`igl.types`

Public Protocols, enum.StrEnum classes, and Literal companions.

Protocols

`igl.types.EncoderProtocol`

Bases: Protocol

A callable mapping ambient inputs to an max_dim-dimensional latent.

`igl.types.OperatorFn`

Bases: Protocol

Log-space kernel operator.

Given a distance tensor d and a width tensor sigma of broadcastable shapes, returns (log_abs, sign) so the multi-scale product kernel can be accumulated in log-space while tracking signs for oscillatory operators.

`igl.types.LossStrategy`

Bases: Protocol

Pluggable loss for :class:igl.MatryoshkaTrainer.

Implementations supply task-specific targets (one-hot encoding, pass-through, log-Euclidean SPD targets, …), the loss to minimise, a scalar metric for early-stopping decisions, and — separately — a curve score used by :func:igl.eval_dimension_curve to make dimension curves informative (e.g. error rate for classifiers, which doesn't saturate the way cross-entropy does).

Attributes:

Name	Type	Description
`higher_is_better`	`bool`	`True` when `metric()` should be maximised (e.g. accuracy), `False` when minimised (e.g. MSE, AIRM). `curve_score()` is always lower-is-better regardless of this flag.

`igl.types.MatryoshkaSampler`

Bases: Protocol

Samples a truncation level k ∈ {1, …, d_max} per training step.

`igl.types.ExtraLoss`

Bases: Protocol

Optional regularizer that contributes a scalar term to the training loss.

Used by :class:igl.MatryoshkaTrainer to fold in geometry-specific penalties (e.g. the SPD orthogonality penalty from :class:igl.spd.OrthogonalityPenalty) without coupling the trainer to that geometry.

The trainer calls the extra loss once per batch (subject to :attr:every), adds weight * contribution to the task loss, and backpropagates through everything in one go. Implementations may return None to skip the contribution for this step (useful when the regularizer is only defined for k ≥ 2, for example).

Attributes:

Name	Type	Description
`weight`	`float`	Multiplier applied to the returned tensor before adding to the task loss.
`every`	`int`	Call frequency in batches (1 = every batch).

String enums

`igl.types.OperatorName`

Bases: StrEnum

Built-in kernel operator names. Canonical reference for kernel choices.

`igl.types.SamplingMode`

Bases: StrEnum

Truncation-level sampling strategies for Matryoshka training.

`igl.types.NormalizeMode`

Bases: StrEnum

Row-wise normalization applied to the design matrix Φ before lstsq.

NONE: identity.
SOFTMAX: row-softmax.
L2: L2-normalise each row.
NW: Nadaraya–Watson (divide each row by its sum, with a small epsilon).

`igl.types.NormType`

Bases: StrEnum

Normalization layer inserted after each hidden Linear of an MLP encoder.

`igl.types.ActivationType`

Bases: StrEnum

Activation function applied after each (Linear → Norm) block.

`igl.types.EncoderKind`

Bases: StrEnum

Kind of encoder built by :func:igl.build_mlp_encoder / future factories.

`igl.types.SchedulerType`

Bases: StrEnum

Learning-rate scheduler choices for :class:igl.MatryoshkaTrainer.