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
)

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.

Args
`points`	A tensor with shape `[B1, ..., Bk, M] and rank R > 1, where M is the dimensionality of the points. </td> </tr><tr> <td>`weights`</td> <td> A tensor with shape`[A1, ..., An, P]`, where P is the number of points to interpolate for each output point. </td> </tr><tr> <td>`indices`</td> <td> 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. </td> </tr><tr> <td>`normalize`</td> <td> A`bool`describing whether or not to normalize the weights on the last axis. </td> </tr><tr> <td>`allow_negative_weights`</td> <td> A`bool`describing whether or not negative weights are allowed. </td> </tr><tr> <td>`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.

Args