Saravanan666/pypi-org huggingface dataset:
Package: topsis-hm
UCS633 Project SubmissionName-Haymant ManglaRoll no.-101703228topsis-hmtopsis-hm is a Python package for displaying ranking of all criteria using Topsis technique to get good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form.Topsis DescriptionTechnique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is one of the multi-criteria models in making decision which is known for its  simplicity, rationality, comprehensibility and good computational efficiency. Multi-criteria decision making (MCDM) refers to making choice of the best alternative from among a finite set of decision alternatives in terms of multiple, usually conflicting criteria.Getting StartedThese instructions will help you to install and use this package for general use.PrerequisitesYour csv file should not have categorical dataInstallationUse the package managerpipto install foobar.pipinstalltopsis-hmUsageYou can import it either in Python IDLE or run directly through command promptFor Command PromptIf you want to use this package on "data.csv" file with 4 columns. You need to change the directory where "data.csv" is stored then. Here -w represents weights which signifies weight of each feature or column in our dataset and -i represents impacts which signifies impact of each column or feature in our data. If a feature is good we will use + to denote else we will use -toposis-hmdata.csv-w1111-i++-+You can use the following command for helptopsis-hm-hFor Python IDLEfromtopsis-hm.topsisimporttoptop(X,weights,impacts)#X should be a matrix#impacts should be a list of string + for positive impact - for negative impact#weights should be a list of int or floatContributingPull 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-ikjot-102116071
Topsis-Ikjot-102116071for:Project-1 (UCS654)submitted by:Ikjot SinghRoll no:102116071Group:3CS11Topsis-Ikjot-102116071 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-ikjot-102116071.pipinstalltopsis-ikjot-102116071UsageEnter 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 (+,-)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.| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) || :----: |:--------------------:|:------------:|:------------:|:---------------:|| M1 | 16 | 12 | 250 | 5 || M2 | 16 | 8  | 200 | 3 || M3 | 32 | 16 | 300 | 4 || M4 | 32 | 8  | 275 | 4 || M5 | 16 | 16 | 225 | 2 |weights 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     4topsis-ikjot-102116071
Package: topsis-Inderjeet-101803689
topsis_packageIt is a python package which is used to find best among the value using mathematical model(topsis)InstallationUse the package managerpipto install this package.pipinstalltopsis-Inderjeet-101803689AboutTOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA 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 thoseTo get started quickly, just use the following:fromtopsis_pckg.topsisimporttopsis
topsis('file_name','Weight','Impact','outputfile_name')make ensure the weights and impacts should be in ""eg: "1,1,1,1" and "+,-,+,-"Pre-requisiteThe data should be enclosed in the csv file. There must be more than 2 columnsResultthe output(outputfile_name)  is saved in the project folder with extra 2 columns with topsis score and rank.LicenseÂ© 2020  Inderjeet SinghMIT
Package: topsis-ishaan
topsis-pythonTopsis analysis of a csv fileUCS633 Project SubmissionName - Ishaan VijhRoll no. - 101703245About 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).Installationpipinstalltopsis-ishaanUsageFollowing query on terminal will provide you the topsis analysis for input csv file.topsis-ishaan -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 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-ishaan-102103281
TOPSISAuthor:Ishaan Gaba.Type:Package.Title:TOPSIS method for multiple-criteria decision making (MCDM).Version:1.1.0Date:2024-01-21Maintainer:Ishaan Gabaigaba_be21@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.How to install this package:>> pip installIn Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csvLicenseÂ© 2024 Ishaan GabaThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-ishan-101816037
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 alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. MoreHow to use this package:
TOPSIS-Ishan-101816037 can be run as in the following example:In Command Prompt:
>> topsis-Ishan-101816037 data.csv “1,1,1,1” “+,+,-,+” result.csv
Package: topsis-iyasha-102103034
For:Assignment-1 (UCS654)Submitted by:iyashaRoll No:102103034Group:3COE2Topsis_iyasha_102103034Topsis_iyasha_102103034 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_iyasha_102103034 using Pypi via pip.$pipinstalltopsis_iyasha_102103034UsageYou can use this package in python as :fromtopsis_iyasha_102103034importtopsisinputFile="sample.csv"weights="1,1,1,1"impacts="-,+,+,+"resultFile="result.csv"topsis(inputFile,weights,impacts,resultFile)ORYou can use this package via commandline as :$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 iyashaPermission 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-jamesfallon
