cuvis_ai.deciders.base_decider.BaseDecider

class cuvis_ai.deciders.base_decider.BaseDecider[source]

Abstract class for Decision Making Nodes.

The decider nodes transform a prediction state into a final prediction based on the task that needs to be accomplished.

Methods

`__init__`()
`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)	Predict labels based on the input labels.
`get_fit_requested_meta`()
`get_forward_requested_meta`()
`load`()	Load from serialized format into an object
`serialize`()	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 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

abstract forward(X)[source]

Predict labels based on the input labels.

abstract property input_dim: tuple[int, int, int]

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

Returns: (tuple) needed shape for data

abstract load() → None[source]: Load from serialized format into an object

abstract property output_dim: tuple[int, int, int]

Returns the shape for the output data. If a dimension is dependent on the input, it will return -1 in the specific position.

Returns: (tuple) expected output shape for data

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