semvec/pypi-packages huggingface dataset:
robustgmm 1.0.2
A robust EM clustering algorithm for Gaussian mixture models
Requires Python: >=3.6

# 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)
sklearn-esa 0.0.13
An ESA implementation in python.

# Get the required resources
<code> scp -r webis@webislab40.medien.uni-weimar.de:/home/weci2587/projects/args-topic-modeling/resources . </code>

# To run the ESA-script with all terms run:
## For normal ESA:
    
    ./esa-all-terms.py  --similarity cos
                        --matrix-path <path_to_resources>/resources/esa-plain/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>
        
## For word2vec-ESA:
    
    ./esa-all-terms.py  --similarity max
                        --matrix-path <path_to_resources>/resources/esa-w2v/<debatepedia|strategic-intelligence|wikipedia>.mat
                        --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                        --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                        --input-path <path_to_input_file>
                        --output-path <path_to_output_file>
    
# To run the word2vec-ESA with reduced terms run:

    ./esa-top-n-terms.py    -n <number_of_terms> 
                            --corpus-path <path_to_resources>/resources/corpora/<debatepedia|strategic-intelligence|wikipedia>.csv
                            --model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin
                            --model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p
                            --input-path <path_to_input_file>
                            --output-path <path_to_output_file>
                            
The input document must be a csv file with "|" as the separator and must contain the column "document", which is used as the input text for the ESA.
diarypy 0.4.3
Diary to create notebooks and store intermediate results and figures
Requires Python: >=3.6
Keywords: diary,notebook,logging,figures,experiments

[![CI][ci:b]][ci]
[![License MIT][license:b]][license]
![Python3.8][python:b]
[![pypi][pypi:b]][pypi]
[![codecov][codecov:b]][codecov]

[ci]: https://github.com/perellonieto/DiaryPy/actions/workflows/ci.yml
[ci:b]: https://github.com/perellonieto/diarypy/workflows/CI/badge.svg
[license]: https://github.com/perellonieto/DiaryPy/blob/master/LICENSE.txt
[license:b]: https://img.shields.io/github/license/perellonieto/diarypy.svg
[python:b]: https://img.shields.io/badge/python-3.8-blue
[pypi]: https://badge.fury.io/py/diarypy
[pypi:b]: https://badge.fury.io/py/diarypy.svg
[codecov]: https://codecov.io/gh/perellonieto/DiaryPy
[codecov:b]: https://codecov.io/gh/perellonieto/DiaryPy/branch/master/graph/badge.svg?token=AYMZPLELT3


# DiaryPy

### A python class to automatically save the partial/intermediary results of a running experiment in a set of notebooks (csv files) and images as files.

[![Build Status](https://travis-ci.org/perellonieto/DiaryPy.svg?branch=master)](https://travis-ci.org/perellonieto/DiaryPy)

Create a new diary

```
from diarypy.diary import Diary
diary = Diary(name='world', path='hello', overwrite=False,
              stdout=False, stderr=False)
```

Create all the notebooks that you want to use

```
diary.add_notebook('validation')
# You can use the returned instance later
notebook_test = diary.add_notebook('test')
# And specify the header
notebook_train = diary.add_notebook('training', header=['iteration', 'accuracy'])
```

Store your results in the different notebooks

```
diary.add_entry('validation', ['accuracy', 0.3])
diary.add_entry('validation', ['accuracy', 0.5])
diary.add_entry('validation', ['accuracy', 0.9])
notebook_train.add_entry([0, 0.4])
notebook_train.add_entry([1, 0.6])
notebook_train.add_entry([2, 0.8])
notebook_test.add_entry(['First test went wrong', 0.345, 'label_1'])
```

Add an image

```
from PIL import Image
image = Image.new(mode="1", size=(16,16), color=0)
diary.save_image(image, filename='test_results')
```

### Resulting files

The files that are generated after executing the previous lines are

```
hello/
└── world
    ├── description.txt
    ├── images
    │   └── test_results_4.png
    ├── test.csv
    └── training.csv
    └── validation.csv
```
the content of the files is

description.txt
```
Date: 2015-10-22 17:43:19.764797
Name : world
Path : hello/world
Overwrite : False
Image_format : png
```

validation.csv
```
1,1,|2021-06-17|,|12:56:45.945000|,|accuracy|,0.3
2,2,|2021-06-17|,|12:56:46.813717|,|accuracy|,0.5
3,3,|2021-06-17|,|12:56:53.358989|,|accuracy|,0.9
```

training.csv
```
|id1|,|id2|,|date|,|time|,|iteration|,|accuracy|
4,1,|2021-06-17|,|12:56:54.231691|,0,0.4
5,2,|2021-06-17|,|12:56:55.128130|,1,0.6
6,3,|2021-06-17|,|12:56:56.006014|,2,0.8
```

test.csv
```
7,1,|2021-06-17|,|12:56:56.761961|,|First test went wrong|,0.345,|label_1|
```

# Unittest

```
python -m unittest discover diarypy
```
cornutils 0.4.1
Utility functions
Requires Python: >=3.7,<4.0

# cornutils
Python package containing utility functions

## Install
```bash
python3 -m pip install cornutils
```

## Example Usage of praktika
```python
import numpy as np
from cornutils.praktika import Data, PlotSettings, aio

x = np.array([0,2,4,6,8,10,12,14,16,18,])
y = np.array([2.8,5.2,6.8,9.6,11.2,14,15.6,17,19.8,21.4,])
delta_y = np.array([1.0,1.5,1.2,0.9,1.4,1.4,1.5,1.2,1.3,1.5,])
data = Data(x, y, sy=delta_y)

s = PlotSettings(label_x='x-label', label_y = 'y-label',label_fit='Test')

def func(beta, x):
    '''
    Function to use for estimation, beta is an array with each parameter
    '''
    y = beta[0] + beta[1] * x
    return y

aio(func, 2, data, s)
```
fastpreprocess 0.2.2
A new way to preprocess data for ML.
Requires Python: >=3.7

<h1 align='center'>Fastpreprocess</h1>

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lkarjun/fastpreprocess/master)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lkarjun/fastpreprocess/blob/master/Demo(running-from-notebook).ipynb#scrollTo=ABxXfXvVn1fF)
[![Generic badge](https://img.shields.io/badge/Python3.9-passing-<COLOR>.svg)](https://shields.io/)
[![PyPI version](https://badge.fury.io/py/fastpreprocess.svg)](https://badge.fury.io/py/fastpreprocess)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/fastpreprocess)
### A new way to preprocess data for ML.

> Developing...
## Demo


https://user-images.githubusercontent.com/58617251/122407735-7b480800-cf9f-11eb-9145-2ba5217190bb.mp4


## Installation
```bash
pip install fastpreprocess
```

## Start Server
```bash
""" 
Args
-fn filenameOrbuffer -dm delimiter -p port -cl cloudconsole -lw pandas lowmemory
"""

fp -p 9000
#Or
fp -fn https://bit.ly/3fCBwej -dm ',' -p 9000
#In cloud console
fp -fn https://bit.ly/3fCBwej -dm ',' -p 9000 -cl true
```