semvec/pypi-packages huggingface dataset:
iva2k-how-long 0.1.4
A simple decorator to measure a function excecution time.
License: MIT
Requires Python: >=3.7,<4.0

iva2k_how_long
==============

Simple Decorator to measure a function execution time.

Very lightweight project to get familiar with poetry publishing to PyPi, built from tutorial https://www.pythoncheatsheet.org/blog/python-projects-with-poetry-and-vscode-part-1


Example
_______

.. code-block:: python

    from iva2_how_long import timer


    @timer
    def some_function():
        return [x for x in range(10_000_000)]
DataStructures10 1.0.1
Data structure components module
License: MIT
Keywords: data_structure

This module is build for all those working professionals/students who have basic knowledge of Data Structures and want to save coding time,this module contains various single LinkedList,stack,queue operations.It also contains 5 sorting algorithms and 2 searching algorithms.
__________________________________________
Linked List Operations
------------------------------------------
from data_structure.linkedlist import create,traversal,insertAtEnd,insertBegin,insertMiddle
#Methods to create and Traverse Linked List
#create("Hi") #To create node,creates one node at a time
#create("How")
#create("Are")
#create("You")
#traversal() #To traverse Linked List

#Output:
#Hi->How->Are->You->None

#Methods to Insert in Linked List
#insertBegin("Hey,") #Insert at Beginning
#traversal()
#output:
#Hey,->Hi->How->Are->You->None

#insertMiddle(2,"Bro") #First parameter takes  position,second parameter takes data
#traversal()
#output:
#Hey,->Hi->Bro->How->Are->You->None

#insertAtEnd("Doing") #Inserting at end
#traversal()
#output:
#Hey,->Hi->Bro->How->Are->You->Doing->None

__________________________________________
Stack Operations
__________________________________________
#from data_structure.stack import Stack
#s = Stack()

#To push element in stack
#s.push(50)
#output:
#'50 pushed'
#s.push(80)
#output:
#'80 pushed'

#To peek the stack
#s.peek()
#output:
#80

#To pop elements in stack
#s.remove()
#output:
#80

__________________________________________

Queue Operations

__________________________________________
#from data_structure.queue import Queue

#q=Queue()

#To insert elements into queue
#q.insert(40)
#output:
#'40 inserted'

#q.insert(50)
#output:
#'50 inserted'

#To remove elements from queue
#q.remove()
#output:
#40

#To check size of Queue
#q.size()
#output:
#1
__________________________________________
Sorting Operations
__________________________________________
#This module contains 5 sorting algorithms
which are:

#1)bubbleSort.
#2)mergeSort.
#3)insertionSort.
#4)shellSort.
#5)selectionSort.

#from data_structure.sort import mergeSort,bubbleSort,insertionSort,shellSort,selectionSort
#sort = mergeSort([5,101,35,121,55,75])
#print(sort)
#output:
#[5, 35, 55, 75, 101, 121]

#sort = selectionSort([5,101,35,121,55,75])
#print(sort)
#output:
#[5, 35, 55, 75, 101, 121]

__________________________________________
#Searching Operations
__________________________________________
#from data_structure.search import linearSearch,binarySearch
#Linear Search
#search = linearSearch([5,101,35,121,55,75],121)
#print(search)
#Binary Search, Note list passed should be sorted
#search = binarySearch([5, 35, 55, 75, 101, 121],121)
#print(search)
#output:
#Found 121 at index 5
__________________________________________
Topsis-Lovish-101803496 1.2.3
Implements Topsis
License: MIT

# TOPSIS

Code by: Lovish Gupta

<hr style = "border:2px solid gray"> </hr>

## What is TOPSIS

TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA(Multi-Criteria Decision Analysis) method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.multi-criteria decision analysis(MCDA) method. 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.


## How to Use This Package

#### Before running, make sure you have pandas installed on your system

Open Command Prompt and input the following commands


>> pip install Topsis-Lovish-101803496



>> python
>>>from topsis.topsispac import topsis
>>>topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")

## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

Â© 2020 Lovish Gupta

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-Alamjeet-101803728 1.1.3
Implements Topsis
License: MIT

# TOPSIS

Code by: Alamjeet Singh 

<hr style = "border:2px solid gray"> </hr>
## What is TOPSIS

TOPSIS stands for â€˜The Technique for Order of Preference by Similarity to the Ideal Solutionâ€™ is a multi-criteria decision analysis(MCDA) method. 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.


## How to run

#### Before running, make sure you have pandas installed on your system

Open Terminal and input the following commands


>> pip install Topsis-Alamjeet-101803728



>> python
>>>from topsis.topsis1 import topsis
>>>topsis("input.csv","1,2,1,2","+,+,-,+","output.csv")

## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

Â© 2020 Alamjeet Singh

This repository is licensed under the MIT license. See LICENSE for details.