Algorithm#

class tpcp.Algorithm[source]#

Base class for all algorithms.

All type-specific algorithm classes should inherit from this class and need to

overwrite _action_methods with the name of the actual action method of this class type
implement a stub for the action method

Attributes:

_action_methods: The name(s) of the action method used by the child class

Examples

>>> class MyAlgorithm(Algorithm):
...     _action_methods = "detect"
...
...     def detect(self, data):
...         return self

If you want to create an optimizable algorithm, add a self_optimize or (self_optimize_with_info) method to your class. We do not provide a separate base class for that, as we can make no assumptions about the call signature of your custom self_optimize method. If you need an “optimizable” version for a group of algorithms you are working with, create a custom OptimizableAlgorithm class or OptimizableAlgorithmMixing that is specific to your algorithm.

Methods

`clone`()	Create a new instance of the class with all parameters copied over.
`get_params`([deep])	Get parameters for this algorithm.
`set_params`(**params)	Set the parameters of this Algorithm.

__init__(*args, **kwargs)#

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns: