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| author | Oleksii Kachaiev <kachayev@gmail.com> | 2023-05-03 10:36:09 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-05-03 10:36:09 +0200 |
| commit | 2aeb591be6b19a93f187516495ed15f1a47be925 (patch) | |
| tree | 9a6f759856a3f6b2d7c6db3514927ba3e5af10b5 /examples/gromov/plot_barycenter_fgw.py | |
| parent | 8a7035bdaa5bb164d1c16febbd83650d1fb6d393 (diff) | |
[DOC] Corrected spelling errors (#467)
* Fix typos in docstrings and examples
* A few more fixes
* Fix ref for `center_ot_dual` function
* Another typo
* Fix titles formatting
* Explicit empty line after math blocks
* Typo: asymmetric
* Fix code cell formatting for 1D barycenters
* Empirical
* Fix indentation for references
* Fixed all WARNINGs about title formatting
* Fix empty lines after math blocks
* Fix whitespace line
* Update changelog
* Consistent Gromov-Wasserstein
* More Gromov-Wasserstein consistency
---------
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'examples/gromov/plot_barycenter_fgw.py')
| -rw-r--r-- | examples/gromov/plot_barycenter_fgw.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py index dc3c6aa..3b5db8b 100644 --- a/examples/gromov/plot_barycenter_fgw.py +++ b/examples/gromov/plot_barycenter_fgw.py @@ -34,8 +34,8 @@ from ot.gromov import fgw_barycenters def find_thresh(C, inf=0.5, sup=3, step=10): """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + The threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjacency matrix and the original matrix. Parameters ---------- @@ -51,15 +51,15 @@ def find_thresh(C, inf=0.5, sup=3, step=10): dist = [] search = np.linspace(inf, sup, step) for thresh in search: - Cprime = sp_to_adjency(C, 0, thresh) + Cprime = sp_to_adjacency(C, 0, thresh) SC = shortest_path(Cprime, method='D') SC[SC == float('inf')] = 100 dist.append(np.linalg.norm(SC - C)) return search[np.argmin(dist)], dist -def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. +def sp_to_adjacency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjacency matrix. All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 Parameters ---------- @@ -174,7 +174,7 @@ A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) # ------------------------- #%% Create the barycenter -bary = nx.from_numpy_array(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) +bary = nx.from_numpy_array(sp_to_adjacency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) for i, v in enumerate(A.ravel()): bary.add_node(i, attr_name=v) |