semvec/pypi-packages huggingface dataset:
Topsis-samanvay-102103315 0.0.1
topsis package for decision making
Keywords: python,video,stream,video stream,camera stream,sockets

# Topsis

TOPSIS is based on the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one. Alternatives are ranked with the use of an overall index calculated based on the distances from the ideal solutions.

It takes 4 arguments :
1.Data.csv file
2.Weights
3.Impacts
4.Result file

Returns file with Topsis Score and Rank

## How to use it?

Open terminal and type pip install Topsis-samanvay-102103315

To get started quickly, just use the following:

from topis_pckg.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')
make ensure the weights and impacts should be in ""

eg: "1,1,1,1,1" and "+,-,+,-,+"
poisson-binomial 0.0.2
methods associated with poisson binomial distribution
Requires Python: >=3.0

# Poisson Binomial


The Poisson binomial distribution is the discrete probability distribution of a sum of independent Bernoulli trials that are not necessarily identically distributed. That is, it is the number of successes in a sequence of n independent yes/no experiments where the success probabilities vary.

The package contains a single class PoissonBinomial.

Parameters: p : array-like
			the probabilities of success for each of the Bernouilli trials

Attributes: pmf : array-like	probability mass function
			the probability of n successes where 0<=n<=len(p)
cdf			:	array-like, 	cumulative distribution function
			the probability of n or less successes where 0<=n<=len(p)


Methods: 

x_or_less(x) :	float,		probability of achieving x or less successes  
x_or_more(x) :	float,		probability of achieving x or more successes

The class PoissonBinomial implements a closed-form expression for the cdf of the Poisson binomial distribution,derived in "On computing the distribution function for the Poisson binomial distribution" by Yili Hong (2013).

The approach is based on finding the discrete fourier transform function for the characteristic function (CF) for the Poisson binomial distribution.