HSGPBase

pydantic model pymc_marketing.mmm.hsgp.HSGPBase

Shared logic between HSGP and HSGPPeriodic.

Methods

HSGPBase.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
HSGPBase.create_variable(name[, xdist])	Create a variable from configuration.
HSGPBase.deterministics_to_replace(name)	Name of the Deterministic variables that are replaced with pm.Flat for out-of-sample.
HSGPBase.plot_curve(curve[, n_samples, ...])	Plot the curve.
HSGPBase.register_data(X)	Register the data to be used in the model.
HSGPBase.sample_prior([coords])	Sample from the prior distribution.
HSGPBase.to_dict()	Convert the object to a dictionary.

field X: InstanceOf[XTensorVariable] | InstanceOf[DataArray] | InstanceOf[np.ndarray] | None = None

The data to be used in the model

field X_mid: float | None = None

The mean of the training data

field demeaned_basis: bool = False

Whether each basis has its mean subtracted from it.

field dims: Dims [Required]

The dimensions of the variable

field m: int [Required]

Number of basis functions

field transform: str | None = None

Optional transformation for the variable. Must be registered or from either pytensor.tensor or pymc.math namespaces.