fix input_pixels argument for compatibility with old models

R/api_check.R

@@ -1364,14 +1364,14 @@
 #' @title Does the result have the same number of pixels as the input values?
 #' @name .check_processed_values
 #' @param values a matrix of processed values
-#' @param n_input_pixels number of pixels in input matrix
+#' @param input_pixels number of pixels in input matrix
 #' @return Called for side effects.
 #' @keywords internal
 #' @noRd
-.check_processed_values <- function(values, n_input_pixels) {
+.check_processed_values <- function(values, input_pixels) {
  .check_set_caller(".check_processed_values")
  .check_that(
- !(is.null(nrow(values))) && nrow(values) == n_input_pixels
+ !(is.null(nrow(values))) && nrow(values) == input_pixels
  )
  return(invisible(values))
 }

R/api_classify.R

@@ -115,7 +115,7 @@
  # Fill with zeros remaining NA pixels
  values <- C_fill_na(values, 0)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Log here
  .debug_log(
  event = "start_block_data_classification",
@@ -127,7 +127,7 @@
  # Are the results consistent with the data input?
  .check_processed_values(
  values = values,
- n_input_pixels = n_input_pixels
+ input_pixels = input_pixels
  )
  # Log
  .debug_log(

R/api_combine_predictions.R

@@ -219,13 +219,13 @@
  # Average probability calculation
  comb_fn <- function(values, uncert_values = NULL) {
  # Check values length
- n_input_pixels <- nrow(values[[1]])
+ input_pixels <- nrow(values[[1]])
  # Combine by average
  values <- weighted_probs(values, weights)
  # get the number of labels
  n_labels <- length(sits_labels(cubes[[1]]))
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  .check_processed_labels(values, n_labels)
  # Return values
  values
@@ -244,13 +244,13 @@
  # Average probability calculation
  comb_fn <- function(values, uncert_values) {
  # Check values length
- n_input_pixels <- nrow(values[[1]])
+ input_pixels <- nrow(values[[1]])
  # Combine by average
  values <- weighted_uncert_probs(values, uncert_values)
  # get the number of labels
  n_labels <- length(sits_labels(cubes[[1]]))
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  .check_processed_labels(values, n_labels)
  # Return values
  values

R/api_label_class.R

@@ -148,10 +148,10 @@
 .label_fn_majority <- function() {
  label_fn <- function(values) {
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  values <- C_label_max_prob(values)
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return values
  values
  }

R/api_mixture_model.R

@@ -160,15 +160,15 @@
  em_mtx <- .endmembers_as_matrix(em)
  mixture_fn <- function(values) {
  # Check values length
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Process NNLS solver and return
  values <- C_nnls_solver_batch(
  x = as.matrix(values),
  em = em_mtx,
  rmse = rmse
  )
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return values
  values
  }

R/api_reclassify.R

@@ -158,7 +158,7 @@
  stop(.conf("messages", ".reclassify_fn_cube_mask"))
  }
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Convert to character vector
  values <- as.character(values)
  mask_values <- as.character(mask_values)
@@ -185,12 +185,12 @@
  # Get values as numeric
  values <- matrix(
  data = labels_code[match(values, labels)],
- nrow = n_input_pixels
+ nrow = input_pixels
  )
  # Mask NA values
  values[is.na(env[["mask"]])] <- NA
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return values
  values
  }

R/api_smooth.R

@@ -172,7 +172,7 @@
  # Define smooth function
  smooth_fn <- function(values, block) {
  # Check values length
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Compute logit
  values <- log(values / (rowSums(values) - values))
  # Process Bayesian
@@ -187,7 +187,7 @@
  # Compute inverse logit
  values <- exp(values) / (exp(values) + 1)
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return values
  values
  }

R/api_uncertainty.R

@@ -239,12 +239,12 @@
  # Define uncertainty function
  uncert_fn <- function(values) {
  # Used in check (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Process least confidence
  # return a matrix[rows(values),1]
  values <- C_least_probs(values)
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return data
  values
  }
@@ -260,11 +260,11 @@
  # Define uncertainty function
  uncert_fn <- function(values) {
  # Used in check (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Process least confidence
  values <- C_entropy_probs(values) # return a matrix[rows(values),1]
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return data
  values
  }
@@ -280,11 +280,11 @@
  # Define uncertainty function
  uncert_fn <- function(values) {
  # Used in check (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Process margin
  values <- C_margin_probs(values) # return a matrix[rows(data),1]
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return data
  values
  }

R/api_variance.R

@@ -176,7 +176,7 @@
  # Define smooth function
  smooth_fn <- function(values, block) {
  # Check values length
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Compute logit
  values <- log(values / (rowSums(values) - values))
  # Process variance
@@ -188,7 +188,7 @@
  neigh_fraction = neigh_fraction
  )
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Return values
  values
  }

