pyhgf.updates.prediction_error.dirichlet.get_candidate#
- pyhgf.updates.prediction_error.dirichlet.get_candidate(value, sensory_precision, expected_mean, expected_sigma, n_samples=20000)[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.
- Parameters:
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)
- Return type:
Tuple[float, float]