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<a href="https://moldyn.github.io/NorMI/faq">FAQ</a>
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# NorMI: Nonparametric Normalized Mutual Information Estimator Based on *k*-NN Statistics
This software provides an extension to the Kraskov-Estimator to allow normalizing the mutual information.
The method will be published soon as:
> **Adaptive Entropy-Based Normalization for (High-Dimensional) Mutual Information**
> D. Nagel, G. Diez, and G. Stock, in prep.
If you use this software package, please cite the above mentioned paper.
## Features
- Intuitive usage via [module](#module---inside-a-python-script) and via [CI](#ci---usage-directly-from-the-command-line)
- Sklearn-style API for fast integration into your Python workflow
- No magic, only a single parameter which can be optimized via cross-validation
- Extensive [documentation](https://moldyn.github.io/NorMI) and detailed discussion in publication
## Installation
The package is called `normi` and is available via [PyPI](https://pypi.org/project/normi) or [conda](https://anaconda.org/conda-forge/normi). To install it, simply call:
```bash
python3 -m pip install --upgrade normi
```
or
```
conda install -c conda-forge normi
```
or for the latest dev version
```bash
# via ssh key
python3 -m pip install git+ssh://git@github.com/moldyn/NorMI.git
# or via password-based login
python3 -m pip install git+https://github.com/moldyn/NorMI.git
```
### Shell Completion
Using the `bash`, `zsh` or `fish` shell click provides an easy way to provide shell completion, checkout the [docs](https://click.palletsprojects.com/en/8.0.x/shell-completion).
In the case of bash you need to add following line to your `~/.bashrc`
```bash
eval "$(_NORMI_COMPLETE=bash_source normi)"
```
## Usage
In general one can call the module directly by its entry point `$ normi` or by calling the module `$ python -m normi`. The latter method is preferred to ensure using the desired python environment. For enabling the shell completion, the entry point needs to be used.
### CI - Usage Directly from the Command Line
The module brings a rich CI using [click](https://click.palletsprojects.com).
For a complete list of all options please see the
[docs](https://moldyn.github.io/NorMI/reference/cli/).
```bash
python -m normi /
--input input_file / # ascii file of shape (n_samples, n_features)
--output output_file / # creates ascii file of shape (n_features, n_features)
--n-dims / # this allows to treat every n_dims columns as a high dimensional feature
--verbose
```
### Module - Inside a Python Script
```python
from normi import NormalizedMI
# Load file
# X is np.ndarray of shape (n_samples, n_features)
nmi = NormalizedMI()
nmi_matrix = nmi.fit_transform(X)
...
```
## Credits
- Logo generated with DALL·E 3 by @gegabo
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Nagel, G. Diez, and G. Stock, in prep.\n\nIf you use this software package, please cite the above mentioned paper.\n\n## Features\n- Intuitive usage via [module](#module---inside-a-python-script) and via [CI](#ci---usage-directly-from-the-command-line)\n- Sklearn-style API for fast integration into your Python workflow\n- No magic, only a single parameter which can be optimized via cross-validation\n- Extensive [documentation](https://moldyn.github.io/NorMI) and detailed discussion in publication\n\n## Installation\nThe package is called `normi` and is available via [PyPI](https://pypi.org/project/normi) or [conda](https://anaconda.org/conda-forge/normi). 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For enabling the shell completion, the entry point needs to be used.\n\n### CI - Usage Directly from the Command Line\nThe module brings a rich CI using [click](https://click.palletsprojects.com).\nFor a complete list of all options please see the\n[docs](https://moldyn.github.io/NorMI/reference/cli/).\n```bash\npython -m normi /\n --input input_file / # ascii file of shape (n_samples, n_features)\n --output output_file / # creates ascii file of shape (n_features, n_features)\n --n-dims / # this allows to treat every n_dims columns as a high dimensional feature\n --verbose\n\n```\n\n### Module - Inside a Python Script\n```python\nfrom normi import NormalizedMI\n\n# Load file\n# X is np.ndarray of shape (n_samples, n_features)\n\nnmi = NormalizedMI()\nnmi_matrix = nmi.fit_transform(X)\n...\n```\n\n## Credits\n\n- Logo generated with DALL\u00b7E 3 by @gegabo\n",
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