Advanced Topics

Advanced topics for more customized training.

Initial control points

You can provide initial control points with a file. The file should contain a list of control points. The file format should be pickled pytorch (.pt), comma-separated values (.csv), tab-separated values (.tsv), JSON (.json), or YAML (.yml or .yaml).

Pickled PyTorch

See PyTorch documentation for details:

• https://pytorch.org/docs/stable/generated/torch.save.html#torch.save

• https://pytorch.org/docs/stable/generated/torch.load.html#torch.load

CSV

"(2, 0)", 0.0, 0.1, 0.2
"(1, 1)", 1.0, 1.1, 1.2
"(0, 2)", 2.0, 2.1, 2.2

TSV

"(2, 0)"     0.0     0.1     0.2
"(1, 1)"     1.0     1.1     1.2
"(0, 2)"     2.0     2.1     2.2

JSON

{
  "(2, 0)": [0.0, 0.1, 0.2],
  "(1, 1)": [1.0, 1.1, 1.2],
  "(0, 2)": [2.0, 2.1, 2.2]
}

YAML

"(2, 0)": [0.0, 0.1, 0.2]
"(1, 1)": [1.0, 1.1, 1.2]
"(0, 2)": [2.0, 2.1, 2.2]

Partial training

Funciton fit() provides some argments for partial training, i.e., train some of control points while the others are fixed.

import torch
import torch_bsf

# Prepare training data
ts = torch.tensor(  # parameters on a simplex
   [
      [3/3, 0/3, 0/3],
      [2/3, 1/3, 0/3],
      [2/3, 0/3, 1/3],
      [1/3, 2/3, 0/3],
      [1/3, 1/3, 1/3],
      [1/3, 0/3, 2/3],
      [0/3, 3/3, 0/3],
      [0/3, 2/3, 1/3],
      [0/3, 1/3, 2/3],
      [0/3, 0/3, 3/3],
   ]
)
xs = 1 - ts * ts  # values corresponding to the parameters

# Train the edges and surface of a Bezier triangle while its vertices are fixed
bs = torch_bsf.fit(
   params=ts,  # input observations (training data)
   values=xs,  # output observations (training data)
   init="control_points.yml",  # initial values of control points
   fix=[[3, 0, 0], [0, 3, 0], [0, 0, 3]],  # fix vertices of the Bezier triangle
)

# Predict by the trained model
t = [[0.2, 0.3, 0.5]]
x = bs(t)
print(f"{t} -> {x}")