Objectives: To practice File and text input output concept 1. (Process scores in a text file) Suppose a text file contains an unspecified number of scores separated by spaces. Write a program that prompts the user to enter the file, reads the scores from the file, and displays their total and average. 2. (Write/read data) Write a program to create a file named Lab07_02.txt if it does not exist. Write 100 integers created randomly into the file using text I/O. Integers are separated by spaces in the file. Read the data back from the file and display the data in increasing order.

时间: 2023-12-03 17:42:55 浏览: 45
1. Here's the code to process scores in a text file: ```python filename = input("Enter the file name: ") try: with open(filename, "r") as file: scores = [int(score) for score in file.read().split()] total = sum(scores) average = total / len(scores) print("Total: ", total) print("Average: ", average) except FileNotFoundError: print("File not found.") ``` 2. Here's the code to write/read data: ```python import random filename = "Lab07_02.txt" try: with open(filename, "x") as file: data = [str(random.randint(1, 100)) for i in range(100)] file.write(" ".join(data)) except FileExistsError: pass try: with open(filename, "r") as file: data = [int(num) for num in file.read().split()] data.sort() print(data) except FileNotFoundError: print("File not found.") ```

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Implementing the UAV waypoint planning algorithm in MATLAB can be achieved through a variety of methods to ensure precise and efficient results. Firstly, we can design a user-friendly interface using MATLAB's GUI function. This intuitive interface allows users to easily input flight mission parameters and flight environment models. Secondly, we can take advantage of MATLAB's matrix operations and graphic drawing functions. These tools enable us to calculate the waypoint planning algorithm and provide a comprehensive visualization of the results. Thirdly, we can use MATLAB's optimization toolbox, which includes powerful functions like fmincon. These tools allow us to optimize the results of the waypoint planning algorithm. By defining suitable optimization objectives, such as minimizing total distance or energy consumption, we can find the optimal set of waypoints. Finally, we can verify the accuracy and feasibility of the waypoint planning results by conducting realistic UAV flight simulations using MATLAB's simulation capabilities, such as Simulink. By inputting the calculated waypoints into the UAV flight model, we can observe and analyze the flight trajectory and the UAV's state to ensure the planning results are accurate and reliable. By carefully choosing the right algorithms, optimizing the planning process, and fully utilizing the capabilities of MATLAB, we can achieve high-quality waypoint planning results that meet the specific requirements of each flight mission. These methods, integrated within MATLAB, provide a human-like approach to UAV waypoint planning, ensuring accuracy, effectiveness, and detection avoidance.还能检测出来,这个基础上再修改

The LULC simulation data we utilized to create future EN maps was produced by X. Liu et al. (2017), which was conducted at the national level. The reason we apply national-level simulated data to a local area is as follows. Firstly, China has a top-down land use planning system (also known as spatial planning) with five levels. The quantitative objectives in national plans are handed down to county-level plans through provincial and prefectural level plans (Zhong et al., 2014). That means land use patterns of nine cities in WUA are required to reflect relevant upper-level plans, for example, to satisfy the land use quota made by Hubei provincial plans and the national plans. Secondly, there are interdependencies across places so what happens in one region produces effects not only on this location but on other regions (Overman et al., 2010). And the increase of construction land in one place will shift protection pressure on natural ecosystems elsewhere for a sustainable goal. The land use simulation at the national level allocated land resources from a top-down perspective and links land use changes in a region to events taking place in other locations through global simulation. However, the Kappa coefficient of the simulated data in WUA is 0.55 and the overall accuracy is 0.71, which is lower than the statistic value at the national-level data. Although the Kappa between 0.4~0.6 is moderate and at an acceptable level (Appiah et al., 2015; Ding et al., 2013; Ku, 2016), the simulated accuracy of the land use data needs to be improved. Future work on exploring the impact of LULC dynamics on EN will develop based on the high-accuracy simulated data and updating the initial simulated time to 2020, by integrating the impacts of socioeconomic factors, climate change, regional planning, land use policy, etc.

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

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