RandomNormal¶
Predicts random values sampled from a normal distribution.
The parameters of the normal distribution are fitted with running statistics. This is equivalent to using surprise.prediction_algorithms.random_pred.NormalPredictor
.
This model expects a dict input with a user
and an item
entries without any type constraint on their values (i.e. can be strings or numbers). Other entries are ignored.
Parameters¶
-
seed – defaults to
None
Randomization seed used for reproducibility.
Attributes¶
-
mean
stats.Mean
-
variance
stats.Var
Examples¶
>>> from river import reco
>>> dataset = (
... ({'user': 'Alice', 'item': 'Superman'}, 8),
... ({'user': 'Alice', 'item': 'Terminator'}, 9),
... ({'user': 'Alice', 'item': 'Star Wars'}, 8),
... ({'user': 'Alice', 'item': 'Notting Hill'}, 2),
... ({'user': 'Alice', 'item': 'Harry Potter'}, 5),
... ({'user': 'Bob', 'item': 'Superman'}, 8),
... ({'user': 'Bob', 'item': 'Terminator'}, 9),
... ({'user': 'Bob', 'item': 'Star Wars'}, 8),
... ({'user': 'Bob', 'item': 'Notting Hill'}, 2)
... )
>>> model = reco.RandomNormal(seed=42)
>>> for x, y in dataset:
... _ = model.learn_one(x, y)
>>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter'})
6.883895
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_one
Fits to a set of features x
and a real-valued target y
.
Parameters
- x (dict)
- y (numbers.Number)
Returns
Regressor: self
predict_one
Predicts the target value of a set of features x
.
Parameters
- x (dict)
Returns
Number: The prediction.