# fast-scores
[][](https://img.shields.io/static/v1?label=python+&message=3.7%2B&color=blue)[](https://github.com/psf/black)[](https://opensource.org/licenses/MIT)[](https://badge.fury.io/py/fast_scores)
Calculate correlatioin matrix fast
## Preinstall fasttext
```
pip install fasttext
```
For Windows without a C/C++ compiler:
* Download a proper whl (e.g., `fasttext‑0.9.2‑cp36‑cp36m‑win_amd64.whl` for 64bits Python 3.6 etc) from [https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext](https://www.lfd.uci.edu/~gohlke/pythonlibs/#fasttext)
```bash
pip install fasttext*.whl
```
or (for python 3.8)
```
pip install https://github.com/ffreemt/ezbee/raw/main/data/artifects/fasttext-0.9.2-cp38-cp38-win_amd64.whl
```
## Installation
```
pip install fast-scores
```
## Usage
```shell
# from fast-scores\tests\test_gen_cmat.py
from fast_scores.gen_cmat import gen_cmat
text_en = "test this\nbla bla\n love"
text_zh = "测试\n 爱\n吃了吗\n你好啊"
list1 = [elm.strip() for elm in text_en.splitlines() if elm.strip()]
list2 = [elm.strip() for elm in text_zh.splitlines() if elm.strip()]
cmat = gen_cmat(list1, list2) # len(list2) x len(list1)
print(cmat)
# [[0.75273851 0. 0. ]
# [0. 0. 0.86848247]
# [0. 0. 0. ]
# [0. 0. 0. ]]
len_y, len_x = cmat.shape
assert cmat.max() > 0.86 # 0.868
_ = divmod(cmat.argmax(), len_x)
assert cmat[_] == cmat.max()
```
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