kkthn.builder

class kkthn.builder.Constraint(residual: "'Expression'", kind: 'str', text: 'str')

Bases: object

residual: Expression

kind: str

text: str

class kkthn.builder.DatasetSpec(parameters_path: 'str', variables_path: 'str | None' = None)

Bases: object

parameters_path: str

variables_path: str | None = None

class kkthn.builder.Expression(fn: Callable, text: str)

Bases: object

eval(ctx)

class kkthn.builder.VectorExpression(fn: Callable, text: str, *, size: int, components: list[Expression] | None = None)

Bases: object

eval(ctx)

class kkthn.builder.Constant(name: str, value: Any)

Bases: object

class kkthn.builder.ConstraintList

Bases: object

items: list[Constraint]

add(*constraints: Constraint) → ConstraintList

equality(expr, *, name: str | None = None) → ConstraintList

inequality(expr, *, name: str | None = None) → ConstraintList

class kkthn.builder.KKTHardNet(name: str = 'kkthardnet', *, train: dict[str, Any] | KKTTrainConfig | None = None, projection: dict[str, Any] | ProjectionSettings | None = None)

Bases: object

parameter_names: list[str]

variable_names: list[str]

inverse_parameter_names: list[str]

objective: Expression | None

dataset_spec: DatasetSpec | None

add_parameter(names: str | Iterable[str])

add_variable(names: str | Iterable[str])

add_inverse_parameter(names: str | Iterable[str], *, init_value: float | Iterable[float] | None = None)

matrix(values) → Constant

Register a constant matrix and return its wrapper.

vector(values) → Constant

Register a constant vector and return its wrapper.

tensor(values) → Constant

Register a constant tensor and return its wrapper.

extract(path: str | Path) → dict[str, Constant]

Load arrays from a .npz file and expose them as model attributes.

dataset(*, parameters: str | Path, variables: str | Path | None = None) → KKTHardNet

set_dataset(*, parameters: str | Path, variables: str | Path | None = None) → KKTHardNet

use_dataset(*, parameters: str | Path, variables: str | Path | None = None) → KKTHardNet

set_train_config(config: dict[str, Any] | KKTTrainConfig) → KKTHardNet

set_projection_config(config: dict[str, Any] | ProjectionSettings) → KKTHardNet

model(*, train: dict[str, Any] | KKTTrainConfig | None = None, projection: dict[str, Any] | ProjectionSettings | None = None) → dict[str, Any]

optimize(*, train: dict[str, Any] | KKTTrainConfig | None = None, projection: dict[str, Any] | ProjectionSettings | None = None) → dict[str, Any]

estimate(*, train: dict[str, Any] | KKTTrainConfig | None = None, projection: dict[str, Any] | ProjectionSettings | None = None) → dict[str, Any]

load(metadata_path: str | Path, *, verbose: bool | None = None) → KKTHardNet

predict(values, *, projection_backend: str = 'jax') → ndarray

summary() → dict[str, Any]

Print a compact summary of the current completed or loaded run.

plot_history(*, show: bool = True, save_dir: str | Path | None = None, bg: str = 'grey')

Plot and save MSE/loss and constraint violation histories.

lin(matrix, expr)

Form A @ expr from an extracted or registered matrix/vector.

batch_lin(matrix, expr)

Form a vector expression from a 2D matrix and vector expression.

quad(matrix, expr) → Expression

Form expr.T @ Q @ expr.

batch_quad(tensor, expr) → VectorExpression

Form batched quadratic expressions from a rank-3 tensor.

batch_exp(expr) → VectorExpression

sin(expr)

cos(expr)

exp(expr)

log(expr)

sqrt(expr)

abs(expr)

kkthn.builder.ProblemBuilder

alias of KKTHardNet