Commit 20ea855d authored by Alison Beamish's avatar Alison Beamish
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Minor changes to documentation

parent 716d3a49
Pipeline #22956 failed with stage
in 10 seconds
......@@ -194,34 +194,34 @@ HaSa::multi_Class_Sampling(
in.raster = raster_stk, # clipped satellite time series stack [raster brick]
init.samples = 75, # starting number of spatial samples (recommended value: 75)
sample_type = "regular", # distribution of spatial samples ("random" or "regular";
# suggest: "regular") *See note 1
suggest: "regular") *See note 1
nb_models = 200, # number of models to collect (recommended value: 200)
nb_it = 10, # number of iterations for model accuracy
# (recommended value:10)
(recommended value:10)
buffer = 10, # distance (in m) for new sample collection around initial
# samples (depends on pixel size and image resolution)
samples (depends on pixel size and image resolution)
reference = ref, # table of reference spectra [data.frame]
model = "rf", # which machine learning algorithm to use ("rf" random
# forest or "svm" support vector machine;
# recommended input: rf)
forest or "svm" support vector machine;
recommended input: rf)
mtry = 10, # number of predictors used at random forest splitting nodes
# (recommended input: mtry << n predictors)
(recommended input: mtry << n predictors)
last = F, # only FALSE for one class classifier (TRUE or FALSE;
# recommended input: FALSE) *See note 2
recommended input: FALSE) *See note 2
seed = 3, # set seed for reproducible results (recommended value: 3)
init.seed = "sample", # "sample" for new or use Run@seeds to reproduce previous
# steps *See note 3
steps *See note 3
outPath = outPath, # output path for saving results
step = 1, # at which step should the procedure start (see 2.b.1)
# (recommended value: 1 (from the beginning))
classNames = classNames, # vector with class names in the order of reference spectra
n_classes = 7, # total number of classes to be separated
multiTest = 1, # number of test runs to compare different probability
# output *See note 4
output *See note 4
RGB = c(19,20,21), # pallette colors for the interactive plots
overwrite = TRUE, # overwrite the KML and raster files from previous runs
save_runs = TRUE, # an class object is saved into disk for each run (default TRUE)
parallel_mode = TRUE, # run loops using all available cores *only possible on Linux machines
save_runs = TRUE, # an class object is saved into disk for each run
(default TRUE)
parallel_mode = TRUE, # run loops using all available cores
*only possible on Linux machines
max_num_cores = 4, # maximum number of cores for parallelism (default 5)
plot_on_browser = FALSE # plot on the browser or inline in a notebook (default TRUE)
)
......@@ -272,7 +272,6 @@ The evaluation, thresholding or re-running, and export process is repeated for e
init.samples # printed in console
nb_models # printed in console
reference # out.reference
step # specify next step number
classNames # out.names
```
......
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