Saravanan666/pypi-org huggingface dataset:
Package: topsis-3283
topsis-3283for:Project-1 (UCS633)submitted by:Katinder KaurRoll no:101703283Group:3COE13topsis-3283 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).InstallationUse the package managerpipto install topsis-3283.pipinstalltopsis-3283UsageEnter csv filename followed by.csvextentsion, then enter theweightsvector with vector values separated by commas, followed by theimpactsvector with comma separated signs(+,-)topsissample.csv"1,1,1,1""+,-,+,+"or vectors can be entered without " "topsissample.csv1,1,1,1+,-,+,+But the second representation does not provide for inadvertent spaces between vector values. So, if the input string contains spaces, make sure to enclose it between double quotes(" ").To view usagehelp, usetopsis /hExamplesample.csvA csv file showing data for different mobile handsets having varying features.ModelStorage space(in gb)Camera(in MP)Price(in $)Looks(out of 5)M116122505M21682003M332163004M43282754M516162252weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]impacts vector = [ + , + , - , + ]input:topsissample.csv"0.25,0.25,0.25,0.25""+,+,-,+"output:TOPSIS RESULTS
-----------------------------

    P-Score  Rank
1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4Other notesThe first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.Make sure the csv does not contain categorical valuesLicenseMIT
Package: topsis33
topsis-pythonTopsis analysis of a csv file in pythonAbout TopsisIt 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. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problemsInstallationpipinstalltopsis33UsageQuery on terminal will provide you the topsis analysis for input csv file.topsis33 -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize.
Size of w and i is equal to number of features.Note that the first row and first column of dataset is droppedRank 1 signifies best decisionLicenseMIT
Package: topsis34
Topsisby Ramneet SinghPython package to calculate  performance score of different models and their ranks.Usagefromtopsis34.topsisimporttopsisobj=topsis.Topsis("data.csv","2.5,3,5,3.5","+,-,+,+")# (filename,weights,impacts)obj.calculate_print_rank()# calculate and prints rankorType in your terminal$topsis34<file_name><weights><impacts>example:$topsis34data.csv"2.5,3,5,3.5""+,-,+,+"
Package: topsis-3561
TOPSIS Implementation in PythonThis repository contains a Python implementation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. TOPSIS is a multi-criteria decision-making technique used to rank a set of alternatives based on their performance with respect to multiple criteria.How to UseRequirementsPython 3pandasnumpyInstallationInstall the required libraries using:pipinstallpandasnumpy

Thetopsisfunctiontakesadataset,weights,andimpactsasinput,anditcalculatestheclosenesstotheidealsolutionforeachalternative.Theweightsrepresenttheimportanceofeachcriterion,andtheimpactssignifywhetheracriterionshouldbemaximized(1)orminimized(-1).

Thefunctionperformsthefollowingsteps:

Normalizethedecisionmatrix.
Multiplyeachcolumnbytherespectiveweight.
Findtheidealandnegative-idealsolutions.
Calculatetheseparationmeasures.
Calculatetheperformancescore.
Calculatetherelativeclosenesstotheidealsolution.
Ifthereisamismatchinthenumberofweights,impacts,andcolumns,aValueErrorisraised.
Package: topsis-7016
topsis-7016for:Project-1 (UCS654)submitted by:Mukul ShardaRoll no:102197016Group:3CS-4topsis-7016 is a Python library which implements the TOPSIS algorithm used for Multiple Criteria Decision Making(MCDM).Know more about TopsisInstallationUse the package managerpipto install topsis-7016.pipinstalltopsis-7016UsageEnter filename followed by.csvor.xlsxextentsion, then enter theweightsvector with vector values separated by commas, followed by theimpactsvector with comma separated signs(+,-), make sure to pass the file where you want to store your results.fromtopsisimporttopsistopsis.main("file_path","weights","impacts","output.csv")Examplesample.csvA csv file showing data for different mobile handsets having varying features.ModelStorage space(in gb)Camera(in MP)Price(in $)Looks(out of 5)M116122505M21682003M332163004M43282754M516162252weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]impacts vector = [ + , + , - , + ]Other notesMake sure the csv does not contain categorical values.Make sure your dataset has atleast 3 rows and 3 columns.The length of weights vector and impact vector must equal the number of columns in dataset.While writing weights vector or impact vectors, please make sure not to put extra spaces.
