libcsv库:轻量级CSV数据读写解决方案

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资源摘要信息:"libcsv是一个用纯ANSI C89编写的小型、简单、快速的CSV库,专门用于处理CSV数据的读写。CSV(Comma-separated values)文件,即逗号分隔的数据文件,常用于数据集成和交换,虽然没有正式标准,但RFC 4180描述了CSV文件的推荐结构。libcsv库提供了一种简单的接口,通过回调函数处理解析的字段和行,且能处理格式不正确的CSV文件。 CSV格式是一种非常常见和广泛使用的数据交换格式,它使用逗号来分隔字段,行尾通常以回车换行符(CR/LF)结束。在MS-DOS风格的CSV文件中,每行结束都会添加这些字符,但最后一行除外,可以没有结束符。libcsv支持这种行结束符的配置,使得它能够适应不同平台和应用程序的需求。 关于标签,libcsv库与csv、env(环境)、rtthread(一个实时操作系统)、pkgs(可能指软件包或库)、C(编程语言)相关。这表明libcsv库可能被设计为适用于C语言环境,特别是在rtthread操作系统上,作为其软件包系统中的一个组件。 压缩包子文件的文件名称列表显示了一个名为libcsv-master的文件夹。这可能意味着libcsv库的源代码存放在这个压缩文件中,用户可以通过解压这个文件来访问源代码,进而进行编译和集成到自己的项目中。" 知识点一:CSV文件和其结构 CSV文件是一种广泛使用的数据格式,其文件内容由逗号分隔的值组成,每一行通常表示一条记录。每行可能以回车换行符(CR/LF)结束。虽然没有统一的CSV标准,但是RFC 4180提供了一种广泛认可的结构,它规定了文件中记录的顺序、字段的分隔符、引用规则等。 知识点二:libcsv库的功能 libcsv库作为一个用纯ANSI C89编写的库,它的主要功能是实现对CSV数据的读写操作。它提供了一个简单的接口,通过回调函数处理每个解析出的字段和行。这样的设计允许开发者在解析CSV数据时插入自定义的处理逻辑。 知识点三:处理不规范CSV文件 CSV文件有时会因为格式不规范而难以解析,例如缺少分隔符、多余的逗号等。libcsv库提供了处理这类不规范CSV文件的能力,这使得它更加健壮和实用。 知识点四:MS-DOS风格的行结束符 在MS-DOS风格的CSV文件中,每一行都以回车换行符(CR/LF)结束,以适应DOS操作系统的行结束符标准。但在文件的最后一行,这个结束符是可选的。libcsv库支持这种格式,使得它能够正确地处理来自不同操作系统的CSV文件。 知识点五:libcsv的使用环境和兼容性 根据标签信息,libcsv库与C语言和rtthread操作系统兼容。rtthread是一个开源的实时操作系统,而C语言是广泛用于系统编程的语言,这表明libcsv库可能被设计为一种轻量级且易于集成的解决方案,适合嵌入式系统和实时应用。 知识点六:libcsv的集成和安装 用户可以通过下载libcsv-master压缩包文件,解压并集成到项目中。集成过程中,用户需要编译库代码,并可能需要根据自身项目的特定需求对代码进行配置或修改。 总结来说,libcsv作为一个轻量级、高效且功能强大的CSV处理库,支持标准和非标准格式的CSV文件处理,特别适合需要高效数据处理和集成的C语言项目和实时操作系统环境。

