torchsig.transforms.functional.normalize¶

torchsig.transforms.functional.normalize(data: ndarray, norm_order: float | Literal['fro', 'nuc'] | None = 2, flatten: bool = False) → ndarray[source]¶

Scale data so that a specified norm computes to 1.

For detailed information, see numpy.linalg.norm.()

For norm=1, norm = max(sum(abs(x), axis=0)) (sum of the elements)
for norm=2, norm = sqrt(sum(abs(x)^2), axis=0) (square-root of the sum of squares)
for norm=np.inf, norm = max(sum(abs(x), axis=1)) (largest absolute value)

Parameters:

data – (batch_size, vector_length, …)-sized data to be normalized.
norm_order – norm order to be passed to np.linalg.norm. Defaults to 2.
flatten – boolean specifying if the input array’s norm should be calculated on the flattened representation of the input data. Defaults to False.

Returns:

Normalized complex array data.