Package: topsis-Aarushi-102003527
TOPSISAuthor:Aarushi Abrol.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.1.1Date:2022-1-22Maintainer:Aarushi Abrolaabrol_be20@thapar.edu.Description:Evaluation of alternatives based on multiple criteria using TOPSIS method..What is TOPSIS?Technique forOrderPreference bySimilarity toIdealSolution(TOPSIS) originated in the 1980s as a multi-criteria decision making method.TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,and greatest distance from the negative-ideal solution.License© 2022 Aarushi AbrolThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-aaryan-102003541
Topsis ModuleImplementation of MCDM using topsis in pythonHow to use it?Open terminal and type "aaryan" followed by input_dataset(in csv), weights(comma separated), impacts(comma separated) and resulting_dataset_name(in csv) as shown below(aaryan Test.csv 1,1,1,1,1 +,+,+,+,+ out.csv)"USAGE :aaryan <input_file_name>.csv (1,1,1,1) (+,+,-,+) <output_file_name>.csv"LicenseⒸ 2023 Aaryan JainThis repository is licensed under the MIT license.
See LICENSE for details
Package: topsis-aaryan-102103053
For:Assignment-1 (UCS654)Submitted by:Aaryan GuptaRoll No:102103053Group:3COE2Topsis_Aaryan_102103053Topsis_Aaryan_102103053 is a Python library for dealing with Multiple Criteria Decision Making (MCDM) problems by applying Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).InstallationInstall Topsis_Aaryan_102103053 using Pypi via pip.$pipinstalltopsis_aaryan_102103053UsageYou can use this package in python as :importtopsis_aaryan_102103053astopinputFile="sample.csv"weights="1,1,1,1"impacts="-,+,+,+"resultFile="result.csv"top.topsis(inputFile,weights,impacts,resultFile)ORYou can use this package via commandline as :$topsis[InputDataFileas.csv][Weightsasastring][Impactsasastring][ResultFileNameas.csv]For Example :$topsissample.csv"1,1,1,1""-,+,+,+"result.csvPlease Note That :The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv  MUST follow the format as shown in sample.csv shown in theExamplesection.The input data file MUST contain three or more columns.The second to last columns of the data file MUST containNUMERICvalues.The number of weights, impacts and columns(second to last)MUST beSAME.Impacts MUST either be '+' or '-'.Impacts and Weights MUST be separated by â€˜,â€™ (comma).Examplesample.csvA csv file that contains data for mobile models and their features.ModelPrice (in $)Storage Space (in GB)Camera (in MP)Looks (1 to 5)M125016125M22001683M330032164M42753284M522516162Weights : 0.25,0.25,0.25,0.25Impacts : -,+,+,+input :$ topsis sample.csv "0.25,0.25,0.25,0.25" "-,+,+,+" result.csvresult.csvA csv file that contains the same data as sample.csv with two additional columns 'Topsis Score' and 'Rank'.ModelPrice (in $)Storage Space (in GB)Camera (in MP)Looks (1 to 5)Topsis ScoreRankM12501612553.433M2200168330.845M33003216469.161M4275328453.472M52251616240.14LicenseMIT LicenseCopyright (c) 2024 Aaryan GuptaPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: topsis-aashish-102017138
Topsis-aashish-102017138Topsis-Aashish-102017138 is a Python library for handling problems related to Multiple Criteria Decision Making(MCDM) where there are more than one features to consider while arriving at a situation by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS),we can make conclusions to help us arrive at the decision with minimal effortInstallationUse the package manager pip to install Topsis-Aashish-102017138Syntaxtopsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv “1,1,1,2” “+,+,-,+” result.csv
or 
topsis inputfile.csv1,1,1,2+,+,-,+ result.csvExampleSample input dataModelCorrRseqRMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Sample Output DataModelCorrRseqRMSEAccuracyTopsis ScoreRankM10.790.621.2560.890.77313014581191562M20.660.442.8963.070.226675957320243625M30.560.311.5762.870.43894948666954914M40.820.672.6870.190.52376269718368453M50.750.561.380.390.81286261329801381LicenseMIT
Package: topsis-aayushi-102103421
OverviewThis is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.InstallationYou can install the package using pip:pip install topsis-aayushi-102103421USAGEfrom topsis-aayushi-102103421 import topsis#Example data (replace this with your actual data)data = {
'Alternative1': [1, 2, 3, 4],
'Alternative2': [4, 3, 2, 1],
# Add more alternatives and their values
}#Criteria weights (replace this with your actual weights)weights = [0.25, 0.25, 0.25, 0.25]#Criteria impacts ('+' or '-' for each criterion)impacts = ['+', '+', '+', '-']#Perform TOPSIS analysisresult = topsis(data, weights, impacts)#Display the rankingprint("Ranking:", result)Parametersdata: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.weights: A list of weights corresponding to the importance of each criterion.impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.LicenseThis package is distributed under the MIT License - see the LICENSE file for details.
