semvec/pypi-packages huggingface dataset:
jsl-lib-tmp-for-databricks 4.2.3rc12
The John Snow Labs Library gives you access to all of John Snow Labs Enterprise And Open Source products in an easy and simple manner. Access 10000+ state-of-the-art NLP and OCR models for Finance, Legal and Medical domains. Easily scalable to Spark Cluster
Keywords: Spark NLP OCR Finance Legal Medical John Snow Labs

# The John Snow Labs Library
The John Snow Labs Library gives you access to all of John Snow Labs Enterprise and Open Source products in an easy and simple manner.             
Access 10000+ state-of-the-art NLP and OCR models for `Finance`, `Legal`,`Medical` and many other domains which effortlessly scales to big data clusters.               
See https://www.johnsnowlabs.com/  and [the docs](https://nlu.johnsnowlabs.com/docs/en/install) for more details     

To install the NLP Libraries in your environment:

1. Get a license key in your [my.johnsnowlabs.com](https://my.johnsnowlabs.com/) account. If you do not have one, get a trial license [here](https://www.johnsnowlabs.com/install/).
2. In your shell, run:
```shell
pip install johnsnowlabs
````

3. In a Python shell, run :
```shell
from johnsnowlabs import spark_nlp  
nlp.install()
````
xfem-avx2 2.0.2204.post27.dev0
(ngs)xfem is an Add-on library to Netgen/NGSolve for unfitted/cut FEM.
Requires Python: >=3.8

(ngs)xfem is an Add-on library to Netgen/NGSolve which enables the use of unfitted finite element technologies known as XFEM, CutFEM, TraceFEM, Finite Cell, ... . ngsxfem is an academic software. Its primary intention is to facilitate the development and validation of new numerical methods.
abc-annMacroF1withCost 0.0.1
ABC-ANN-MacroF1withCost is a classification method that combines ABC algorithm with a artificial neural network classification model

# ARTIFICIAL BEE COLONY ALGORITHM WITH LOGISTIC REGRESSION (ABC-LR)

ABC-LR is a binary classification method in which the ABC algorithm is used instead of the Gradient Descent algorithm to train the weights in the Logistic Regression classification model. The purpose of the ABC algorithm in the ABC-LR method is to minimize the value of the cost function. The ABC algorithm is a very popular metaheuristic method that can search for solutions both locally and globally in the solution space. In addition, it has been shown in the study that the ABC-LR classification method achieves superior classification success compared to the LR classification method.

Although the ABC-LR classification method can handle complex and high-dimensional data, its runtime can be high. Therefore, CPU and GPU parallelized versions of the ABC-LR classification method are presented here, and significant improvements in runtime can be achieved.

ABC-LR is written in Python3 and continuously tested with Python 3.7 and 3.10.

## Installation

Install ABC-LR via PyPI:

```
pip install abcLR
```

Or alternatively, clone the environment:

```
git clone https://github.com/kagandedeturk/ABC-LR.git
```

## CPU Version Usage

```py

import numpy as np
parallelType = np
from abcLR import ABC_LR_Model
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
y = y.reshape(-1,1)

lb = -16
ub = 16
evaluationNumber = 80000
# FVS = trainData.shape[1]
limit = 50
P = 60
MR = 0.3
L2 = 0

model = ABC_LR_Model(lb=lb, ub=ub, evaluationNumber=evaluationNumber, limit=limit, P=P, MR=MR, L2=L2, parallelType=parallelType)
#start_time = dt.datetime.now()
model.fit(X, y)
#print(f"Run time: {dt.datetime.now()-start_time}")
print(f"Result: {model.score(X, y)}")

```

## GPU Version Usage

```py
import cupy as cp
parallelType = cp
from abcLR import ABC_LR_Model
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)

X = parallelType.array(X)
y = parallelType.array(y.reshape(-1,1))

lb = -16
ub = 16
evaluationNumber = 80000
# FVS = trainData.shape[1]
limit = 50
P = 60
MR = 0.3
L2 = 0

model = ABC_LR_Model(lb=lb, ub=ub, evaluationNumber=evaluationNumber, limit=limit, P=P, MR=MR, L2=L2, parallelType=parallelType)
#start_time = dt.datetime.now()
model.fit(X, y)
#print(f"Run time: {dt.datetime.now()-start_time}")
print(f"Result: {model.score(X, y)}")

```

## License

This program is free software: you can redistribute it and/or modify
it under the terms of the 3-clause BSD license (please see the LICENSE file).

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the 3-clause BSD license
along with this program (see LICENSE file).
If not, see [here](https://opensource.org/licenses/BSD-3-Clause).

Copyright (c) 2022, Bilge Kagan Dedeturk (kagandedeturk@gmail.com)