DataInt

class aixd.data.DataInt(name: str, dim: int = 1, domain: Interval | None = None, unit: str | None = None, position: int | None = None, position_index: int | None = None, transformations: List[str] | List[DataObjectTransform] | None = None, dtype: str = 'int64', flag_split_perdim: bool = False)[source]

Integer data type.

Parameters:

name (str) – Name of the data object.
dim (int, optional, default=1) – Dimensionality of the data object, or different columns to perform the split on.
domain (Interval, optional, default=None) – Domain of the data object.
unit (str, optional, default=None) – Unit of the data object (e.g. m, kg. m^2, m/s^2 etc.). Use ^ to indicate powers (e.g. m^2), and _ to indicate subscripts (e.g. m_1).
position (int, optional, default=None) – Position of the data object in the vector.
position_index (int, optional, default=None) – Index of the data object in the vector.
transformations (Union[List[str], List[DataObjectTransform]], optional, default=None) – List of transformations to be applied to the data object.
dtype (str, optional, default=”int64”) – The dtype of the numpy are of the expected data matrix.
flag_split_perdim (bool, optional, default=False) – If True, object is split across dimensions in a DataBlock.

Methods

`from_range`	Class method to initialization a DataInt from a list of integers.
`get_activation_outhead`	Tries to interfere the activation function for the output head, based on the last transformation.
`get_loss_evaluation`	Returns the loss function for approximating this feature.
`get_ml_heads`	Returns a fully-connected head for encoding and decoding this feature
`get_objective`	Returns the loss function for approximating this feature.

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.
`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.