Package: topsis-abhimat-101983058
TOPSIS-PythonSubmitted By:Abhimat Gupta 101983058pypi:https://pypi.org/project/topsis-abhimat-101983058git:https://github.com/abhi0444/topsis-abhimat-101983058.gitHow to use this package:This package can be run as in the following example:In Python notebook :>>> import pandas as pd
>>> from topsis_py.topsis import topsis
>>> df = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> df = topsis(d,w,im)
Package: topsis-abhinav-101803411
TOPSIS-PythonSubmitted By:Abhinav Narang 101803411How to use this package:This package can be run as in the following example:In Python notebook :>>> import pandas as pd
>>> from topsis_py.topsis import topsis
>>> df = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> df = topsis(d,w,im)
Package: topsis-ADITYA-101903150
#Topsis-Aditya-101903150
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-ADITYA-101903150##Sample Use case
import topsis-ADITYA-10903150 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-AJ
ï»¿Implementation of TOPSISâ€¢Input File
o Input file contain three or more columns
o First column is the object/variable name (e.g. M1, M2, M3, M4â€¦...)
o From 2nd to last columns contain numeric values only
â€¢ Output Files
o Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANKInstallationpip install TOPSIS-amrit-101803089SynchronizationÂ© 2020 Amrit JainThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-akashdeep-102103243
TopsisTOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Akashdeep Singh Kataria, 102103243, TIET, Patiala.InstallationUse the package manager pip to install topsis-3283.pip install topsis_akashdeep_102103243UsageEnter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.python [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]Examplepython sample.csv "1,1,1,1" "-,+,+,+" output.csvPlease Note That"The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv MUST follow the format as shown in sample.csv shown in the Example section.
The input data file MUST contain three or more columns.
The second to last columns of the data file MUST contain NUMERIC values.
The number of weights, impacts and columns (second to last) MUST be SAME.
Impacts MUST either be '+' or '-'.
Impacts and Weights MUST be separated by , (comma).LicenseÂ© 2024 Akashdeep Singh KatariaThis reopsitory is licensed under MIT License. See LICENSE for details.
Package: topsis-Akshit-101903151
#Topsis-Akshit-101903151
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-Akshit-101903151##Sample Use case
import topsis-Akshit-10903151 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-amisha-102117166
TOPSIS PACKAGE - Assignment 1I have developed a command line python program to implement the TOPSIS.
TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real worldInstallationpip install topsis-amisha-102117166UsagePlease provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.py -m topsis.__main__ [input_file_name.csv] [weight as string] [impact as string] [result_file_name.csv]Example UsageThe below example is for the data have 5 columns.py -m topsis.__main__ "C:\User\...." "1,1,2,0.5,0.75" "+,+,-,-,-" "C:\User\....."Example DatasetFund NameP1P2P3P4P5M10.780.615.534.710.4M20.880.77558.416.26M30.610.375.939.911.7M40.760.584.257.715.81M50.840.713.24813.19M60.760.58468.818.54M70.810.666.538.211.54M80.810.663.232.89.37Output DatasetFund NameP1P2P3P4P5TOPSIS ScoreRankM10.780.615.534.710.40.53037405450411224M20.880.77558.416.260.53725102207784133M30.610.375.939.911.70.47157072109146048M40.760.584.257.715.810.50994830547602796M50.840.713.24813.190.577234782933251M60.760.58468.818.540.494478878337379257M70.810.666.538.211.540.52441072526314295M80.810.663.232.89.370.55765336722857032Important PointsThere should be only numeric columns except the first column i.e. Fund Name.Input file must contain atleast three columns.CopyrightsLicense© 2024 AmishaThis repository is licensed under the MIT license.See LICENSE for details.
