removed FLIBS from Makevars and included new file sits_predictors

DESCRIPTION

@@ -189,6 +189,7 @@ Collate:
  'sits_parallel.R'
  'sits_patterns.R'
  'sits_plot.R'
+ 'sits_predictors.R'
  'sits_reclassify.R'
  'sits_raster_data.R'
  'sits_raster_sub_image.R'

NAMESPACE

@@ -270,6 +270,8 @@ export(sits_mlp)
 export(sits_model_export)
 export(sits_mosaic)
 export(sits_patterns)
+export(sits_pred_features)
+export(sits_predictors)
 export(sits_reclassify)
 export(sits_reduce_imbalance)
 export(sits_regularize)

R/sits_predictors.R

@@ -0,0 +1,151 @@
+#' @title Obtain predictors for time series samples
+#' @name sits_predictors
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Predictors are X-Y values required for machine learning
+#' algorithms, organized as a data table where each row corresponds
+#' to a training sample. The first two columns of the predictors table
+#' are categorical ("label_id" and "label"). The other columns are
+#' the values of each band and time, organized first by band and then by time.
+#'
+#'
+#' @param samples Time series in sits format
+#'
+#'
+#' @return The predictors for the sample: a data.frame with one row per sample.
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+#'
+#' @export
+sits_predictors <- function(samples) {
+ pred <- .predictors(samples)
+}
+
+#' @title Obtain numerical values of predictors for time series samples
+#' @name sits_pred_features
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Predictors are X-Y values required for machine learning
+#' algorithms, organized as a data table where each row corresponds
+#' to a training sample. The first two columns of the predictors table
+#' are categorical ("label_id" and "label"). The other columns are
+#' the values of each band and time, organized first by band and then by time.
+#' This function returns the numeric values associated to each sample.
+#'
+#' @param pred X-Y predictors: a data.table with one row per sample.
+#'
+#' @return The Y predictors for the sample: a data.frame with one row per sample.
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+#'
+#' @export
+sits_pred_features <- function(pred){
+ features <- .pred_features(pred)
+}
+#' @title Obtain categorical id and labels of predictors for time series samples
+#' @name sits_pred_reference
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Predictors are X-Y values required for machine learning
+#' algorithms, organized as a data table where each row corresponds
+#' to a training sample. The first two columns of the predictors table
+#' are categorical ("label_id" and "label"). The other columns are
+#' the values of each band and time, organized first by band and then by time.
+#' This function returns the numeric values associated to each sample.
+#'
+#' @param pred X-Y predictors: a data.table with one row per sample.
+#'
+#' @return The label associated to each training sample.
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+sits_pred_references <- function(pred){
+ ref <- .pred_references(pred)
+}
+#' @title Normalize predictor values
+#' @name sits_pred_normalize
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Most machine learning algorithms require data to be
+#' normalized. This applies to the "SVM" method and to all deep learning ones.
+#' To normalize the predictors, it is required that the statistics per band
+#' for each sample have been obtained by the "sits_stats" function.
+#'
+#' @param pred X-Y predictors: a data.table with one row per sample.
+#' @param stats The output of the "sits_stats" function applied to the samples.
+#'
+#' @return A normalized set of predictor values
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+sits_pred_normalize <- function(pred, stats) {
+ pred <- .pred_normalize(pred, stats)
+}
+#' @title Obtain a fraction of the predictors data frame
+#' @name sits_pred_sample
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Many machine learning algorithms (especially deep learning)
+#' use part of the original samples as test data to adjust its hyperparameters
+#' and to find an optimal point of convergence using gradient descent.
+#' This function extracts a fraction of the predictors to serve as test values
+#' for the deep learning algorithm.
+#'
+#' @param pred X-Y predictors: a data.table with one row per sample.
+#' @param frac Fraction of the X-Y predictors to be extracted
+#'
+#' @return A fraction of the X-Y predictors.
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+sits_pred_sample <- function(pred, frac){
+ sample <- .pred_sample(pred, frac)
+}
+#' @title Obtain statistics for all sample bands
+#' @name sits_stats
+#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
+#' @description Most machine learning algorithms require data to be
+#' normalized. This applies to the "SVM" method and to all deep learning ones.
+#' To normalize the predictors, it is necessary to extract the statistics
+#' of each band of the samples. This function computes the 2% and 98% quantiles
+#' of the distribution of each band of the samples. This values are used as
+#' minimum and maximum values in the normalization operation performed by
+#' the sits_pred_normalize() function.
+#'
+#' @param samples Time series samples uses as training data.
+#'
+#' @return A list with the 2% and 98% quantiles for each band of the
+#' training data.
+#'
+#' @note
+#' Please refer to the sits documentation available in
+#' <https://e-sensing.github.io/sitsbook/> for detailed examples.
+#' @examples
+#' if (sits_run_examples()) {
+#' summary(samples_modis_ndvi)
+#' }
+sits_stats <- function(samples){
+ stats <- .sits_stats(samples)
+}

src/Makevars

@@ -1,3 +1,3 @@
 # CXX_STD = CXX11
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
-PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
+PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS)