tfg.math.interpolation.weighted.interpolate

Weighted interpolation for M-D point sets.

tfg.math.interpolation.weighted.interpolate(
    points: type_alias.TensorLike,
    weights: type_alias.TensorLike,
    indices: type_alias.TensorLike,
    normalize: bool = True,
    allow_negative_weights: bool = False,
    name: str = 'weighted_interpolate'
) -> type_alias.TensorLike

Given an M-D point set, this function can be used to generate a new point set that is formed by interpolating a subset of points in the set.

Note
In the following, A1 to An, and B1 to Bk are optional batch dimensions.

Args
points	A tensor with shape [B1, ..., Bk, M] and rank R > 1, where M is the dimensionality of the points.
weights	A tensor with shape [A1, ..., An, P], where P is the number of points to interpolate for each output point.
indices	A tensor of dtype tf.int32 and shape [A1, ..., An, P, R-1], which contains the point indices to be used for each output point. The R-1 dimensional axis gives the slice index of a single point in points. The first n+1 dimensions of weights and indices must match, or be broadcast compatible.
normalize	A bool describing whether or not to normalize the weights on the last axis.
allow_negative_weights	A bool describing whether or not negative weights are allowed.
name	A name for this op. Defaults to "weighted_interpolate".

Returns
A tensor of shape [A1, ..., An, M] storing the interpolated M-D points. The first n dimensions will be the same as weights and indices.