fuzzy-c-means
Fuzzy c-means Clustering
Description
This implementation is based on the paper FCM: The fuzzy c-means clustering algorithm by: James C.Bezdek, Robert Ehrlich, and William Full
To run the tests
sh run_tests.sh
To run the coverage
sh run_coverage.sh
Install via pip
pip install fuzzycmeans
How to use it
- Fit the model. This is to cluster any given data X.
X = np.array([[1, 1], [1, 2], [2, 2], [0, 0], [0, 0]])
fcm = FCM(n_clusters=3, max_iter=1)
fcm.fit(X, [0, 0, 0, 1, 2])
- (Optional.) Use the model to assign new data points to existing clusters. Note that the predict function would return the membership as this a fuzzy clustering.
Y = np.array([[1, 2], [2, 2], [3, 1], [2, 1], [6, 8]])
membership = fcm.predict(Y)
Release files for fuzzycmeans 1.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fuzzycmeans-1.0.4.tar.gz | 11.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fuzzycmeans-1.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.9 kB
Release files / fuzzycmeans-1.0.4.tar.gz
| Download URL | fuzzycmeans-1.0.4.tar.gz |
|---|---|
| Size | 11.5 kB |
| Tags | Source |
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Release files / fuzzycmeans-1.0.4-py3-none-any.whl
| Download URL | fuzzycmeans-1.0.4-py3-none-any.whl |
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| Size | 12.4 kB |
| Tags | Python 3 |
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