# R2LogR2RBF¶

class menpo.transform.R2LogR2RBF(c)[source]

Bases: RadialBasisFunction

The $$r^2 \log{r^2}$$ basis function.

The derivative of this function is $$2 r (\log{r^2} + 1)$$.

Note

$$r = \lVert x - c \rVert$$

Parameters: c ((n_centres, n_dims) ndarray) – The set of centers that make the basis. Usually represents a set of source landmarks.
apply(x, batch_size=None, **kwargs)

Applies this transform to x.

If x is Transformable, x will be handed this transform object to transform itself non-destructively (a transformed copy of the object will be returned).

If not, x is assumed to be an ndarray. The transformation will be non-destructive, returning the transformed version.

Any kwargs will be passed to the specific transform _apply() method.

Parameters: x (Transformable or (n_points, n_dims) ndarray) – The array or object to be transformed. batch_size (int, optional) – If not None, this determines how many items from the numpy array will be passed through the transform at a time. This is useful for operations that require large intermediate matrices to be computed. kwargs (dict) – Passed through to _apply(). transformed (type(x)) – The transformed object or array
apply_inplace(*args, **kwargs)

Deprecated as public supported API, use the non-mutating apply() instead.

For internal performance-specific uses, see _apply_inplace().

compose_after(transform)

Returns a TransformChain that represents this transform composed after the given transform:

c = a.compose_after(b)
c.apply(p) == a.apply(b.apply(p))


a and b are left unchanged.

This corresponds to the usual mathematical formalism for the compose operator, o.

Parameters: transform (Transform) – Transform to be applied before self transform (TransformChain) – The resulting transform chain.
compose_before(transform)

Returns a TransformChain that represents this transform composed before the given transform:

c = a.compose_before(b)
c.apply(p) == b.apply(a.apply(p))


a and b are left unchanged.

Parameters: transform (Transform) – Transform to be applied after self transform (TransformChain) – The resulting transform chain.
copy()

Generate an efficient copy of this object.

Note that Numpy arrays and other Copyable objects on self will be deeply copied. Dictionaries and sets will be shallow copied, and everything else will be assigned (no copy will be made).

Classes that store state other than numpy arrays and immutable types should overwrite this method to ensure all state is copied.

Returns: type(self) – A copy of this object
n_centres

The number of centres.

Type: int
n_dims

The RBF can only be applied on points with the same dimensionality as the centres.

Type: int
n_dims_output

The result of the transform has a dimension (weight) for every centre.

Type: int