R/sits_lighttae.R

@@ -322,7 +322,7 @@ sits_lighttae <- function(samples = NULL,
  # Unserialize model
  torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Transform input into a 3D tensor
  # Reshape the 2D matrix into a 3D array
  n_samples <- nrow(values)
@@ -356,7 +356,7 @@ sits_lighttae <- function(samples = NULL,
  )
  # Are the results consistent with the data input?
  .check_processed_values(
- values = values, n_input_pixels = n_input_pixels
+ values = values, input_pixels = input_pixels
  )
  # Update the columns names to labels
  colnames(values) <- labels

R/sits_machine_learning.R

@@ -79,13 +79,13 @@ sits_rfor <- function(samples = NULL, num_trees = 100, mtry = NULL, ...) {
  # Verifies if randomForest package is installed
  .check_require_packages("randomForest")
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Do classification
  values <- stats::predict(
  object = model, newdata = values, type = "prob"
  )
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Reorder matrix columns if needed
  if (any(labels != colnames(values))) {
  values <- values[, labels]
@@ -193,7 +193,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
  # Verifies if e1071 package is installed
  .check_require_packages("e1071")
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Performs data normalization
  values <- .pred_normalize(pred = values, stats = ml_stats)
  # Do classification
@@ -203,7 +203,7 @@ sits_svm <- function(samples = NULL, formula = sits_formula_linear(),
  # Get the predicted probabilities
  values <- attr(values, "probabilities")
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Reorder matrix columns if needed
  if (any(labels != colnames(values))) {
  values <- values[, labels]
@@ -337,14 +337,14 @@ sits_xgboost <- function(samples = NULL, learning_rate = 0.15,
  # Verifies if xgboost package is installed
  .check_require_packages("xgboost")
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Do classification
  values <- stats::predict(
  object = model, as.matrix(values), ntreelimit = ntreelimit,
  reshape = TRUE
  )
  # Are the results consistent with the data input?
- .check_processed_values(values, n_input_pixels)
+ .check_processed_values(values, input_pixels)
  # Update the columns names to labels
  colnames(values) <- labels
  return(values)

R/sits_mlp.R

@@ -277,7 +277,7 @@ sits_mlp <- function(samples = NULL,
  # Unserialize model
  torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Performs data normalization
  values <- .pred_normalize(pred = values, stats = ml_stats)
  # Transform input into matrix
@@ -305,7 +305,7 @@ sits_mlp <- function(samples = NULL,
  )
  # Are the results consistent with the data input?
  .check_processed_values(
- values = values, n_input_pixels = n_input_pixels
+ values = values, input_pixels = input_pixels
  )
  # Update the columns names to labels
  colnames(values) <- labels

R/sits_resnet.R

@@ -370,7 +370,7 @@ sits_resnet <- function(samples = NULL,
  # Unserialize model
  torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Transform input into a 3D tensor
  # Reshape the 2D matrix into a 3D array
  n_samples <- nrow(values)
@@ -403,7 +403,7 @@ sits_resnet <- function(samples = NULL,
  x = torch::torch_tensor(values, device = "cpu")
  )
  .check_processed_values(
- values = values, n_input_pixels = n_input_pixels
+ values = values, input_pixels = input_pixels
  )
  # Update the columns names to labels
  colnames(values) <- labels

R/sits_tae.R

@@ -289,7 +289,7 @@ sits_tae <- function(samples = NULL,
  # Unserialize model
  torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Transform input into a 3D tensor
  # Reshape the 2D matrix into a 3D array
  n_samples <- nrow(values)
@@ -323,7 +323,7 @@ sits_tae <- function(samples = NULL,
  )
  # Are the results consistent with the data input?
  .check_processed_values(
- values = values, n_input_pixels = n_input_pixels
+ values = values, input_pixels = input_pixels
  )
  # Update the columns names to labels
  colnames(values) <- labels

R/sits_tempcnn.R

@@ -339,7 +339,7 @@ sits_tempcnn <- function(samples = NULL,
  # Unserialize model
  torch_model[["model"]] <- .torch_unserialize_model(serialized_model)
  # Used to check values (below)
- n_input_pixels <- nrow(values)
+ input_pixels <- nrow(values)
  # Transform input into a 3D tensor
  # Reshape the 2D matrix into a 3D array
  n_samples <- nrow(values)
@@ -374,7 +374,7 @@ sits_tempcnn <- function(samples = NULL,
  )
  # Are the results consistent with the data input?
  .check_processed_values(
- values = values, n_input_pixels = n_input_pixels
+ values = values, input_pixels = input_pixels
  )
  # Update the columns names to labels
  colnames(values) <- labels