pyhgf.updates.prediction_error.dirichlet.get_candidate

pyhgf.updates.prediction_error.dirichlet.get_candidate(value, sensory_precision, expected_mean, expected_sigma, n_samples=20000)

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:

tuple[float, float]