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 design_matrix_classification(N, kernel_mode, X):
ret = np.ndarray(shape=(N, N))
for i in range(0, N):
for j in range(0, N):
if (kernel_mode == "polynomial"):
ret[i, j] = kernel.polynomial_kernel(X[i], X[j - 1])
elif (kernel_mode == "linear_spline"):
ret[i, j] = kernel.linear_spline(X[i], X[j - 1])
elif (kernel_mode == "gaussian"):
ret[i, j] = kernel.gaussian(X[i], X[j - 1])
elif (kernel_mode == "rbf"):
ret[i, j] = kernel.rbf(X[i], X[j - 1])
return ret
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