[leetcode] 731. My Calendar II @ python

博客围绕实现MyCalendarTwo类以存储事件展开,新事件添加不能导致三重预订。给出示例说明预订情况,解法是构造overlaps和calender两个数列,分别存重叠和总的预定,每次添加时检查重叠数列与区间是否重叠,区间为半开半闭。

原题

Implement a MyCalendarTwo class to store your events. A new event can be added if adding the event will not cause a triple booking.

Your class will have one method, book(int start, int end). Formally, this represents a booking on the half open interval [start, end), the range of real numbers x such that start <= x < end.

A triple booking happens when three events have some non-empty intersection (ie., there is some time that is common to all 3 events.)

For each call to the method MyCalendar.book, return true if the event can be added to the calendar successfully without causing a triple booking. Otherwise, return false and do not add the event to the calendar.

Your class will be called like this: MyCalendar cal = new MyCalendar(); MyCalendar.book(start, end)
Example 1:

MyCalendar();
MyCalendar.book(10, 20); // returns true
MyCalendar.book(50, 60); // returns true
MyCalendar.book(10, 40); // returns true
MyCalendar.book(5, 15); // returns false
MyCalendar.book(5, 10); // returns true
MyCalendar.book(25, 55); // returns true
Explanation:
The first two events can be booked. The third event can be double booked.
The fourth event (5, 15) can’t be booked, because it would result in a triple booking.
The fifth event (5, 10) can be booked, as it does not use time 10 which is already double booked.
The sixth event (25, 55) can be booked, as the time in [25, 40) will be double booked with the third event;
the time [40, 50) will be single booked, and the time [50, 55) will be double booked with the second event.

Note:

The number of calls to MyCalendar.book per test case will be at most 1000.
In calls to MyCalendar.book(start, end), start and end are integers in the range [0, 10^9].

解法

构造两个数列overlaps和calender, 分别储存重叠的和总的预定, 每次要加booking时, 检查重叠的数列是否与overlaps和calender的区间重叠, 重叠的条件是start < j and end > i, 注意区间是半开半闭区间.

代码

class MyCalendarTwo(object):

    def __init__(self):
        self.overlaps = []
        self.calender = []
        

    def book(self, start, end):
        """
        :type start: int
        :type end: int
        :rtype: bool
        """
        for i, j in self.overlaps:
            if start < j and end > i:
                return False
        for i, j in self.calender:
            if start < j and end > i:
                self.overlaps.append((max(i, start), min(j, end)))
        self.calender.append((start, end))
        return True


# Your MyCalendarTwo object will be instantiated and called as such:
# obj = MyCalendarTwo()
# param_1 = obj.book(start,end)
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值