Prediction Models

Implements a variety of prediction models.

ia.gaius.prediction_models.average_emotives(record)

Averages the emotives in a list (e.g. predictions ensemble or percepts). The emotives in the record are of type: [{‘e1’: 4, ‘e2’: 5}, {‘e2’: 6}, {‘e1’: 5 ‘e3’: -4}]

Parameters:: record (list) – List of emotive dictionaries to average emotives of
Returns:: Dictionary of Averaged Emotives
Return type:: dict

Example

from ia.gaius.prediction_models import average_emotives
record = [{'e1': 4, 'e2': 5}, {'e2': 6}, {'e1': 5 'e3': -4}]
averages = average_emotives(record=record)

ia.gaius.prediction_models.bucket_predictions(ensemble)

ia.gaius.prediction_models.hive_model_classification(ensembles)

Compute the “hive predicted model classification” based on the ensembles provided from each node

Parameters:: ensembles (dict) – should be dictionary of { node_name: prediction_ensemble }
Returns:: hive predicted classification
Return type:: str

ia.gaius.prediction_models.hive_model_emotives(ensembles)

Compute average of emotives in model by calling average_emotives() on ensembles

Parameters:: ensembles (list) – Prediction ensemble to compute average emotives of
Returns:: _description_
Return type:: _type_

ia.gaius.prediction_models.make_modeled_emotives(ensemble)

The emotives in the ensemble are of type: ‘emotives’:[{‘e1’: 4, ‘e2’: 5}, {‘e2’: 6}, {‘e1’: 5 ‘e3’: -4}] First calls average_emotives() on each prediction in the ensemble, then calls bucket_predictions() on the ensemble. After bucketing predictions, the function model_per_emotives() is called for each emotive present in the ensemble. Dict returned contains { emotive: model_per_emotive } for each emotive in the ensemble

Parameters:: ensemble (list) – Prediction ensemble containing emotives to model
Returns:: Dictionary of modelled emotive values
Return type:: dict

ia.gaius.prediction_models.model_per_emotive(ensemble, emotive, potential_normalization_factor)

Using a Weighted Moving Average, though the ‘moving’ part refers to the prediction index.

Parameters:

ensemble (list, required) – The prediction ensemble to use in computations
emotive (_type_) – The emotive to compute a moving average of
potential_normalization_factor (_type_) – Divisor to normailize the potential value of each prediction

Returns:

_description_

Return type:

_type_

ia.gaius.prediction_models.prediction_ensemble_model_classification(ensemble): For classifications, we don’t bother with marginal_probability because classifications are discrete symbols, not numeric values.

ia.gaius.prediction_models.principal_delta(principal, other, potential)