def test3Class():
X1 = np.array([[3, 1], [3, -1], [5, 1], [5, -1]])
X2 = np.array([[1, 0], [0, 1], [0, -1], [-1, 0]])
X3 = np.array([[1, 10], [3, 11]])
X_train = np.concatenate((X1, X2))
X_train = np.concatenate((X_train, X3))
y_train = np.array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3]).reshape(10, 1)
XStar = np.eye(10)
X_test = np.array([[4, 1], [5, 0], [4, 0], [4, 1], [0, 0], [-1, -2],
[1, 2], [1, -2]])
y_test = np.array([1, 1, 1, 1, 2, 2, 2, 2]).reshape(8, 1)
svmp = svmplus.SVMPlus(svm_type="LIBSVM",
C=10,
gamma=.01,
kernel_x="rbf",
degree_x=3,
gamma_x=.001,
kernel_xstar="rbf",
degree_xstar=3,
gamma_xstar=0.001,
tol=1e-10)
svmp.fit(X_train, XStar, y_train)
y_predict = svmp.predict(X_test)
print(y_predict)
correct = np.sum(y_predict == y_test)
print("Prediction accuracy of SVM")
print("%d out of %d predictions correct" % (correct, len(y_predict)))
def testSVMPlus():
# X_train, X_test, y_train, y_test, XStar = 2*np.eye(3), 3*np.eye(3), [1,1,-1], [1,1,-1], 2*np.eye(3)
# X1 = np.array([[3, 1], [3, -1], [6, 1], [6, -1]])
# X2 = np.array([[1, 0], [0, 1], [0, -1], [-1, 0]])
# X_train = XStar = np.concatenate((X1, X2))
# y_train = [1, 1, 1, 1, -1, -1, -1, -1]
X_train = np.array([[17, 24, 1, 8, 15], [23, 5, 7, 14, 16],
[4, 6, 13, 20, 22], [10, 12, 19, 21, 3],
[11, 18, 25, 2, 9]])
y_train = [1, 1, -1, 1, -1]
XStar = np.eye(5)
XStar[0, 0] = 1
XStar[1, 1] = 2
XStar[2, 2] = 3
XStar[3, 3] = 4
XStar[4, 4] = 5
# train and predict using SVM plus
# svmp = SVMPlus(C=1, gamma=1, kernel_x= "rbf", gamma_x = .0019,
# kernel_xstar="rbf", gamma_xstar = 0.0568)
# svmp.fit(X_train, XStar, y_train)
svmp = svmplus.SVMPlus(svm_type="LIBSVM",
C=1,
gamma=1,
kernel_x="linear",
degree_x=2,
gamma_x=.0019,
kernel_xstar="linear",
degree_xstar=2,
gamma_xstar=0.0568)
svmp.fit(X_train, XStar, y_train)
def test3Class():
X1 = np.array([[3, 1], [3, -1], [5, 1], [5, -1]])
X2 = np.array([[1, 0], [0, 1], [0, -1], [-1, 0]])
X3 = np.array([[1, 10], [3, 11]])
X_train = np.concatenate((X1, X2))
X_train = np.concatenate((X_train, X3))
y_train = np.array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3]).reshape(10, 1)
XStar = np.eye(10)
X_test = np.array([[3, 0], [5, 0], [4, 0], [4, 1], [0, 0], [-1, -2],
[1, 2], [1, -2]])
y_test = np.array([1, 1, 1, 1, 2, 2, 2, 2]).reshape(8, 1)
svmp = svmplus.SVMPlus(svm_type="QP",
C=1000,
gamma=.1,
kernel_x="rbf",
degree_x=2,
gamma_x=.00001,
kernel_xstar="rbf",
degree_xstar=2,
gamma_xstar=0.00001)
#svmp = libsvm.LibSVMPlus(C=100, gamma=.0001, kernel_x="rbf", degree_x=2, gamma_x=.0001,
# kernel_xstar="rbf", degree_xstar=2, gamma_xstar=0.00001)
svmp.fit(X_train, XStar, np.array(y_train).reshape(len(X_train), 1))
y_predict = svmp.predict(X_test)
print(y_predict)
def testPolynomial():
X_train, X_test, y_train, y_test, XStar = prepareDigitData()
svmp = svmplus.SVMPlus(svm_type="LIBSVM",
C=10,
gamma=.01,
kernel_x="poly",
kernel_xstar="poly",
tol=1e-10)
svmp.fit(X_train, XStar, y_train)
y_predict = svmp.predict(X_test)
correct = np.sum(y_predict.flat == y_test)
print("Prediction accuracy of SVM")
print("%d out of %d predictions correct" % (correct, len(y_predict)))
def testLinearSVMPlus():
X_train, X_test, y_train, y_test, XStar = prepareDigitData()
svmp = svmplus.SVMPlus(svm_type="QP",
C=1000,
gamma=.00001,
kernel_x="linear",
kernel_xstar="linear",
tol=1e-10)
svmp.fit(X_train, XStar, np.array(y_train).reshape(len(X_train), 1))
y_predict = svmp.predict(X_test)
correct = np.sum(y_predict == y_test)
print("Prediction accuracy of SVM")
print("%d out of %d predictions correct" % (correct, len(y_predict)))
def testLinearSVMPlus():
X_train, X_test, y_train, y_test, XStar = prepareDigitData()
y_test = np.array(y_test)
svmp = svmplus.SVMPlus(svm_type="LIBSVM",
C=10,
kernel_x="linear",
kernel_xstar="linear",
tol=1e-10)
svmp.fit(X_train, XStar, y_train)
y_predict = svmp.predict(X_test)
correct = np.sum(y_predict.flat == y_test)
print("Prediction accuracy of SVM")
print("%d out of %d predictions correct" % (correct, len(y_predict)))
def testRbfSVMPlus():
X_train, X_test, y_train, y_test, XStar = prepareDigitData()
# train and predict using SVM plus
svmp = svmplus.SVMPlus(svm_type="LIBSVM",
C=10,
gamma=.00001,
kernel_x="rbf",
gamma_x=.000001,
kernel_xstar="rbf",
gamma_xstar=.000001,
tol=1e-10)
svmp.fit(X_train, XStar, y_train)
y_predict = svmp.predict(X_test)
correct = np.sum(y_predict.flat == y_test)
print("Prediction accuracy of SVM")
print("%d out of %d predictions correct" % (correct, len(y_predict)))
