adaptive.IntegratorLearner#
- class adaptive.IntegratorLearner(*args, **kwargs)[source]#
Bases:
adaptive.learner.base_learner.BaseLearner
- property approximating_intervals#
- property err#
- property igral#
- loss(real=True)[source]#
Return the loss for the current state of the learner.
- Parameters
real (bool, default: True) – If False, return the “expected” loss, i.e. the loss including the as-yet unevaluated points (possibly by interpolation).
- property npoints#
Number of evaluated points.
- tell(point, value)[source]#
Tell the learner about a single value.
- Parameters
x (A value from the function domain) –
y (A value from the function image) –
- tell_pending()[source]#
Tell the learner that ‘x’ has been requested such that it’s not suggested again.
- to_dataframe(with_default_function_args: bool = True, function_prefix: str = 'function.', x_name: str = 'x', y_name: str = 'y') pandas.core.frame.DataFrame [source]#
Return the data as a
pandas.DataFrame
.- Parameters
with_default_function_args (bool, optional) – Include the
learner.function
’s default arguments as a column, by default Truefunction_prefix (str, optional) – Prefix to the
learner.function
’s default arguments’ names, by default “function.”seed_name (str, optional) – Name of the seed parameter, by default “seed”
x_name (str, optional) – Name of the input value, by default “x”
y_name (str, optional) – Name of the output value, by default “y”
- Returns
- Return type
pandas.DataFrame
- Raises
ImportError – If
pandas
is not installed.