semvec/pypi-packages huggingface dataset:
TOPSIS-DishaSharma-101803637 0.0.1
To calculate TOPSIS

TOPSIS
===================

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

----------


TopsisPy
-------------
TopsisPy is a Python Package implementing Topsis method used for multi-criteria decision analysis. Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.

TopsisPy intends to make the process of TOPSIS simple in python. Key features of the package are -

> - Easy to use
> - Numpy based
> - Ideal for Students

--------
Topsis for decision making
-------------

#### Step 1. Construct the decision matrixes and determine the weights of criteria for k-decision makers. 



#### Step 2. Calculate the normalized decision matrix for each decision maker. 

This step transforms various attribute dimensions into non-dimensional
attributes which allows comparisons across criteria. Because various criteria
are usually measured in various units, the scores in the evaluation matrix X
have to be transformed to a normalized scale. The normalization of values
can be carried out by one of the several known standardized formulas. 


#### Step 3. Determine the positive ideal and negative ideal solutions for each decision maker

The weighted normalized value  v is calculated by multiplying it with weight.

#### Step 4. Calculate the separation measures from the positive ideal solution and the negative ideal solution. 

Identify the positive ideal alternative (extreme performance on each criterion)
and identify the negative ideal alternative (reverse extreme performance on each
criterion). The ideal positive solution is the solution that maximizes the benefit
criteria and minimizes the cost criteria whereas the negative ideal solution
maximizes the cost criteria and minimizes the benefit criteria. 

#### Step 5. Calculate the separation measures.

In the TOPSIS method a number of distance metrics can be applied.

#### Step 6. Calculate the relative closeness to the positive ideal solution. 

#### Step 7. Rank the preference order or select the alternative closest to 1.
A set of alternatives now can be ranked by the descending order of the value
of Ri.
TOPSIS-Disha-101803637 0.0.2
To calculate TOPSIS

TOPSIS
===================

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

----------


TopsisPy
-------------
TopsisPy is a Python Package implementing Topsis method used for multi-criteria decision analysis. Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.

TopsisPy intends to make the process of TOPSIS simple in python. Key features of the package are -

> - Easy to use
> - Numpy based
> - Ideal for Students

--------
Topsis for decision making
-------------

####Step 1. Construct the decision matrixes and determine the weights of criteria for k-decision makers. 



#### Step 2. Calculate the normalized decision matrix for each decision maker. 

This step transforms various attribute dimensions into non-dimensional
attributes which allows comparisons across criteria. Because various criteria
are usually measured in various units, the scores in the evaluation matrix X
have to be transformed to a normalized scale. The normalization of values
can be carried out by one of the several known standardized formulas. 


#### Step 3. Determine the positive ideal and negative ideal solutions for each decision maker

The weighted normalized value  v is calculated by multiplying it with weight.

#### Step 4. Calculate the separation measures from the positive ideal solution and the negative ideal solution. 

Identify the positive ideal alternative (extreme performance on each criterion)
and identify the negative ideal alternative (reverse extreme performance on each
criterion). The ideal positive solution is the solution that maximizes the benefit
criteria and minimizes the cost criteria whereas the negative ideal solution
maximizes the cost criteria and minimizes the benefit criteria. 

#### Step 5. Calculate the separation measures.

In the TOPSIS method a number of distance metrics can be applied.

####Step 6. Calculate the relative closeness to the positive ideal solution. 

####Step 7. Rank the preference order or select the alternative closest to 1.
A set of alternatives now can be ranked by the descending order of the value
of Ri.
graycode 1.0.5
Convert two's complement integer to gray code and vice versa
Requires Python: >=3.5

# Introduction

Gray code or reflected binary code (RBC) is an ordering of binary numbers such
that two successive numbers only differ in one bit. In essence, this is an
alternative to two's complement integers when it is useful to change only
one bit between successive numbers.

This library implements functions to convert gray code to two's complement
integer and vice versa.

See LICENSE.md for copyright information. This library is licensed under the
MIT license.

See https://en.wikipedia.org/wiki/Gray_code for details and applications of
gray codes.

# Installation

```
$ python3 setup.py install
```
Or from PyPi:
```
$ pip3 install graycode
```

# Example

```
import graycode

for i in range(8):
    print(i, '{:03b}'.format(graycode.tc_to_gray_code(i)))
```
=>
```
0 000
1 001
2 011
3 010
4 110
5 111
6 101
7 100
```
Reverse is also provided:
```
graycode.gray_code_to_tc(0b011)
```
=>
```
2
```
gen_gray_codes(n) produces all n bit gray codes:
```
graycode.gen_gray_codes(3)
```
=>
```
[0, 1, 3, 2, 6, 7, 5, 4]
```
ccfkotlin 1.1.0
Kotlin code conversions fixer

# Lab2 Code conventions fixer for Kotlin

After successful installation project can be used command:
`ccfkotlin (-v|-fx) -(p|d|f) <path>`

#### Command line options
```text
Options:
  -v, --verify                    Verify files without editing.
   
  -fx, --fix                      Rewrite files

  -d, --directory TEXT            Absolute or relative path to directory with 
                                  files to be fixed.
  
  -p, --project TEXT              Absolute or relative path to project with 
                                  files to be fixed.
                                  
  -f, --file TEXT                 Absolute or relative path to file to be fixed.

  -h, --help                      Show this message and exit.`
  ```
  *Note: any other input is equivalent to* `--help`