Metadata-Version: 2.1
Name: robustgmm
Version: 1.0.2
Summary: A robust EM clustering algorithm for Gaussian mixture models
Home-page: https://github.com/HongJea-Park/robust_EM_for_gmm.git
Author: HongJea Park
Author-email: hongjea.park@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.17.1)
Requires-Dist: scipy (>=1.3.1)
Provides-Extra: all_extras
Requires-Dist: scikit-learn (>=0.2.0) ; extra == 'all_extras'
Requires-Dist: matplotlib (>=3.1.1) ; extra == 'all_extras'
Provides-Extra: dev
Requires-Dist: scikit-learn (>=0.2.0) ; extra == 'dev'
Requires-Dist: matplotlib (>=3.1.1) ; extra == 'dev'
Provides-Extra: test
Requires-Dist: scikit-learn (>=0.2.0) ; extra == 'test'
Requires-Dist: matplotlib (>=3.1.1) ; extra == 'test'

# A Robust EM Clustering Algorithm for Gaussian Mixture Models

## Description

Python implementation of Robust EM Clustering for Gaussian Mixture Models[1].
(Click [here] to view the paper for more detail.)

<br>

- `robustgmm.robustgmm` 

    Scikit-learn API style for Robust GMM

- `robustgmm.generator` 

    Generator for synthetic data from mixture of gaussian.

<br>

For more detail to use, see the example below or [paper_example.py]


* Reference

    MS Yang, A robust EM clustering algorithm for gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961

[here]: https://pdfs.semanticscholar.org/b6f2/9775838dd411a73a9655269b8854b674cf8b.pdf
[paper_example.py]: ./test/paper_example.py

<br>

## Install

1. Install from PyPI

    ```bash
    pip install robustgmm
    ```

2. Install from Github

    ```bash
    pip install git+https://github.com/HongJea-Park/robust_EM_for_gmm.git
    ```

<br>

## Example

All examples are conducted to compare with the experimental results of the paper.

```python
# For more detail, refer ./test/paper_example.py
import numpy as np

from robustgmm import RobustGMM
from robustgmm import Generator_Multivariate_Normal


# Generate data from 2 multivariate normal distribution with fixed random seed
np.random.seed(0)
real_means = np.array([[.0, .0], [20, .0]])
real_covs = np.array([[[1, .0], [.0, 1]],
                      [[9, .0], [.0, 9]]])
mix_prob = np.array([.5, .5])
generator = Generator_Multivariate_Normal(means=real_means,
                                          covs=real_covs,
                                          mix_prob=mix_prob)
X = generator.get_sample(800)

# GMM using robust EM Algorithm
rgmm = RobustGMM()
rgmm.fit(X)
```

<br>

## Figures for each examples in paper

1. ### Example 1
    ![example1-1](./figure/example1_1.png)
    ![example1-2](./figure/example1_2.png)

2. ### Example 2
    ![example2-1-1](./figure/example2_1_1.png)
    ![example2-1-2](./figure/example2_1_2.png)
    ![example2-2-1](./figure/example2_2_1.png)
    ![example2-2-2](./figure/example2_2_2.png)

3. ### Example 3
    ![example3-1](./figure/example3_1.png)
    ![example3-2](./figure/example3_2.png)

4. ### Example 4
    ![example4](./figure/example4.png)

5. ### Example 5
    ![example5-1](./figure/example5_1.png)
    ![example5-2](./figure/example5_2.png)

6. ### Example 6
    ![example6-1](./figure/example6_1.png)
    ![example6-2](./figure/example6_2.png)

7. ### Example 7
    ![example7](./figure/example7.png)

8. ### Computational time cost
    ![timecost](./figure/time_cost.png)


