静态模式层级检测算法代码

当涉及到静态模式层级检测算法时，有许多不同的方法和技术可供选择。以下是一个简单的例子，展示了一种基于字符串匹配的层级检测算法的代码实现：

def detect_hierarchy(patterns, text):
    results = []
    for pattern in patterns:
        if pattern in text:
            results.append(pattern)
    return results

这个函数接受两个参数：`patterns` 是一个包含要检测的模式的列表，`text` 是要在其中进行模式匹配的文本。

该算法遍历模式列表，并对每个模式执行简单的字符串匹配操作。如果某个模式在文本中出现，则将其添加到结果列表中。最后，返回结果列表。

你可以根据需要对这个简单的示例代码进行修改和扩展。例如，你可以引入更复杂的匹配算法，或者考虑使用正则表达式来进行模式匹配。

相关问题

静态代理模式的Java代码

静态代理模式是一种设计模式，它可以在不修改原始类的情况下增强其功能。以下是一个简单的静态代理模式的Java代码示例：

public interface Subject {
    void request();
}

public class RealSubject implements Subject {
    public void request() {
        System.out.println("RealSubject: Handling request.");
    }
}

public class Proxy implements Subject {
    private RealSubject realSubject;

    public Proxy() {
        realSubject = new RealSubject();
    }

    public void request() {
        System.out.println("Proxy: Logging request.");
        realSubject.request();
        System.out.println("Proxy: Logging response.");
    }
}

public class Client {
    public static void main(String[] args) {
        Proxy proxy = new Proxy();
        proxy.request();
    }
}

在上面的代码中，`Subject` 接口定义了一个 `request()` 方法，`RealSubject` 类实现了 `Subject` 接口并实现了 `request()` 方法。`Proxy` 类也实现了 `Subject` 接口，并且它包含一个实际主题的实例。

当客户端调用 `Proxy` 对象的 `request()` 方法时，`Proxy` 对象会记录请求并将其传递给实际主题（即 `RealSubject` 对象）。然后，`Proxy` 对象将响应记录并返回给客户端。

编制时间片轮转调度算法和静态优先级调度算法代码

时间片轮转调度算法的代码如下：

class Process:
    def __init__(self, pid, arrival_time, burst_time):
        self.pid = pid
        self.arrival_time = arrival_time
        self.burst_time = burst_time
        self.remaining_time = burst_time

    def execute(self, time_slice):
        if self.remaining_time > time_slice:
            self.remaining_time -= time_slice
            return time_slice
        else:
            time_executed = self.remaining_time
            self.remaining_time = 0
            return time_executed


def round_robin_scheduling(processes, time_slice):
    n = len(processes)
    remaining_processes = processes.copy()
    current_time = 0
    waiting_time = 0
    turnaround_time = 0
    
    while remaining_processes:
        current_process = remaining_processes.pop(0)
        if current_process.arrival_time > current_time:
            current_time = current_process.arrival_time
        time_executed = current_process.execute(time_slice)
        current_time += time_executed
        if current_process.remaining_time == 0:
            waiting_time += current_time - current_process.arrival_time - current_process.burst_time
            turnaround_time += current_time - current_process.arrival_time
        else:
            remaining_processes.append(current_process)
            
    avg_waiting_time = waiting_time / n
    avg_turnaround_time = turnaround_time / n
    return avg_waiting_time, avg_turnaround_time

静态优先级调度算法的代码如下：

class Process:
    def __init__(self, pid, arrival_time, burst_time, priority):
        self.pid = pid
        self.arrival_time = arrival_time
        self.burst_time = burst_time
        self.priority = priority

    def execute(self):
        self.burst_time -= 1


def static_priority_scheduling(processes):
    n = len(processes)
    remaining_processes = processes.copy()
    current_time = 0
    waiting_time = 0
    turnaround_time = 0
    
    while remaining_processes:
        remaining_processes.sort(key=lambda x: (x.priority, x.arrival_time))
        current_process = remaining_processes.pop(0)
        if current_process.arrival_time > current_time:
            current_time = current_process.arrival_time
        current_process.execute()
        current_time += 1
        if current_process.burst_time == 0:
            waiting_time += current_time - current_process.arrival_time - current_process.priority
            turnaround_time += current_time - current_process.arrival_time
        else:
            remaining_processes.append(current_process)
            
    avg_waiting_time = waiting_time / n
    avg_turnaround_time = turnaround_time / n
    return avg_waiting_time, avg_turnaround_time