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:

CSV

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

TSV

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

JSON

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

YAML

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

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

ts = torch.tensor(  # parameters on a simplex
   [
      [8/8, 0/8],
      [7/8, 1/8],
      [6/8, 2/8],
      [5/8, 3/8],
      [4/8, 4/8],
      [3/8, 5/8],
      [2/8, 6/8],
      [1/8, 7/8],
      [0/8, 8/8],
   ]
)
xs = 1 - ts * ts  # values corresponding to the parameters

# Initialize 2D control points of a Bezier triangle of degree 3
init = {
   # index: value
   (3, 0): [0.0, 0.1],
   (2, 1): [1.0, 1.1],
   (1, 2): [2.0, 2.1],
   (0, 3): [3.0, 3.1],
}

# Or, generate random control points in [0, 1)
init = torch_bsf.bezier_simplex.rand(n_params=2, n_values=2, degree=3)

# Or, load control points from a file
init = torch_bsf.bezier_simplex.load("control_points.yml")

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

# Predict by the trained model
t = [
   [0.2, 0.8],
   [0.7, 0.3],
]
x = bs(t)
print(f"{t} -> {x}")