This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution) approach to select best among many things based on different attributes, such as selecting the best Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy. You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-' where '+' indicates that you need to maximise value of that particular column and '-' indicates that you need to minimise value of that particular column. After passing all these parameters you will get your rank row.
1 maintainer
Metadata
1 maintainer
Release files for tpsisar18 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| tpsisar18-0.0.2.tar.gz | 2.2 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tpsisar18-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.7 kB
Release files / tpsisar18-0.0.2.tar.gz
| Download URL | tpsisar18-0.0.2.tar.gz |
|---|---|
| Size | 2.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
150c09f071531ba9e04f3f0b26c9fbc3bf68efee5813c78e5f9e48097da10fc1
|
|
BLAKE2b-256 checksum How to use checksums |
afa7b5d11158785cc50dfa6e3dddd78cc4033eb571e5cd8aa8bf0c516fae3b35
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.5
|
Release files / tpsisar18-0.0.2-py3-none-any.whl
| Download URL | tpsisar18-0.0.2-py3-none-any.whl |
|---|---|
| Size | 3.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a7b4a56986c995d409a0ab9415eab7758b7a20ae5446497d93f6f34dfab132b2
|
|
BLAKE2b-256 checksum How to use checksums |
410ebe1e2b8e3753d0b76f3a092b44e924d0aff0f1e2ea8bf739b83f1b298fe6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.5
|
Security for tpsisar18 0.0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