TOPSIS-PythonInspired byTOPSIS-Python(python2).Our python 3 code follows the same structure, defining a topsis class,
but uses numpy linear algebra in order to modernise, optimise, and remove
redundant code.What is TOPSISTechnique 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 farthest distance from the negative-ideal
solution. More details atwikipedia. The
TOPSIS algorithm is succinctly explained in thispaper comparing TOPSIS and
VIKOR methodsUsing TOPSIS-PythonTOPSIS-Python can be run as in the following example:>>> 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 = [1, 1, 1, 0]
>>> decision = 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 inTOPSIS 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
inI.By default, the optimisation (TOPSIS calculation) does not take place. No values
are stored indecision.Cordecision.optimum_choice.These can be calculated, either by callingdecision.calc(), or by calling a
representation of the decision (which will itself calldecision.calc()):>>> decision

Alternatives ranking C:
[0.74269409 0.40359933 0.17586999 0.44142927]

Best alternative
a[0]: [7. 9. 9. 8.]The rankings are saved indecision.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.InstallationThe module can be installed from pypi using pip, or downloaded and installed
directly from gitpypi:https://pypi.org/project/topsis-jamesfallongit:https://gitlab.com/jamesfallon/topsis-python.gitpipInstallation with pip can be achieved in one line:pip install topsis-jamesfallonAnacondaIf you are using anaconda, make sure that you are using your environment'spip, and not the OS default. Your installationonly if using anacondamay
look like:Activate your environment(base) $ conda activate custom_envEnsure pip is installed in your environment(custom_env) $ conda install pipMake sure you are using your environment's pip installation. If not, you can always prepend the full path before the pip command.(custom_env) $ which pip
.../path_to_your_anaconda_installation/anaconda3/envs/custom_env/bin/pipNow install topsis from pypi(custom_env) $ pip install topsis-jamesfallon
Collecting topsis-jamesfallon
Installing collected packages: topsis-jamesfallon
Successfully installed topsis-jamesfallon-0.2
Package: topsis-jaskaran-101803660
This is a topsis program using python and is a cli based program. Use python topsis-jaskaran-101803660 <InputDataFile> <Weights> <Impacts> <ResultFileName>Change Log0.0.1(13/11/20)First Release0.0.2(13/11/20)Bug Fixes0.0.3(13/11/20)More Bug Fixes1.0.0(13/11/20)Stable Build
Package: topsis-jasleen-102003238
Topsis-jasleen-102003238Topsis implementation in python. Used as a command line tool to generate topsis score and ranks of the given input data file along with weights and impacts. The method helps to find the best alternative from a set of options by comparing each option's similarity to an ideal solution and a negative-ideal solution, which are defined based on the decision-maker's preferences.Installationpip install Topsis-jasleen-102003238How to use it?Open the terminal / cmd and type topsis <input_filename.csv> <"Weights"> <"Impacts"> <output_filename.csv>License© 2023 jasleenThis repository is licensed under the MIT license. See LICENSE for details.
Package: topsis-jatin-101866020
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 alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. MoreHow to use this package:
TOPSIS-Jatin-101866020 can be run as in the following example:In Command Prompt:
>> topsis-jatin-101866020 data.csv “1,1,1,1” “+,+,-,+” result.csv
Package: topsis-kamakshi
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 methods.Just install my package using pip install topsis-kamakshi , import it and use the class topsis’s evaluate function to get the result.The code is included inside too as topsis_code.py
Package: topsis-kanuj-102017188
Topsis Python PackageThis is a Python Package implementing Topsis Multi-Criteria Decison Making method.It is a simple implementation using
- pandas
- numpyIt stores the output as a csv file with user-specified name.Output contains the original data with two additional columns i.e. Topsis Score and Rank based on the score.InstallationTo install the packagepip install topsis-kanuj-102017188UsageTo use the package from the command line (Data file and result file name should contain .csv extension)topsis [data file name] [weights as string seperated by ','] [impacts as string seperated by ','] [result data file name]For exampletopsis data.csv "1,1,1,1,1" "+,+,+,-,+" result.csvSampleData SampleModel,StorageSpace,Camera,Price,Looks
M1,16,12,250,5
M2,16,8,200,3
M3,32,16,300,4
M4,32,8,275,4
M5,16,16,225,2Sample topsis commandtopsis data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csvSample Output fileModel,StorageSpace,Camera,Price,Looks,TopsisScore,Rank
M1,16,12,250,5,0.534277,3.0
M2,16,8,200,3,0.308368,5.0
M3,32,16,300,4,0.691632,1.0
M4,32,8,275,4,0.534737,2.0
M5,16,16,225,2,0.401046,4.0NotesThe data should only contain numerical data.
