# Initialization Methods – catsim.initialization¶

All implemented classes in this module inherit from a base abstract class Initializer. Simulator allows that a custom initializer be used during the simulation, as long as it also inherits from Initializer.

class catsim.initialization.FixedPointInitializer(start: float)[source]

Initializes every proficiency at the same point.

initialize(index: int = None, **kwargs)float[source]

Returns the same proficiency value that was passed to the constructor of the initializer

Parameters

index – the index of the current examinee. This parameter is not used by this method.

Returns

the same proficiency value that was passed to the constructor of the initializer

class catsim.initialization.RandomInitializer(dist_type: str = 'uniform', dist_params: tuple = - 5, 5)[source]

Randomly initializes the first estimate of an examinee’s proficiency

Parameters
• dist_type – either uniform or normal

• dist_params – a tuple containing minimum and maximum values for the uniform distribution (in no particular order) or the average and standard deviation values for the normal distribution (in this particular order).

initialize(index: int = None, **kwargs)float[source]

Generates a value using the chosen distribution and parameters

Parameters

index – the index of the current examinee. This parameter is not used by this method.

Returns

a proficiency value generated from the chosen distribution using the passed parameters