def xgb(x_train, y_train, x_test, y_test):
model = XGBClassifier()
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
performance_metrics.performance(y_test, y_pred)
def SVM(x_train, y_train, x_test, y_test):
clf = SVC(kernel='rbf')
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
print performance_metrics.performance(y_test, y_pred)
with open('SVM_cifar', 'wb') as f:
cPickle.dump(clf, f)
def KNN(x_train, y_train, x_test, y_test):
neigh = KNeighborsClassifier(n_neighbors=21)
neigh.fit(x_train, y_train)
y_pred = neigh.predict(x_test)
return performance_metrics.performance(y_test, y_pred)
def SVM(x_train, y_train, x_test, y_test):
# clf = SVC(kernel = 'linear')
# print "Linear kernel"
# clf.fit(x_train, y_train)
# y_pred = clf.predict(x_test)
# print performance_metrics.performance(y_test, y_pred)
print "rbf kernel"
clf = SVC(kernel='rbf', C=10, gamma=10)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
print performance_metrics.performance(y_test, y_pred)
#print y_test[1:50],y_pred[1:50]
# print "polynomial kernel"
# clf = SVC(kernel = 'poly',degree=3,C=10)
# clf.fit(x_train, y_train)
# y_pred = clf.predict(x_test)
# print performance_metrics.performance(y_test, y_pred)
return clf
def SVM(x_train, y_train, x_test, y_test): clf = SVC(kernel = 'linear') print "Linear kernel" clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print performance_metrics.performance(y_test, y_pred) print "rbf kernel" clf = SVC(kernel = 'rbf',C=1,gamma=0.1) clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print performance_metrics.performance(y_test, y_pred) print "polynomial kernel" clf = SVC(kernel = 'poly',degree=2,C=10) clf.fit(x_train, y_train) y_pred = clf.predict(x_test) print performance_metrics.performance(y_test, y_pred)