Package: topsis-kartik-102003565
TOPSIS Package in PythonSubmitted by: Kartik MadanRoll no: 102003565TOPSISTOPSIS is an acronym that stands for Technique of Order Preference Similarity to the Ideal Solution and is a pretty straightforward MCDA 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.How to useThe package topsis-kartik-102003565 can be run though the command line as follows:>> pip install topsis-kartik-102003565>>python topsis data.csv "1,1,1,1" "+,+,-,+" resultSample InputThe decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.Fund NameP1P2P3P4M125016125M22001683M330032164M42753284M522516162Weights `weights` is not already normalised will be normalised later in the code.Information of benefit positive(+) or negative(-) impact criteria should be provided inimpacts.Sample OutputThe output that generated from the following input will be:ModelP1P2P3P4Topsis ScoreRankM1250161250.5342773M220016830.3083685M3300321640.6916321M427532840.5347372M5225161620.4010464The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .
Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.
Package: topsis-kartikye-102103708
TopsisTOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Kartikye Chadda, 102103708, TIET, Patiala.InstallationUse the package manager pip to install topsis-3283.pip install topsis_kartikye_102103708UsageEnter 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]Exampletopsis_kartikye_102103708 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 Katikye ChaddaThis reopsitory is licensed under MIT License. See LICENSE for details.
Package: topsis-khushi-102103479
topsis-khushi-102103479submitted by: Khushi attri Roll no: 102103479 Group: 3COE17topsis-khushi-102103479 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-khushi-102103479.pip install topsis-khushi-102103479UsageEnter 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 (+,-)topsis sample.csv "1,1,1,1" "+,-,+,+"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 = [ + , + , - , + ]input:topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"output:TOPSIS RESULTSP-Score  Rank1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4License2024 Khushi Attri
Package: topsis-kjindal-101703299
Ranking System Using TopsisProject 1 : UCS633Submitted By:Kunal Jindal 101703299pypi:https://pypi.org/project/topsis-kjindal-101703299/InstallationUse the package managerpipto install topsis-kjindal-101703299.pipinstalltopsis-kjindal-101703299How to use this package:topsis-kjindal-101703299 can be run as shown below:In Command Prompt>> topsis data.csv "1,1,1,1" "+,+,-,+"In Python IDLE:>>>importpandasaspd>>>fromtopsis_python.topsisimporttopsis>>>data=pd.read_csv('data.csv').values>>>data=data[:,1:]>>>w=[1,1,1,1]>>>impacts=["+","+","-","+"]>>>topsis(data,w,impacts)Sample datasetThe decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.ModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Weights list is not already normalised will be normalised later in the code.Information of benefit positive(+) or negative(-) impact criteria should be provided inimpacts.OutputModel   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Package: topsis-kritika-101983031
TOPSIS_kritika_101983031 implementation of Technique of Order Preference Similarity to the Ideal Solution. it is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.
to run from command line:
following format must be followed:
Usages:
python topsis.py    
Example:
python topsis.py inputfile.csv “1,1,1,2” “+,+,-,+” result.csv
Package: topsis-Kushagra-101917112
TOPSIS-PythonSubmitted By:Kushagra 101917112What is TOPSISTechnique for **Order **Preference by **Similarity to **IdealSolution (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. More details atwikipedia.How to use this package:TOPSIS-Kushagra 101917112  can be run as in the following example:In Command Prompttopsis data.csv "1,1,1,1" "+,+,-,+"Sample datasetThe decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.ModelCorrelationR2RMSEAccuracyM10.790.621.2560.89M20.660.442.8963.07M30.560.311.5762.87M40.820.672.6870.19M50.750.561.380.39Information of benefit positive(+) or negative(-) impact criteria should be provided inI.OutputModel   Score    Rank1    0.77221     2
2    0.225599    5
3    0.438897    4
4    0.523878    3
5    0.811389    1