Write java code: Copy the files, small_weapons.txt, and large_weapons.txt from the assignment folder on Blackboard and save them to your folder. For testing purposes, you should use the small file. Use the large file when you think the application works correctly. To see what is in the files use a text editor. Nilesh is currently enjoying the action RPG game Torchlight 2 which is an awesome game and totally blows Auction House Simulator 3, oh sorry, that should be Diablo 3, out of the water. He has got a file containing info on some of the unique weapons in the game. The transaction file contains the following information: Weapon Name (string) Weapon Type (string) Damage (int) Weapon Speed (double) … To tell if one weapon is better than another you need to know the Damage Per Second (DPS) the weapon does, since weapons have a different attack speed. DPS is calculated by taking the damage value and dividing it by the attack speed.a) You will write a program that will allow Nilesh to load the file and display the weapon info and DPS of every weapon in the input file. When the user chooses to open a weapon file, they are required to type in the filename. It will then read the 4 values about a particular weapon and then display the 4 values to the console window on one line neatly padded along with the DPS of the weapon. This is repeated until the end of the file. b) Modify your code from a) so that the weapon information written to the console window is also written to a text file. The user should be able to specify the name of the file. Add a header to the beginning of the file which has column headers for each column. At the end of the text file display the total number of weapons in the file. c) Document your code appropriately and add your name and id number as comments at the top of your code. Please also submit this text file you have created. Enter the file you want to search end with .txt: large_weapons.txt 1 Blackfang Bludgeon Great Hammer 489 1.44 339.58333333333337 2 Bones 2 Boneshredder Great Axe 256 0.84 304.76190476190476 3 Comet's Tail Great Sword 872 1.2 726.6666666666667 4 Decapitator Great Sword 188 1.08 174.07407407407408 5 Demolisher Great Hammer 887 1.32 671.9696969696969

2023-05-27 上传

# -*- coding: utf-8 -*- """ Transform the data type from ascii to ubyte format (8 bits unsigned binary) and save to new files, which would reduce the data size to 1/3, and would save the data transforming time when read by the python @author: Marmot """ import numpy as np import time from itertools import islice import pandas as pd # data_folder = '../../data/' set_list = ['train','testA','testB'] size_list = [10000,2000,2000] time1= time.time() for set_name,set_size in zip(set_list,size_list): output_file = data_folder + set_name + '_ubyte.txt' f = open(output_file, "w") f.close() Img_ind = 0 input_file = data_folder + set_name +'.txt' with open(input_file) as f: for content in f: Img_ind = Img_ind +1 print('transforming ' + set_name + ': ' + str(Img_ind).zfill(5)) line = content.split(',') title = line[0] + ' '+line[1] data_write = np.asarray(line[2].strip().split(' ')).astype(np.ubyte) data_write = (data_write + 1).astype(np.ubyte) if data_write.max()>255: print('too large') if data_write.min()<0: print('too small') f = open(output_file, "a") f.write(data_write.tobytes()) f.close() time2 = time.time() print('total elapse time:'+ str(time2- time1)) #%% generate train label list value_list =[] set_name = 'train' input_file = data_folder + set_name +'.txt' with open(input_file) as f: for content in f: line = content.split(',') value_list.append(float(line[1])) value_list = pd.DataFrame(value_list, columns=['value']) value_list.to_csv(data_folder + 'train_label.csv',index = False,header = False)

2023-06-10 上传

帮我解释一下这段话:The connection growth algorithm greedily activates useful, but currently ‘dormant,’ connections. We incorporate it in the following learning policy: Policy 1: Add a connection w iff it can quickly reduce the value of loss function L. The DNN seed contains only a small fraction of active connections to propagate gradients. To locate the ‘dormant’ connections that can reduce L effectively, we evaluate ∂L/∂w for all the ‘dormant’ connections w (computed either using the whole training set or a large batch). Policy 1 activates ‘dormant’ connections iff they are the most efficient at reducing L. This can also assist with avoiding local minima and achieving higher accuracy [28]. To illustrate this policy, we plot the connections grown from the input to the first layer of LeNet-300-100 [7] (for the MNIST dataset) in Fig. 3. The image center has a much higher grown density than the margins, consistent with the fact that the MNIST digits are centered. From a neuroscience perspective, our connection growth algorithm coincides with the Hebbian theory: “Neurons that fire together wire together [29]." We define the stimulation magnitude of the mth presynaptic neuron in the (l + 1)th layer and the n th postsynaptic neuron in the l th layer as ∂L/∂ul+1 m and x l n , respectively. The connections activated based on Hebbian theory would have a strong correlation between presynaptic and postsynaptic cells, thus a large value of (∂L/∂ul+1 m )x l n . This is also the magnitude of the gradient of L with respect to w (w is the weight that connects u l+1 m and x l n ): |∂L/∂w| = (∂L/∂ul+1 m )x l n (1) Thus, this is mathematically equivalent to Policy 1.

2023-05-17 上传