topological-data-analysis
Here are 98 public repositories matching this topic...
Currently, the color palette is hardcoded in visuals.py:
https://github.com/MLWave/kepler-mapper/blob/0dfa7f736bd4218e8996888785b9d2adc63d5344/kmapper/visuals.py#L8-L15
and again in kmapper.js
I'd like to see this
- duplication fixed and
- allow the palette
Test code:
from sklearn.datasets import load_digits
from gtda.homology import CubicalPersistence
from gtda.diagrams import PairwiseDistance
X, _ = load_digits(return_X_y=True)
nb_samples = 100
CP = CubicalPersistence(homology_dimensions=(0, 1))
X_diag = CP.fit_transform(X[:nb_samples].reshape(nb_samples, 8, 8))
PD = PairwiseDistance(
metric='bottleneck', metric_params={'delta'
-
Updated
Jul 10, 2020 - C++
Each project has a set of example scripts, it looks like Sphinx-gallery makes displaying rendering these examples look very nice.
-
Updated
Feb 20, 2019 - Python
-
Updated
Jun 24, 2020 - C++
In Representative Cocycles tutorial, this code block
result = ripser(x, coeff=17, do_cocycles=True)
diagrams = result['dgms']
cocycles = result['cocycles']
D = result['dm']
raises a KeyError: 'dm' since returned object doesn't have the key. The available keys are:
result.keys()
# ['dgms', 'cocycles', 'num_edges', 'dperm2all', 'idx_perm', 'r_cover']
It could be useful to have a way to clone a simplex tree, more efficiently than a loop on get_filtration doing inserts (we have a copy constructor in C++).
-
Updated
Jun 10, 2020 - C++
link from python FAQ
-
Updated
Jul 8, 2018 - Scala
-
Updated
Jul 12, 2018 - Python
Magnus Botnan is running RIVET on OS X in dark mode and reports that the numbers in the GUI's text boxes (for example, for slope and offset) are shown as white; but that background for the boxes is also white, so that the numbers are invisible.
<img width="800" alt="Screen Shot 2020-01-19 at 12 22 43 PM" src="https://user-images.githubusercontent.com/12559620/72685271-76370e80-3ab6-11ea-8409-f1
-
Updated
Nov 11, 2019 - Python
-
Updated
Jul 16, 2018 - Python
Need to adjust documentation to reflect above fact. Potentially add parameter in permutation test function to allow user to pick their own distance function (takes persistent homology of two datasets as parameters, returns numeric).
Thanks to @kisungyou for bringing this to my attention.
-
Updated
Jun 15, 2020
-
Updated
Jan 28, 2019 - Jupyter Notebook
-
Updated
Mar 27, 2019 - Julia
-
Updated
Sep 28, 2019 - Haskell
-
Updated
Apr 15, 2020 - Jupyter Notebook
-
Updated
Jan 18, 2020 - Python
-
Updated
Apr 8, 2020 - Python
-
Updated
May 16, 2017 - Scala
-
Updated
Apr 25, 2019 - Cuda
-
Updated
Apr 4, 2019 - Python
-
Updated
Feb 29, 2020
So far we have to build docs locally and push them to gh-pages.
Wanted new behavior:
-
docs are updated automatically with each push of
master -
there remains a possobility to build docs locally in some gitignored folder for testing befor pushing
@rkwitt Research if u want to. i'll do the coding.
Improve this page
Add a description, image, and links to the topological-data-analysis topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the topological-data-analysis topic, visit your repo's landing page and select "manage topics."
Following up on the discussion here, it would be good to document how to get reproducible results with UMAP.
I think we should consider changing
random_statein the UMAP constructor to a seed (e.g. 42, like the newtransform_seeddefault) so that UMAP is reproducible by default.We should document that users can set `ran