pyhgf.updates.prediction_error.nodes.dirichlet.get_candidate#
- pyhgf.updates.prediction_error.nodes.dirichlet.get_candidate(value: float, sensory_precision: float, expected_mean: Array | ndarray | bool_ | number | bool | int | float | complex, expected_sigma: Array | ndarray | bool_ | number | bool | int | float | complex, n_samples: int = 20000) Tuple[float, float][source]#
Find the best cluster candidate given previous clusters and an input value.
- Parameters:
- value
The new observation.
- sensory_precision
The expected precision of the new observation.
- expected_mean
The mean of the existing clusters.
- expected_sigma
The standard deviation of the existing clusters.
- n_samples
The number of samples that should be simulated.
- Returns:
- mean
The mean of the new candidate cluster.
- sigma
The standard deviation of the new candidate cluster.