Package: topsis-anadya-102103021
topsis-anadya-102103021topsis-anadya-102103021 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).InstallationUse the package manager pip to install topsis-anadya-102103021.pip install topsis-anadya-102103021How to use it?Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-), and finally enter the name of the output file also followed by .csv extension.sample.csv 1,1,1,1 +,-,+,+ final.csvNoteMake sure the csv does not contain categorical values.LicenseÂ© 2024 Anadya Sahai
This repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-analysis
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).This package will helps you in TOPSIS analysis of csv files.
Please make sure that your dataset doesn't contain categorical data.Source:https://en.wikipedia.org/wiki/TOPSIS
Package: topsis-analysis-irtebat
Topsis Analysispypi:https://pypi.org/project/topsis-analysis-irtebatgit:https://github.com/Irtebat/topsis-pythonAbout TopsisThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).InstallationUse the package managerpipto install topsis-Irtebat.pipinstalltopsis-analysis-irtebatUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis -n "dataset-name" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."Do not mention the file format, 'csv', as part of dataset-name.
w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize.
Size of w and i is equal to number of features.Note that the first row and first column of dataset is droppedRank 1 signifies best decisionContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: topsis-Anant-102083043
#Topsis-Anant-102083043
Multiple criteria decision making (MCDM) using TOPSISInstallationpip install topsis-Anant-102083043##Sample Use case
import topsis-Anant-102083043 as t
t.topsis("input.csv","1,2,1,2,3","+,+,-,+,-","output.csv")
Package: topsis-Ananya-102017024
Project DescriptionTopsisTOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Multi-Criteria Decision Ananlysis (MCDA) method that is used to solve multiple criteria decision making problems.It uses 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 and ranks solutions that have multiple criterias.InstallationUsepipto install:pip install topsis-ananya-102017024PrerequisiteLibraries needed to be downloaded before running this package are :NumpyPandasUsageRuntopsisin the input file's directory as follows:topsis <input_file_name> <weights> <impacts> <output_file_name>For example,
Use quotation marks while including spaces in any argument:topsis data.csv "1, 1, 1, 1" "+, -, +, -" result.csvor vectors can be entered without any spacetopsis data.csv 1,1,1,1 +,-,+,- result.csvNote thatInput file must contain three or more columns.Except the first column of the input file the rest must be numericWeights must be numericImpacts must be either+(for features that are to be maximised)-(for features that are to be minimized)Both Impacts & Weights must be seperated by a comma,Exampleinput.csvAttributePriceStorageCameraLooksM125016125M22001683M330032164M42753284M522516162On running the following commandtopsis input.csv "0.25,0.25,0.25,0.25" "-,+,+,+" output.csvoutput.csvAttributePriceStorageCameraLooksTopsis ScoreRankM1250161250.5342693M220016830.3083145M3300321640.6916861M427532840.5348072M5225161620.4012224LicenseMIT
Package: topsis-antuanant
What is TOPSIS
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the altenrative of shortest Euclidean distance from the idael solution, and fartherst distance from the negative-ideal solution. More details at wikipedia. The TOPSIS algorithm is succintly explained in this paper comparing TOPSIS and VIKOR methodsUsing TOPSIS-Python
TOPSIS-Python can be run as in the following example:import numpy as np
from topsis import topsis
a = [[7, 9, 9, 8], [8, 7, 8, 7], [9, 6, 8, 9], [6, 7, 8, 6]]
w = [0.1, 0.4, 0.3, 0.2]
I = np.array([1, 1, 1, 0]
decision = topsis.topsis(a, w, I)
The decision matrix (a) should be constructed with each row representing an alternative, and each column representing a criterion. We have used an example given in TOPSIS Method in MADM (Dr. Farhad Faez)Weights (w) is not already normalised will be normalised upon initialisation. Information on benefit (1) cost (0) criteria should be provided in I.By default, the optimisation (TOPSIS calculation) does not take place. No values are stored in decision.C or decision.optimum_choice.These can be calculated, either by calling decision.calc(), or by calling a representation of the decision (which will itself call decision.calc()):decisionAlternatives ranking C:
[0.74269409 0.40359933 0.17586999 0.44142927]Best alternative
a[0]: [7. 9. 9. 8.]The rankings are saved in decision.C, with the highest ranking $0.74269409$ offering us the best decision, and lowest ranking $0.17586999$ offering the worst decision making, according to TOPSIS method.We are also then shown the best alternative index (which happens to be index 0 in this example), and the associated criteria coefficients of this alternative.Anant Chopra  101703063
Aarshi Arora  101703005