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 (float) – The new observation.
sensory_precision (float) – The expected precision of the new observation.
expected_mean (Array | ndarray | bool | number | bool | int | float | complex) – The mean of the existing clusters.
expected_sigma (Array | ndarray | bool | number | bool | int | float | complex) – The standard deviation of the existing clusters.
n_samples (int) – 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.
- Return type: