mvpa2.misc.neighborhood.Sphere¶

class
mvpa2.misc.neighborhood.
Sphere
(radius, element_sizes=None, distance_func=None)¶ NDimensional hypersphere.
Use this if you want to obtain all the neighbors within a given radius from a point in a space with arbitrary number of dimensions assuming that the space is discrete.
No validation of producing coordinates within any extent is done.
Examples
Create a Sphere of diameter 1 and obtain all coordinates within range for the coordinate (1,1,1).
>>> s = Sphere(1) >>> s((2, 1)) [(1, 1), (2, 0), (2, 1), (2, 2), (3, 1)] >>> s((1, )) [(0,), (1,), (2,)]
If elements in discrete space have different sizes across dimensions, it might be preferable to specify element_sizes parameter.
>>> s = Sphere(2, element_sizes=(1.5, 2.5)) >>> s((2, 1)) [(1, 1), (2, 1), (3, 1)]
>>> s = Sphere(1, element_sizes=(1.5, 0.4)) >>> s((2, 1)) [(2, 1), (2, 0), (2, 1), (2, 2), (2, 3)]
Methods
train
(dataset)Initialize the Sphere
Parameters: radius : float
Radius of the ‘sphere’. If no
element_sizes
provided – radius would be effectively in number of voxels (if operating on MRI data).element_sizes : None or iterable of floats
Sizes of elements in each dimension. If None, it is equivalent to 1s in all dimensions.
distance_func : None or lambda
Distance function to use (choose one from
mvpa2.clfs.distance
). If None, cartesian_distance to be used.Methods
train
(dataset)
distance_func
¶

element_sizes
¶

radius
¶

train
(dataset)¶
