tf.linalg.band_part

Copy a tensor setting everything outside a central band in each innermost matrix to zero.

tf.linalg.band_part(
    input: _atypes.TensorFuzzingAnnotation[TV_MatrixBandPart_T],
    num_lower: _atypes.TensorFuzzingAnnotation[TV_MatrixBandPart_Tindex],
    num_upper: _atypes.TensorFuzzingAnnotation[TV_MatrixBandPart_Tindex],
    name=None
) -> _atypes.TensorFuzzingAnnotation[TV_MatrixBandPart_T]

The band part is computed as follows: Assume input has k dimensions [I, J, K, ..., M, N], then the output is a tensor with the same shape where

band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n].

The indicator function

in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && (num_upper < 0 || (n-m) <= num_upper).

For example:

# if 'input' is [[ 0,  1,  2, 3]
#                [-1,  0,  1, 2]
#                [-2, -1,  0, 1]
#                [-3, -2, -1, 0]],

tf.linalg.band_part(input, 1, -1) ==> [[ 0,  1,  2, 3]
                                       [-1,  0,  1, 2]
                                       [ 0, -1,  0, 1]
                                       [ 0,  0, -1, 0]],

tf.linalg.band_part(input, 2, 1) ==> [[ 0,  1,  0, 0]
                                      [-1,  0,  1, 0]
                                      [-2, -1,  0, 1]
                                      [ 0, -2, -1, 0]]

Useful special cases:

 tf.linalg.band_part(input, 0, -1) ==> Upper triangular part.
 tf.linalg.band_part(input, -1, 0) ==> Lower triangular part.
 tf.linalg.band_part(input, 0, 0) ==> Diagonal.

input A Tensor. Rank k tensor.
num_lower A Tensor. Must be one of the following types: int32, int64. 0-D tensor. Number of subdiagonals to keep. If negative, keep entire lower triangle.
num_upper A Tensor. Must have the same type as num_lower. 0-D tensor. Number of superdiagonals to keep. If negative, keep entire upper triangle.
name A name for the operation (optional).

A Tensor. Has the same type as input.