Weighted interpolation for M-D point sets.
tfg.math.interpolation.weighted.interpolate( points, weights, indices, normalize=True, allow_negative_weights=False, name=None )
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.
In the following, A1 to An, and B1 to Bk are optional batch dimensions.
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.
booldescribing whether or not to normalize the weights on the last axis.
booldescribing whether or not negative weights are allowed.
name: A name for this op. Defaults to "weighted_interpolate".
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.