from OLP.Models.SVM import SVM
from OLP.Metrics.ClassificationMetrics import *
if __name__ == '__main__':
xs = [[-1., -1.], [-2., -1.], [1., 1.], [2., 1.]]
ys = [0, 0, 1, 1]
clf = SVM()
clf.fit(xs, ys)
ysTrue = [0, 0, 1, 1]
ysPred = clf.predict([[-0.8, -1], [-0.7, 0.6], [0.9, 0.9], [-0.8, -0.8]])
print 'accuracy =', getAccuracy(ysTrue, ysPred)
print 'precision =', getPrecision(ysTrue, ysPred)
print 'recall =', getRecall(ysTrue, ysPred)
print 'f1 =', getF1(ysTrue, ysPred)
print 'auc =', getAUC(ysTrue, ysPred)
print 'confusion matrix =\n', getConfusionMatrix(ysTrue, ysPred)
from OLP.Models.SVM import SVM
if __name__ == '__main__':
xs = [[-1., -1.], [-2., -1.], [1., 1.], [2., 1.]]
ys = [1, 1, 2, 2]
clf = SVM()
clf.fit(xs, ys)
print clf.predict([[-0.8, -1]])
from OLP.Models.SVM import SVM
if __name__ == '__main__':
xs = [[-1., -1.], [-2., -1.], [1., 1.], [2., 1.]]
ys = [1, 1, 2, 2]
clf = SVM()
clf.fit(xs, ys)
print clf.predict([[-0.8, -1]])
from OLP.Models.SVM import SVM
from OLP.Metrics.ClassificationMetrics import *
if __name__ == '__main__':
xs = [[-1., -1.], [-2., -1.], [1., 1.], [2., 1.]]
ys = [0, 0, 1, 1]
clf = SVM()
clf.fit(xs, ys)
ysTrue = [0, 0, 1, 1]
ysPred = clf.predict([[-0.8, -1], [-0.7, 0.6], [0.9, 0.9], [-0.8, -0.8]])
print 'accuracy =', getAccuracy(ysTrue, ysPred)
print 'precision =', getPrecision(ysTrue, ysPred)
print 'recall =', getRecall(ysTrue, ysPred)
print 'f1 =', getF1(ysTrue, ysPred)
print 'auc =', getAUC(ysTrue, ysPred)
print 'confusion matrix =\n', getConfusionMatrix(ysTrue, ysPred)