semvec/pypi-packages huggingface dataset:
qsarmodelingpy 0.3.1
A package for building and validating QSAR models
Requires Python: >=3.6

# QSARModelingPy Core

[![PyPI](https://img.shields.io/pypi/v/qsarmodelingpy)](https://pypi.org/project/qsarmodelingpy/)
[![docs](https://img.shields.io/badge/docs-v0.1.0-blue)](https://hellmrf.github.io/QSARModelingPy/)
![PyPI - Format](https://img.shields.io/pypi/format/qsarmodelingpy)

**QSARModeling** é uma ferramenta gratuita e open-source para a construção e validação de modelos QSAR e QSPR. [Originalmente desenvolvido](https://doi.org/10.1590/S0100-40422013000400013) em Java, esta biblioteca em Python permite ao usuário a aplicação programática, personalizável e escalável dos recursos do software.

Alguns recursos podem ser destacados, como:

-   Redução de dimensionalidade baseada em variância, correlação e autocorrelação;
-   Seleção de variáveis: Ordered Predictors Selection (OPS) ou Algoritmo Genético (GA)
-   Validação cruzada: y-randomization e leave-N-out
-   Validação externa

## Uso

> Esta versão do QSARModelingPy Core é uma biblioteca para a linguagem de programação Python, com foco em programadores que desejam implementar sua própria versão do QSAR Modeling, expandi-lo ou utilizá-lo programaticamente. Se deseja uma versão funcional desenvolvida para o usuário final, veja as [interfaces do QSARModelingPy](https://github.com/hellmrf/QSARModelingPyInterfaces).

Para utilizar, instale a biblioteca em seu ambiente.

```sh
$ pip install qsarmodelingpy
```
basic_math_calculator 0.1.0
Very simple calculator 

# Basic calculator


This is a calculator project for basic math functions.<br />
Implemented methods:<br />
				addition<br />
				subtraction<br />
				multiplication<br />
				division<br />
				nth-root ((n) root of a number)<br />
				reset memory<br />

# Installation


pip install basic-math-calculator

from calculator.calculator import Calculator


# Usage

Result is stored and overwritten in memory until reset() method is ran.

Calculation methods can take either one or two arguments (in addition to self).
If only one arguments is given calculations will be performed with memory value (latest result), otherwise calculations will be performed between two arguments and memory value will be overwritten.

For example:

	>>> calc = Calculator()
	>>> calc.addition(4, 5)
	9
	>>> calc.addition(10)
	19
	>>> calc.multiplication(2)
	38
	>>> calc.multiplication(2, 5)
	10
	>>> calc.reset()
	0

# License

This project is licensed under the terms of the **MIT** [license](https://choosealicense.com/licenses/mit/)
Topsis-Sneha-101916120 1.5
Topsis


# TOPSIS implementation in Python
This package is implementation of topsis technique for Order Preference by Similarity to Ideal Solution.\
With the help of this package TOPSIS implementation can be done in simple few line code.\

# Input
It takes an input CSV file which must contain >=3 columns. \
Additional paramater weight and impact in the form of a list and Format of output file \
First column is the object/variable name (e.g. M1, M2, M3, M4 ..)\
From 2nd to last columns must contain numeric values only.\


## Usage:
```python
!pip install Topsis-Sneha-101916120==1.5
tp=__import__("Topsis-Sneha-101916120")
tp.topsis("101916120-data.csv","1,1,1,1,1","+,+,-,+,+","result.csv")
#tp.topsis("input file name ","weights","impacts","outpul file name")
```
## Output
Result file will be a csv file containing all the columns of input file and two additional columns having Topsis Score and Rank.
Topsis-Nervan-101916101 1.7
Topsis


# Topsis Description

Following python package will help you to use Topsis. It is the Technique for Order of Preference by Similarity to Ideal 
# Usage

__Step 1:__ Install the topsis package using the following commmand. (pip install Topsis-Nervan-101916101)

__Step 2:__ Import the package using the commmand. (tp=\_\_import__("Topsis-Nervan-101916101"))

__Step 3:__ Call the Topsis function to generate the csv file.


1st parameter => input dataframe
2nd parameter =>  weights 
3rd parameter => impacts 
4th parameter => name of output csv file. For eg:

tp.topsis('101916101-data.csv',"1,1,1,1,1","+,+,+,+,+","result.csv")

This command will generate a csv file named result.csv.

# Constraints

1) length of Weights and Impacts should be same
2) Weights must be commas seperated and in the form of a string.
3) Impacts must be commas seperated and in the form of a string. 
4) Don't forget to put .csv extension otherwise it will throw an error
5) Impacts having only positive and negative sign.
Topsis-Shikha-101903629 0.0.0
TOPSIS Implementation using Python
Keywords: python,topsis python,calculate topsis score,TOPSIS,topsis using python

## Package Usage

# TOPSIS-Technique for order performance by similarity to ideal solution
Submitted By: Shikha 101903629

-------------

### What is TOPSIS?
A simple but powerful decision method.


![](https://i.ytimg.com/vi/0_imbSU7mH4/hqdefault.jpg)

TOPSIS, known as Technique for Order of Preference by Similarity to Ideal Solution, is a multi-criteria decision analysis method. It compares a set of alternatives based on a pre-specified criterion. The method is used in the business across various industries, every time we need to make an analytical decision based on collected data.
### How to use this package?
To begin with, you have to install the package 'Topsis_Shikha_101903629' using the following command:
bash
pip install Topsis_Shikha_101903629

Then you just need to do a function call by passing some specified parameters.
We provided you with a function named as top_score().
This function requires three parameters.
### Parameters:

1. First Parameter - Matrix

   Create a matrix consisting of M alternatives and N criteria. This matrix is usually called an “evaluation matrix”.
All values should be numerical.

2. Second Parameter - Array consisting weights for each column. 

   Assign weights to each column. Weights
can be (1,1,1,1) or (1,1,0.5,0.5) or (1,1,2,2)

3. Third Parameter - Array consisting impact of each column. 

   E.g. : (1,0,1,1) (1 for max value as best, 0 for min value as best)


Example:
bash
from python_topsis import Topsis_Shikha_101903629 as tps
tps.top_score(evaluation_matrix, weights, impacts)



>Coding is the language of the future, and every one should learn it !!