DataReal

class aixd.data.DataReal(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 = 'float64', flag_split_perdim: bool = False)[source]

Bases: DataObject

Real 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=”float64”) – 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 DataReal from a range defined by vmin and vmax.

get_activation_outhead

Tries to interfere the activation function for the output head, based on the last transformation.

get_loss_evaluation

Returns the evaluation loss function for 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.

plot_distrib

Plots the distribution of the passed data as a Histogram.

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.

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.