def test_kernel_values(self):
self.assertEqual(kernel.linear_kernel(np.array([1, 0]), np.array([0, 0])), 0)
self.assertEqual(kernel.linear_kernel(np.ones(10), np.ones(10)), 10)
desc = np.zeros(2).reshape(2, 1)
self.assertEqual(kernel.gaussian_kernel(desc, desc, 10)[0, 0], 1)
desc1 = np.array([sqrt(2)*3, 0]).reshape(2, 1)
desc2 = np.zeros(2).reshape(2, 1)
self.assertAlmostEqual(kernel.gaussian_kernel(desc1, desc2, 3)[0, 0], exp(-1), 7)
def predict(relevance_vectors, X, mean, kernel_choice):
prediction = []
for xi in range(len(X)):
phi_x = 0
for ri in range(len(relevance_vectors)):
if kernel_choice == "gaussian":
phi_x += mean[ri + 1] * (kernel.gaussian(
X[xi], relevance_vectors[ri])) + mean[0]
elif kernel_choice == "linear":
t1 = X[xi]
phi_x += mean[ri + 1] * (kernel.linear_kernel(
X[xi], relevance_vectors[ri])) + mean[0]
elif kernel_choice == "polynomial":
t1 = X[xi]
phi_x += mean[ri + 1] * (kernel.polynomial_kernel(
X[xi], relevance_vectors[ri])) + mean[0]
elif kernel_choice == "linear_spline":
phi_x += mean[ri + 1] * (kernel.linear_spline(
X[xi], relevance_vectors[ri])) + mean[0]
elif kernel_choice == "rbf":
phi_x += mean[ri + 1] * (kernel.rbf(
X[xi], relevance_vectors[ri])) # + mean[0]
phi_x += mean[0]
prediction.append(phi_x)
return prediction
def Relevance_Vector_Classification_Prediction (Xtest, relevance_vectors, weightMaxPosteriori, kernel_choice):
Psum = 0
res = []
for xi in range(len(Xtest)):
for ri in range (len(relevance_vectors)):
if kernel_choice=="gaussian":
Psum+=weightMaxPosteriori[ri+1]*(kernel.gaussian(Xtest[xi], relevance_vectors[ri])) + weightMaxPosteriori[0]
elif kernel_choice=="linear":
Psum+=weightMaxPosteriori[ri+1]*(kernel.linear_kernel(Xtest[xi], relevance_vectors[ri])) + weightMaxPosteriori[0]
elif kernel_choice=="polynomial":
Psum+=weightMaxPosteriori[ri+1]*(kernel.polynomial_kernel(Xtest[xi], relevance_vectors[ri])) + weightMaxPosteriori[0]
elif kernel_choice=="linear_spline":
Psum+=weightMaxPosteriori[ri+1]*(kernel.linear_spline(Xtest[xi], relevance_vectors[ri])) + weightMaxPosteriori[0]
y = sigmoid_function(Psum)
if y >0.5:
res.append(1)
elif y<= 0.5:
res.append(0)
return res
def __linear(self, data, dx, dy):
shifted = self.__neighbor(data, dx, dy)
linear_kernel(data, dx, dy, shifted[1:, 1:])
return shifted
def test_linear_energy_matrix_shape(self):
desc1 = np.ones((50, 5))
desc2 = np.ones((400, 5))
shape = np.shape(kernel.linear_kernel(desc1, desc2))
self.assertEqual(shape, (50, 400))