semvec/pypi-packages huggingface dataset:
glove-py 0.2.3
glove Python bindings

**glove-py is an implementation of the GloVe algorithm for learning word vectors from a corpus.** 


## Installation


using pip:

```
pip install glove-py
```

install with github code :

```
git clone git@github.com:f20500909/glove_py.git
python3 setup.py install
```


using in shell



### Run

with python:
```python
from glove import *

model = Glove(params)
model.train(input_file)
model.to_txt()
words = model.most_similary("one", 10)
print(words)

```

List of available `params` and their default value:

```
help info......
  -input_file               input_file [small_text]
  -vocab_size               vocabulary size to train  [0]
  -max_size                 maximum size to keep in courpus [10000000]
  -min_count                minimum times  [1]
  -window                   the window size to get common occurrence [10]
  -embed_size               embedded size  [80]
  -epoch                    train epoch [1]
  -threads                  train thread [40]
  -memory_limit             memory used to tain (GB) [1]
  -lr                       learning rate [0.05]
```

with shell:
./scripts/demo.sh


我去掉了很过我看上去用处不大的功能(只是我认为的用处打大的功能,有些细节并不理解也移除了),如提取共现矩阵的symmetric功能

忽略了大小写,统一改大写为小写


TODO:

修复有时会发生的错误:   read err...: Connection reset by peer

在合并共现词时，高频和低频矩阵好像写在了一个文件里面，感觉有点问题.....




在编译pybind_11时不能让对应文件被include 必须用cmake加载


![word](https://github.com/f20500909/glove_py/blob/master/scripts/tsne.png)
### Reference

- [GloVe ](https://github.com/stanfordnlp/GloVe)
- [GloVe-cpp](https://github.com/Yevgnen/GloVe-cpp)
- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.
easyeda 2.0
A useful tool to Exploratory Data Analysis
Requires Python: >=3.5

## 1，Introduction

The easyeda is a simple but useful tool to do Exploratory Data Analysis in Machine Learning.
It can be used in both classification task and regression task. 


## 2，Use Example

First, you can use pip to install easyeda.

```bash
pip install easyeda
```

Then, you can use it like below.

```python
from easyeda import eda
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split

## 
boston = datasets.load_boston()
df = pd.DataFrame(boston.data,columns = boston.feature_names)
df["label"] = boston.target
dftrain,dftest = train_test_split(df,test_size = 0.3)
dfeda = eda(dftrain,dftest,language="Chinese")

```

### 3，Contact to the author

Github:  https://github.com/lyhue1991/easyeda

Email: lyhue1991@163.com