")
expect_error(
sits_classify(
data = point,
ml_model = model
)
)
})
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