Update Katz_FD #36
Update Katz_FD #36
Conversation
|
Thank you @PiethonProgram — I'll aim to review the PR later this week. In the meantime, please make sure that the CI tests and lint tests are all passing. |
|
Applied formatting changes and should pass lint cases. Changed self.assertEqual(np.round(katz_fd(x_k), 3), VALUE) from VALUE = 5.783 to 1.503 to reflect formula change |
antropy/fractal.py
Outdated
| # euclidian distance calculation | ||
| euclidean_distance = np.sqrt(1 + np.square(np.diff(x, axis=axis))) | ||
|
|
||
| # total and average path lengths | ||
| total_path_length = euclidean_distance.sum(axis=axis) | ||
| average_path_length = euclidean_distance.mean(axis=axis) | ||
|
|
||
| # max distance from first to all | ||
| horizontal_diffs = np.arange(1, x.shape[axis]) | ||
| vertical_diffs = np.take(x, indices=np.arange(1, x.shape[axis]), axis=axis) - np.take( | ||
| x, indices=[0], axis=axis | ||
| ) | ||
|
|
||
| if axis == 1: # reshape if needed | ||
| horizontal_diffs = horizontal_diffs.reshape(1, -1) | ||
| elif axis == 0: | ||
| horizontal_diffs = horizontal_diffs.reshape(-1, 1) | ||
|
|
||
| # Euclidean distance and max distance | ||
| distances = np.sqrt(np.square(horizontal_diffs) + np.square(vertical_diffs)) | ||
| max_distance = np.max(distances, axis=axis) | ||
|
|
||
| # Katz Fractal Dimension Calculation | ||
| full_distance = np.log10(total_path_length / average_path_length) | ||
| kfd = np.squeeze(full_distance / (full_distance + np.log10(max_distance / total_path_length))) | ||
|
|
||
| # ensure scalar output |
There was a problem hiding this comment.
Hi @PiethonProgram,
I think that this implementation can be simplified, for example by following the proposed new implementation in: #34, or by leveraging the Neurokit2 implementation (which as of present gives the same output as Antropy): https://github.com/neuropsychology/NeuroKit/blob/45c9ad90d863ebf4e9d043b975a10d9f8fdeb06b/neurokit2/complexity/fractal_katz.py#L6
There was a problem hiding this comment.
Sounds good, I will make the adjustments.
There was a problem hiding this comment.
Hello, I have taken a look at the implementations you mentioned. Are you sure it can be simplified? Previous implementations that you mentioned are shorter since they are all single-channel.
If you want to only offer single-channel feature extraction then I can make the changes, but otherwise, unless you want to try and decrease time using Numba, I don't think there is much I can "simplify."
There was a problem hiding this comment.
Good point about the support for ND arrays. I have not yet found the time to do a deep dive into this, but can we just take the existing implementation of Antropy (see below) and replace the distance calculation by the Euclidean distance?
dists = np.abs(np.diff(x, axis=axis))
ll = dists.sum(axis=axis)
ln = np.log10(ll / dists.mean(axis=axis))
aux_d = x - np.take(x, indices=[0], axis=axis)
d = np.max(np.abs(aux_d), axis=axis)
kfd = np.squeeze(ln / (ln + np.log10(d / ll)))or is there more to your implementation that I'm missing?
Thank you
There was a problem hiding this comment.
I think the essence of the code is the same, but some additional "bits" are needed when using Euclidean distance in n-dimensions in order to check for distances from one to the other.
If we were only speaking in 1-dimension, then yes, we can simply just replace the distance calculation line.
|
Should I be concerned with the unsuccessful checks : It seems the issue is related to GitHub versions, and not the code itself. |
|
Yeah don't worry about the CI failures, I need to make some upgrade to the GitHub Actions workflow. Thanks! |
|
Reformatted and "simplified" code. Note : Black formatting caused line 208 in fractal.py to expand into 7 lines (likely due to nested parenthesis restrictions) <= No impact on functions, just aesthetics. |
Fixed Katz_FD implementation by utilizing Euclidean distances. Also modified test cases to include tests for new method.