x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)

在没有使用sklearn库的情况下，你可以自己实现一个简单的train_test_split函数来划分训练集和测试集。以下是一个示例代码：

import numpy as np

def train_test_split(x, y, train_size=0.7, random_state=None):
    if random_state is not None:
        np.random.seed(random_state)
        
    indices = np.arange(len(x))
    np.random.shuffle(indices)
    
    train_size = int(train_size * len(x))
    train_indices = indices[:train_size]
    test_indices = indices[train_size:]
    
    x_train = x[train_indices]
    x_test = x[test_indices]
    y_train = y[train_indices]
    y_test = y[test_indices]
    
    return x_train, x_test, y_train, y_test

使用这个函数，你可以将输入的特征向量x和标签y按照指定的train_size比例划分为训练集和测试集。random_state参数用于控制随机划分的种子，以确保结果的可重复性。调用这个函数后，会返回划分好的x_train, x_test, y_train, y_test四个数组。你可以根据自己的需要进行后续的模型训练和评估。