cann.data package

Submodules

cann.data.datasets module

class cann.data.datasets.ParamSurfaceDataset(*args, **kwargs)[source]

Bases: Dataset

Synthetic dataset for surrogate learning/inference.

Each sample is a tuple (θ_i, x_i, Q*(x_i; θ_i), w) where:

  • θ_i: Model parameters (e.g., rho, kappa) as a numpy array of shape (n,).

  • x_i: Market input features (e.g., moneyness, time to maturity) as a numpy array of shape (m,).

  • Q*(x_i; θ_i): Observed market value (e.g., implied volatility or option price) as a float.

  • w: Optional weight for the sample as a float.

    This dataclass stores the arrays directly as fields (parameters, market_inputs, and optional observed/weights) and prepares torch tensors in __post_init__ for efficient training.

market_inputs
model_cfg
property num_parameters

Number of model parameters (n).

property num_samples

Number of samples (N).

observed = None
parameters
weights = None

cann.data.market_surface module

class cann.data.market_surface.MarketSurface(market_inputs, observed, weights=None, metadata=None)[source]

Bases: object

Container for market surface data used in model calibration.

Fields:

market_inputs (shape (N, m)), observed (shape (N,) or (N, 1)), optional weights (shape (N,)), and optional metadata.

market_inputs
metadata = None
observed
weights = None

cann.data.samplers module

Module contents