jax.numpy.diagflat¶

jax.numpy.diagflat(v, k=0)[source]¶

Create a two-dimensional array with the flattened input as a diagonal.

LAX-backend implementation of diagflat(). This differs from np.diagflat for some scalar values of v, jax always returns a two-dimensional array, whereas numpy may return a scalar depending on the type of v.

Original docstring below.

Parameters

v (array_like) – Input data, which is flattened and set as the k-th diagonal of the output.
k (int, optional) – Diagonal to set; 0, the default, corresponds to the “main” diagonal, a positive (negative) k giving the number of the diagonal above (below) the main.

Returns

out – The 2-D output array.

Return type

ndarray

See also

diag(): MATLAB work-alike for 1-D and 2-D arrays.
diagonal(): Return specified diagonals.
trace(): Sum along diagonals.

Examples

>>> np.diagflat([[1,2], [3,4]])
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0],
       [0, 0, 0, 4]])

>>> np.diagflat([1,2], 1)
array([[0, 1, 0],
       [0, 0, 2],
       [0, 0, 0]])