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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.
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.
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