Kernel SVM library based on sklearn and GPlib.
Project description
kSVMlib
Kernel SVM library based on sklearn and GPlib. Provides similar functionality to GPlib for SVMs.
Setup kSVMlib
- Create and activate virtualenv (for python2) or venv (for python3)
# for python3
python3 -m venv .env
# or for python2
python2 -m virtualenv .env
source .env/bin/activate
- Upgrade pip
python -m pip install --upgrade pip
- Install kSVMlib package
python -m pip install ksvmlib
Use kSVMlib
- Import kSVMlib to use it in your python script.
import ksvmlib
- Generate some random data.
import numpy as np
data = {}
data['X'] = np.vstack((
np.random.multivariate_normal([1, 1], [[1, 0], [0, 1]], 100),
np.random.multivariate_normal([3, 3], [[1, 0], [0, 1]], 100)
))
data['Y'] = np.vstack((
np.ones((100, 1)),
np.zeros((100, 1)),
))
validation = ksvmlib.dm.RandFold(fold_len=0.2, n_folds=1)
train_set, test_set = validation.get_folds(data)[0]
- Initialize the KSVM model and a metric to measure the results.
model = ksvmlib.KSVM(ksvmlib.ker.SquaredExponential())
accuracy = ksvmlib.me.Accuracy()
- Fit the model to the data.
fitting_method = ksvmlib.fit.GridSearch(
obj_fun=accuracy.fold_measure,
max_fun_call=300
)
train_validation = ksvmlib.dm.RandFold(fold_len=0.2, n_folds=3)
log = fitting_method.fit(model, train_validation.get_folds(
train_set
))
print("Fitting log: {}".format(log))
- Finally plot the results.
print("Accuracy: {}".format(accuracy.measure(model, train_set, test_set)))
ksvmlib.plot.kernel_sort_data(model, test_set)
- There are more examples in examples/ directory. Check them out!
Develop kSVMlib
- Update API documentation
source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../ksvmlib
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ksvmlib-0.1.0.tar.gz
(17.1 kB
view hashes)
Built Distribution
Close
Hashes for ksvmlib-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9eaf0fded0677269662585cb2c8b3eb283215a1a3c554d6ca044dece2aa53aa9 |
|
MD5 | d0e8ec253b101a9f3f0c275c40bb51c6 |
|
BLAKE2b-256 | 242c69c1d04fbafd4fdf5f913ffe453feea83c118e3fa8c1917b361fd7a9d782 |