https://github.com/scikit-learn-contrib/scikit-dimension

A Python package for intrinsic dimension estimation
https://github.com/scikit-learn-contrib/scikit-dimension

Last synced: 8 months ago
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A Python package for intrinsic dimension estimation

• Host: GitHub
• URL: https://github.com/scikit-learn-contrib/scikit-dimension
• Owner: scikit-learn-contrib
• License: bsd-3-clause
• Created: 2019-12-28T10:01:51.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2025-02-10T14:32:25.000Z (10 months ago)
• Last Synced: 2025-04-03T11:16:21.243Z (9 months ago)
• Language: Python
• Homepage: https://scikit-dimension.readthedocs.io
• Size: 18 MB
• Stars: 85
• Watchers: 5
• Forks: 18
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# scikit-dimension

scikit-dimension is a Python module for intrinsic dimension estimation built according to the [scikit-learn](https://github.com/scikit-learn/scikit-learn) API and distributed under the 3-Clause BSD license.

Please refer to the [documentation](https://scikit-dimension.readthedocs.io) and the [paper](https://www.mdpi.com/1099-4300/23/10/1368) for detailed API, examples and references

### Installation

Using pip:

```bash

pip install scikit-dimension

```

From source:

```bash

git clone https://github.com/j-bac/scikit-dimension

cd scikit-dimension

pip install .

```

 

### Quick start

Local and global estimators can be used in this way:

```python

import skdim

import numpy as np

#generate data : np.array (n_points x n_dim). Here a uniformly sampled 5-ball embedded in 10 dimensions

data = np.zeros((1000,10))

data[:,:5] = skdim.datasets.hyperBall(n = 1000, d = 5, radius = 1, random_state = 0)

#estimate global intrinsic dimension

danco = skdim.id.DANCo().fit(data)

#estimate local intrinsic dimension (dimension in k-nearest-neighborhoods around each point):

lpca = skdim.id.lPCA().fit_pw(data,

                              n_neighbors = 100,

                              n_jobs = 1)

                            

#get estimated intrinsic dimension

print(danco.dimension_, np.mean(lpca.dimension_pw_)) 

```