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)