def test(self, testing_data_matrix, testing_label_matrix):
testing_label_matrix = numpy_helper.transfer_values(testing_label_matrix, self.transfer_hash)
row_count = testing_data_matrix.shape[0]
errors = tuple(
self.__classify(testing_data_matrix[i, :]) != numpy.sign(testing_label_matrix[i, 0])
for i in range(row_count)
)
self.last_test_info = self.__package_info(errors.count(True), row_count)
return self.last_test_info
def train(
cls,
data_matrix,
label_matrix,
edge_threshold,
tolerance,
max_iteration_count,
arg_exp,
transfer_hash={1: 1, -1: -1},
):
label_matrix = numpy_helper.transfer_values(label_matrix, transfer_hash)
smo_basic = SmoBasic(data_matrix, label_matrix, edge_threshold, tolerance, arg_exp)
alphas, b = smo_basic.cal_alphas_and_b(max_iteration_count)
nonzero_indexs = numpy.nonzero(alphas.A > 0)[0]
selected_data_matrix = data_matrix[nonzero_indexs]
selected_alphas_label_matrix = numpy.multiply(label_matrix[nonzero_indexs], alphas[nonzero_indexs])
return cls(selected_data_matrix, selected_alphas_label_matrix, b, arg_exp, transfer_hash)
