def classiy(data, target):
num_k = 10
score_SVC = 0
skf = StratifiedKFold(n_splits=num_k)
for train_index, test_index in skf.split(data, target):
X_train, X_test = data[train_index], data[test_index]
y_train, y_test = target[train_index], target[test_index]
n, d = X_train.shape
# 这里的gamma好像也是可以训练出来的
gamma = 1.0 / d
kernel_functions = [
k_helpers.create_linear_kernel,
# k_helpers.create_rbf_kernel(gamma),
# k_helpers.create_poly_kernel(3, gamma),
# k_helpers.create_exponential_kernel(gamma),
# k_helpers.create_histogram_kernel,
# k_helpers.create_histogram_kernel,
# k_helpers.create_exponential_kernel(gamma),
# k_helpers.create_sigmoid_kernel(gamma)
]
for j in range(y_train.size):
if y_train[j] != 0:
y_train[j] = -1
else:
y_train[j] = 1
for i in range(y_test.size):
if y_test[i] != 0:
y_test[i] = -1
else:
y_test[i] = 1
# 核函数与权重之间进行线性组合 形成新的合成后的核函数 给SVM 进行分类
clf = svm.SVC(kernel='precomputed')
kernel_matrices = k_helpers.get_all_kernels(X_train, kernel_functions)
# 固定权重设置
# new_train = k_helpers.get_combined_kernel(kernel_matrices, weight_train)
# clf.fit(new_train, y_train)
# 训练权重
# 惩罚数值 C:penalty value => 0<=alpha_i<=C
C = 1
M = len(kernel_functions)
pointD = np.ones(M) / M
weights, combined_kernel, J, alpha, duality_gap, final_gamma = algo1.find_kernel_weights(pointD, kernel_matrices, C, y_train, 1, gamma)
print('************************最后算到的结果************************')
print('weights', weights)
# # print('combined_kernel', combined_kernel)
# # print('J', J)
# # print('alpha', alpha)
# # print('duality_gap', duality_gap)
print('gamma', final_gamma)
# 训练出权重了以后，接下来就是合成新的矩阵
clf.fit(combined_kernel, y_train)
# 这里的测试集需要重新调整一下
final_test_data = test_kernel_processing(X_train, X_test, kernel_functions, weights)
# no train
# final_test_data = test_kernel_processing(X_train, X_test, kernel_functions, weight_train)
score_SVC += clf.score(final_test_data, y_test)
print('一次循环的精度为%s' % (clf.score(final_test_data, y_test)))
print('SVC最后的分类精度：%s' % (score_SVC / num_k))
def kernel_chose(feature, kernel_type, gamma):
if kernel_type == 'linear':
kernel_functions = [k_helpers.create_linear_kernel]
elif kernel_type == 'rbf':
kernel_functions = [k_helpers.create_rbf_kernel(gamma)]
elif kernel_type == 'poly':
kernel_functions = [k_helpers.create_poly_kernel(2, gamma)]
elif kernel_type == 'exponential':
kernel_functions = [k_helpers.create_exponential_kernel(gamma)]
elif kernel_type == 'sigmoid':
kernel_functions = [k_helpers.create_sigmoid_kernel(gamma)]
else:
kernel_functions = [k_helpers.create_histogram_kernel]
kernel_matrices = k_helpers.get_all_kernels(feature, kernel_functions)
return kernel_matrices
def train_kernel(train_data, train_labels):
n, d = train_data.shape
nlabels = train_labels.size
# Make labels -1 and 1
for i in range(nlabels):
if train_labels[i] != 0:
train_labels[i] = -1
else:
train_labels[i] = 1
gamma = 1.0 / d
kernel_functions = [
k_helpers.create_linear_kernel,
# k_helpers.create_rbf_kernel(gamma),
# k_helpers.create_poly_kernel(2, gamma),
# k_helpers.create_poly_kernel(3, gamma),
# k_helpers.create_poly_kernel(4, gamma),
# k_helpers.create_sigmoid_kernel(gamma),
]
M = len(kernel_functions)
# The weights of each kernel
# Initialized to 1/M
d = np.ones(M) / M
# Stores all the individual kernel matrices
kernel_matrices = k_helpers.get_all_kernels(train_data, kernel_functions)
# 惩罚数值 C:penalty value => 0<=alpha_i<=C
C = 1
weights, combined_kernel, J, alpha, duality_gap, gamma = algo1.find_kernel_weights(
d, kernel_matrices, C, train_labels, 1, gamma)
print('************************最后算到的结果************************')
print('weights', weights)
print('combined_kernel', combined_kernel)
print('J', J)
print('alpha', alpha)
print('duality_gap', duality_gap)
print('gamma', gamma)
return weights
def my_kernel(u):
kernel_matrices = k_helpers.get_all_kernels(u, kernel_functions)
combined_kernel_matrix = k_helpers.get_combined_kernel(
kernel_matrices, weight_train)
return combined_kernel_matrix