mvpa2.mappers.fxΒΆ
Transform data by applying a function along samples or feature axis.
Functions
| absolute_features() | Returns a mapper that converts features into absolute values. |
| array_whereequal(a, x) | Reliable comparison for numpy.ndarray numpy.ndarray (as of 1.5.0.dev) fails to compare tuples in array of dtype object, e.g. |
| borrowdoc(cls[, methodname]) | Return a decorator to borrow docstring from another cls.`methodname` It should not be used for __init__ methods of classes derived from ClassWithCollections since __doc__’s of those are handled by the AttributeCollector anyways. |
| max_of_abs(x) | Max of absolute values along the 2nd axis |
| maxofabs_sample() | Returns a mapper that finds max of absolute values of all samples. |
| mean_feature([attrfx]) | Returns a mapper that computes the mean feature of a dataset. |
| mean_group_feature(attrs[, attrfx]) | Returns a mapper that computes the mean features of unique feature groups. |
| mean_group_sample(attrs[, attrfx]) | Returns a mapper that computes the mean samples of unique sample groups. |
| mean_sample([attrfx]) | Returns a mapper that computes the mean sample of a dataset. |
| sum_of_abs(x) | Sum of absolute values along the 2nd axis |
| sum_sample([attrfx]) | Returns a mapper that computes the sum sample of a dataset. |
| sumofabs_sample() | Returns a mapper that returns the sum of absolute values of all samples. |
Classes
| BinaryFxNode(fx, space, **kwargs) | Extract a dataset attribute and call a function with it and the samples. |
| Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
| FxMapper(axis, fx[, fxargs, uattrs, attrfx]) | Apply a custom transformation to (groups of) samples or features. |
| Mapper(**kwargs) | Basic mapper interface definition. |
| Node([space, postproc]) | Common processing object. |
