cuvis_ai.deciders.combining_decider.CombiningDecider

class cuvis_ai.deciders.combining_decider.CombiningDecider(channel_count: int | None = None, rule: Callable[[ndarray], bool] | None = None)[source]

Bases: BaseDecider

Decider using values of multiple channels to classify the result. The data of all channels at a spatial location are utilized in the chosen decision strategy to classify each data point.

Parameters:

channel_count (int) – The number of channels to expect
rule (Callable[[np.ndarray], bool]) – The decision strategy to use. all_agree() and at_least_n_agree() are provided here. Custom strategies may also be used.

__init__(channel_count: int | None = None, rule: Callable[[ndarray], bool] | None = None) → None[source]

Methods

`__init__`([channel_count, rule])
`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 the chosen :arg:`rule` to the input data. :param X: Data to classify. :type X: np.ndarray.
`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

fit(X)

forward(X: ndarray) → ndarray[source]

Apply the chosen :arg:`rule` to the input data. :param X: Data to classify. :type X: np.ndarray

Returns:: Data classified to a single channel boolean matrix.
Return type:: np.ndarray

get_fit_requested_meta()

get_forward_requested_meta()

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.

Returns: (tuple) needed shape for data

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.

Returns:: Provided shape for data
Return type:: tuple

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

set_fit_meta_request(**kwargs)

set_forward_meta_request(**kwargs)