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 = &#x27;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.

tfg.math.interpolation.weighted.interpolate

Note

Args

Returns