TrainableDataAction#
- class TrainableDataAction(validate=True)[source]#
A base class for data actions that need training.
Note
The parameters of trainable nodes computed during training should be listed in the class method
TrainableDataAction._default_parameters()
. These parameters are initialized at construction time and serialized together with the constructor arguments. All parameters defined inTrainableDataAction._default_parameters()
should be assigned a None value to indicate that the node has not been trained.Parameter values can be updated with the
set_parameters()
method and refer to using theTrainableDataAction.parameters()
method. This is required to correctly JSON serialize and deserialize a trainable node with parameters set during training.Create new node.
- Parameters:
validate (bool) â If set to False the DataAction will not validate its input.
Attributes
- is_trained#
Return False if the DataAction needs to be trained.
A node is considered trained if all its parameters are assigned, or do not have
None
values.- Returns:
True if the data action has been trained.
- parameters#
Return the parameters of the trainable node.
Methods
- __call__(data)#
Call the data action of this node on the data.
- Parameters:
data (ndarray) â A numpy array with arbitrary dtype. If the elements are ufloat objects consisting of a nominal value and a standard error, then the error propagation is done automatically.
- Returns:
The processed data.
- Return type:
ndarray
- abstract train(data)[source]#
Train a DataAction.
Certain data processing nodes, such as a SVD, require data to first train.
- Parameters:
data (ndarray) â A data array for training. This is a single numpy array containing all circuit results input to the data processor
DataProcessor#train()
method.