Fit a Kolmogorov-Arnold Fourier Network

Fits a KAF model for regression using mean squared error loss.

Usage

kaf_fit(
  x,
  y,
  task = c("auto", "regression", "binary", "multiclass"),
  hidden = c(64, 64),
  num_grids = 16,
  dropout = 0,
  use_layernorm = TRUE,
  fourier_init_scale = 0.01,
  epochs = 1000,
  lr = 0.001,
  batch_size = NULL,
  shuffle = TRUE,
  validation_split = 0,
  x_val = NULL,
  y_val = NULL,
  weight_decay = 0,
  standardize_x = TRUE,
  standardize_y = NULL,
  patience = NULL,
  verbose = TRUE,
  print_every = 100,
  seed = NULL,
  restore_best = TRUE,
  min_delta = 0
)

Arguments

x: Matrix, data frame, vector, or 2D torch tensor of predictors.
y: Vector, matrix, data frame, or torch tensor of targets.
task: Character. One of "auto", "regression", "binary", or "multiclass". With "auto", factor, character, and logical targets are treated as classification; numeric targets are treated as regression.
hidden: Integer vector. Hidden layer sizes.
num_grids: Integer. Number of Fourier frequencies per KAF layer.
dropout: Numeric. Dropout probability.
use_layernorm: Logical. Whether to apply layer normalization.
fourier_init_scale: Numeric. Initial scale of the Fourier branch.
epochs: Integer. Maximum number of training epochs.
lr: Numeric. Learning rate.
batch_size: Optional integer. Mini-batch size. If NULL, full-batch training is used.
shuffle: Logical. Whether to shuffle training rows each epoch.
validation_split: Numeric in [0, 1). Fraction of rows to reserve for validation. Ignored if x_val and y_val are supplied.
x_val: Optional validation predictors.
y_val: Optional validation targets.
weight_decay: Numeric. Adam weight decay.
standardize_x: Logical. Whether to standardize predictors using the training-set mean and standard deviation.
standardize_y: Logical or NULL. Whether to standardize regression targets using the training-set mean and standard deviation. If NULL, targets are standardized for regression and not standardized for classification. Predictions are automatically transformed back to the original target scale.
patience: Optional integer. Number of epochs without improvement before early stopping.
verbose: Logical. Whether to print progress.
print_every: Integer. Print frequency.
seed: Optional integer random seed.
restore_best: Logical. Whether to restore the best observed model state after training.
min_delta: Numeric. Minimum loss improvement required to update the best model state.

Value

An object of class "kaf_fit".