SVM_by_SMO

Jul 28, 2017

e426ffd · Jul 28, 2017

Name	Name	Last commit message	Last commit date
parent directory ..
small_data	small_data	add SVM SMO and QP	Jul 28, 2017
README.md	README.md	add SVM SMO and QP	Jul 28, 2017
SVCSMO.py	SVCSMO.py	add SVM SMO and QP	Jul 28, 2017
SVCSMO.pyc	SVCSMO.pyc	add SVM SMO and QP	Jul 28, 2017
testSVM-SMO.py	testSVM-SMO.py	add SVM SMO and QP	Jul 28, 2017

README.md

Simple implementation of a Support Vector Classification using the Sequential Minimal Optimization (SMO) algorithm for training.

Setup model (following parameters are default)

from SVCSMO import SVCSMO
model = SVCSMO(max_iter=10000, kernel_type='linear', C=1.0, epsilon=0.001)

Train model

model.fit(X, y)

Predict new observations

y_hat = model.predict(X_test)