For each constituent norm, set *vmin*, *vmax* to min, max of the corresponding component in *A*. Parameters ---------- A : array-like The input data, as an iterable or a structured numpy array. - If iterable, must be of length `n_com
(self, A)
| 3530 | return result |
| 3531 | |
| 3532 | def autoscale(self, A): |
| 3533 | """ |
| 3534 | For each constituent norm, set *vmin*, *vmax* to min, max of the corresponding |
| 3535 | component in *A*. |
| 3536 | |
| 3537 | Parameters |
| 3538 | ---------- |
| 3539 | A : array-like |
| 3540 | The input data, as an iterable or a structured numpy array. |
| 3541 | |
| 3542 | - If iterable, must be of length `n_components`. Each element |
| 3543 | is used for the limits of one constituent norm. |
| 3544 | - If structured array, must have `n_components` fields. Each field |
| 3545 | is used for the limits of one constituent norm. |
| 3546 | """ |
| 3547 | with self.callbacks.blocked(): |
| 3548 | A = self._iterable_components_in_data(A, self.n_components) |
| 3549 | for n, a in zip(self.norms, A): |
| 3550 | n.autoscale(a) |
| 3551 | self._changed() |
| 3552 | |
| 3553 | def autoscale_None(self, A): |
| 3554 | """ |