cuvis_ai.deciders.binary_decider.BinaryDecider

class cuvis_ai.deciders.binary_decider.BinaryDecider(threshold: float = 1.0)[source]

Simple decider node using a static threshold to classify data.

Parameters:: threshold (Any) – The threshold to use for classification: result = (input >= threshold)

Methods

`__init__`([threshold])
`check_input_dim`(X)	Check that the parameters for the input data data match user expectations
`check_output_dim`(X)	Check that the parameters for the output data data match user expectations
`fit`(X)
`forward`(X)	Apply binary decision on input data.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`(params, filepath)	Load this node from a serialized graph.
`serialize`(directory)	Convert the class into a serialized representation
`set_fit_meta_request`(**kwargs)
`set_forward_meta_request`(**kwargs)

Attributes

`input_dim`	Returns the needed shape for the input data.
`output_dim`	Returns the provided shape for the output data.

check_input_dim(X)

Check that the parameters for the input data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

check_output_dim(X)

Check that the parameters for the output data data match user expectations

Parameters: X (array-like): Input data.

Returns: (Bool) Valid data

Apply binary decision on input data.

Paramaters

returns:: Classified input data. Where the datapoints are False if smaller than the threshold or True if larger or equal.
rtype:: np.ndarray

property input_dim

Returns the needed shape for the input data. If a dimension is not important it will return -1 in the specific position.

load(params: dict, filepath: str)[source]: Load this node from a serialized graph.

property output_dim

Returns the provided shape for the output data. If a dimension is not important it will return -1 in the specific position.

serialize(directory: str)[source]: Convert the class into a serialized representation