DataBool

class aixd.data.DataBool(name: str, dim: int = 1, **kwargs)[source]

Bases: DataCategorical

Boolean type, i.e., categorical type with options ‘True’, ‘False’

Parameters:
  • name (str) – Name of the data object.

  • dim (int) – Dimensionality of the data object, or different columns to perform the split on.

  • kwargs – Additional keyword arguments passed to DataCategorical.

Inherited Methods

append_transformation

Adds the transformation at the end.

check_data_consistency

Check if the data is consistent with the defined domain.

copy

Returns a copy of the data object.

from_options

Class method to for initialization from list of integers

get_activation_outhead

Returns the activation function for approximating this feature.

get_loss_evaluation

Returns the loss function for approximating this feature.

get_ml_heads

Returns a fully-connected head with the appropriate number of in / out channels for encoding / decoding this feature

get_objective

Returns the loss function for approximating this feature.

grid_samples

Returns grid samples from the domain of the data object.

has_name

Returns if the passed name match with the name of the DataObject.

inverse_transform

Inverse transformation of the data matrix according to specification DataObject.transformations.

is_data_transformed

Simple heuristic to determine if the data is transformed.

plot_distrib

Plots the distribution of the passed data as a Barplot.

prepend_transformation

Adds the transformation at the start.

print_transf_norm

Prints an overview of the defined transformations.

random_samples

Returns random samples from the domain of the data object.

sample_around

Returns samples from the domain of the data object around the given centroid.

transform

Transforms the data matrix according to specification DataObject.transformations.

transform_is_fitted

Check if all transformations are fitted.

update_dobj_types

update_obj

Updates the domain of the data object with the passed data.