KCluster
This is project is a class that allows for a combination of clustering numeric and string data using kmean, kmedians, and kmodes all in one. You can also weight your variables.
Installation
Run the following to install:
pip install kcluster
Usage
from kcluster import KCluster
model = KCluster()
model.fit(X)
Development kcluster
To install kcluster, along with the tools you need to develop and run tests, run the following in your virtualend:
$ pip install -e .[dev]
1 maintainer
Metadata
1 maintainer
Release files for kcluster 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 | |
|---|---|---|---|
| kcluster-0.0.2.tar.gz | 10.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| kcluster-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.9 kB
Release files / kcluster-0.0.2.tar.gz
| Download URL | kcluster-0.0.2.tar.gz |
|---|---|
| Size | 10.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
|
Release files / kcluster-0.0.2-py3-none-any.whl
| Download URL | kcluster-0.0.2-py3-none-any.whl |
|---|---|
| Size | 11.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
|
