c3.libraries.sampling
¶
Functions to select samples from a dataset by various criteria.
Module Contents¶
- c3.libraries.sampling.all(learn_from, batch_size)[source]¶
Return all points.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
All indeces.
- Return type
list
- c3.libraries.sampling.from_start(learn_from, batch_size)[source]¶
Select from the beginning.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.from_end(learn_from, batch_size)[source]¶
Select from the end.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.even(learn_from, batch_size)[source]¶
Select evenly distanced samples across the set.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.random_sample(learn_from, batch_size)[source]¶
Select randomly.
- Parameters
learn_from (list) – List of data points.
batch_size (int) – Number of points to select.
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.high_std(learn_from, batch_size)[source]¶
Select points that have a high ratio of standard deviation to mean. Sampling from ORBIT data, points with a high std have the most coherent error, thus might be suitable for model learning. This has yet to be confirmed beyond anecdotal observation.
- Parameters
learn_from (list) – List of data points.
batch_size (int) – Number of points to select.
- Returns
Selected indices.
- Return type
list