tfg.math.interpolation.weighted.interpolate

Weighted interpolation for M-D point sets.

tfg.math.interpolation.weighted.interpolate(
    points,
    weights,
    indices,
    normalize=True,
    allow_negative_weights=False,
    name=None
)

Defined in math/interpolation/weighted.py.

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.