SpatialDownscaling (version 0.1.2)

predict.UNet: Predict function for UNet model

Description

This function generates predictions using a trained UNet model.

Usage

# S3 method for UNet
predict(object, newdata, time_points = NULL, ...)

Value

Array of predictions in format (x, y, time).

Arguments

object

A UNet model object.

newdata

Array or list of arrays. New data to predict on in format (x, y, time).

time_points

An optional numeric vector containing the time points of the new data.

...

Additional arguments (not used).

Details

The predict function applies the trained UNet model to new coarse data. It performs denormalization if the model was trained with normalization.

See Also

unet for fitting UNet model.

Examples

# \donttest{
 # Create tiny dummy data:
 # Coarse grid: 8x8 → Fine grid: 16x16
 nx_c <- 8 
 ny_c <- 8
 nx_f <- 16
 ny_f <- 16
 T <- 5  # number of time steps
 
 # Coarse data:
 coarse_data <- array(runif(nx_c * ny_c * T),
                      dim = c(nx_c, ny_c, T))
 
 # Fine data:
 fine_data <- array(runif(nx_f * ny_f * T),
                    dim = c(nx_f, ny_f, T))
 
 # Optional time points
 time_points <- 1:T
 
 # Fit a tiny UNet (very small filters to keep the example fast)
 model_obj <- unet(
   coarse_data,
   fine_data,
   time_points = time_points,
   filters = c(8, 16),
   initial_filters = c(4),
   epochs = 1,
   batch_size = 4,
   verbose = 0
 )
 
 T_new <- 3
 newdata <- array(runif(nx_c * ny_c * T_new),
                      dim = c(nx_c, ny_c, T_new))
 predictions <- predict(model_obj, newdata, 1:T_new)
# }