Build observation matrices for the CNN1D model

Converts a prepared data frame into the arrays needed for training or evaluation: a 3-D sequence array Xseq (shape [N, K, p+1]) and companion vectors pf, y, and sample weights w.

Usage

make_obs_matrices(
  df,
  x_inputs,
  scaler,
  K = 64L,
  knot_grid = seq(0, 1, length.out = K),
  pf_left = -2,
  pf_right = 7.6,
  wet_split_cm = 4.2,
  w_wet = 1,
  w_dry = 1
)

Arguments

df

A prepared data frame (output of prepare_swrc_data() or compatible structure) containing covariate columns, matric_head, theta_n, and theta_max_n.

x_inputs

Character vector of covariate column names.

scaler

A fitted scaler from fit_minmax().

K

Number of knot points (default 64L).

knot_grid

Numeric vector of knot positions (default seq(0, 1, length.out = K)).

pf_left

Left boundary of the pF domain (default -2).

pf_right

Right boundary of the pF domain (default 7.6).

wet_split_cm

Matric head threshold (cm) separating wet / dry end (default 4.2, corresponding to pF approximately 0.62).

w_wet

Sample weight for wet-end observations (default 1).

w_dry

Sample weight for dry-end observations (default 1).

Value

A named list:

Xseq

3-D numeric array [N, K, p+1].

pf

Numeric matrix [N, 1] of normalised pF values.

y

Numeric matrix [N, 2]: columns are theta_n and theta_max_n.

w

Numeric vector of sample weights.