test_that("Creating clustering using Self-organizing Maps", {
set.seed(2903)
expect_warning(som_map <- sits_som_map(
samples_modis_ndvi,
grid_xdim = 4,
grid_ydim = 4,
distance = "euclidean"
))
expect_true(all(colnames(som_map$labelled_neurons) %in%
c("id_neuron", "label_samples", "count",
"prior_prob", "post_prob")))
expect_true(som_map$labelled_neurons[1, ]$prior_prob >= 0)
expect_true(som_map$labelled_neurons[1, ]$post_prob >= 0)
expect_true(all(unique(som_map$labelled_neurons$id_neuron) %in% 1:16))
cleaned_samples <- sits_som_clean_samples(som_map)
expect_true("eval" %in% names(cleaned_samples))
expect_true("post_prob" %in% names(cleaned_samples))
expect_true(all(cleaned_samples$eval %in% c("clean", "analyze", "remove")))
expect_true(cleaned_samples[1, ]$post_prob > 0)
cluster_purity <- suppressMessages(sits_som_evaluate_cluster(som_map))
expect_true(cluster_purity[1, ]$mixture_percentage > 60)
expect_true(cluster_purity[2, ]$mixture_percentage < 40)
expect_error(sits_som_clean_samples(samples_modis_ndvi))
expect_error(sits_som_evaluate_samples(samples_modis_ndvi))
expect_warning(som_map <- sits_som_map(
samples_modis_ndvi,
grid_xdim = 4,
grid_ydim = 4,
distance = "dtw"
)
)
expect_true(all(colnames(som_map$labelled_neurons) %in%
c("id_neuron", "label_samples", "count",
"prior_prob", "post_prob")))
})
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