The research thesis report in Russian presented in report.pdf
Firstly import package
from mk_ls_svm_lib as mk
Create instance of classificator with list of kernels
kernel_set = [mk.kernel.RBF(10), mk.kernel.Poly(1,2)] clf = mk.mk_ls_svm.MKLSSVM(kernel_set)
Fit classificator
import numpy as np X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) clf = clf.fit(X,y)
Predict
predicted_y = clf.predict(X)
You can save your classificator into file
clf.to_pkl('my_clf.pkl')
And load it
clf = mk.mk_ls_svm.load_clf_from_pkl('my_clf.pkl')
Also you can use built-in k-fold crossvalidation
score = mk.crossvalidation.cross_val_score(clf, X, y)