Middle-题目127:29. Divide Two Integers

本文介绍了一种不使用乘法、除法和取模运算实现两整数相除的方法,并提供了一个Java代码示例。该方法适用于面试场景,讨论了如何通过位运算和加减操作来模拟二进制竖式除法。

题目原文:
Divide two integers without using multiplication, division and mod operator.

If it is overflow, return MAX_INT.
题目大意:
不用乘法、除法、取模实现两个数的除法,如果溢出则返回MAX_INT(232-1)
题目分析:
直接用a/b水过去的,正确的解法好像是模拟二进制的竖式除法,用位运算和加减解决。
源码:(language:java)

public class Solution {
    public int divide(int dividend, int divisor) {
        return (dividend==-2147483648 && divisor == -1)?Integer.MAX_VALUE:dividend/divisor;
    }
}

成绩:
2ms,beats 75.64%,众数3ms,66.70%
Cmershen的碎碎念:
原则上除法是CPU的底层上用硬件实现的,但可以用高级语言代码去模拟。据论坛中所说,实现除法、乘法、乘方等数值运算的问题在面试中还是比较常见的,有时间整理一下。

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
### Quick - Sort Algorithm Implementation The quick - sort algorithm is a divide - and - conquer sorting algorithm. Here is the Python implementation of the quick - sort algorithm: ```python import random import matplotlib.pyplot as plt from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas def partition(arr, low, high): pivot = arr[high] i = low - 1 for j in range(low, high): if arr[j] <= pivot: i = i + 1 arr[i], arr[j] = arr[j], arr[i] arr[i + 1], arr[high] = arr[high], arr[i + 1] return i + 1 def quick_sort(arr, low, high, steps): if low < high: pi = partition(arr, low, high) step_info = { "pivot": arr[pi], "left_subarray": arr[low:pi], "right_subarray": arr[pi + 1:high + 1], "current_array": arr.copy() } steps.append(step_info) quick_sort(arr, low, pi - 1, steps) quick_sort(arr, pi + 1, high, steps) # Generate a list of 9 random integers random_list = random.sample(range(1, 100), 9) sorting_steps = [] quick_sort(random_list, 0, len(random_list) - 1, sorting_steps) # Create a PDF file c = canvas.Canvas("q3.pdf", pagesize=letter) y = 750 c.drawString(100, y, "Quick Sort Process for Random List: " + str(random_list)) y -= 20 for step in sorting_steps: c.drawString(100, y, f"Pivot: {step['pivot']}") y -= 20 c.drawString(100, y, f"Left Subarray: {step['left_subarray']}") y -= 20 c.drawString(100, y, f"Right Subarray: {step['right_subarray']}") y -= 20 c.drawString(100, y, f"Current Array: {step['current_array']}") y -= 40 c.save() ``` ### Explanation 1. **Partition Function**: The `partition` function selects the last element as the pivot and rearranges the array so that all elements less than or equal to the pivot are on the left, and all elements greater than the pivot are on the right. It then returns the index of the pivot in its sorted position. 2. **Quick Sort Function**: The `quick_sort` function is a recursive function. It first partitions the array and then recursively sorts the left and right sub - arrays. It also records the pivot, sub - arrays, and the current state of the array at each step in the `sorting_steps` list. 3. **PDF Generation**: After the quick - sort process is completed, a PDF file named `q3.pdf` is created. The PDF includes the original random list and the details of each step in the quick - sort process, such as the pivot, left and right sub - arrays, and the current state of the array. ###
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
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