Search - data set .mat

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[Other resource] dCHAP9 DL : 0: 1. 在IBConsole中添加两个用户LOGIN和MATER，密码均为PASSWORD。 2. 用MISDBA用户登录MISDB数据库。 3. 在ISQL中，输入第9章提供的SQL语句；或者根据表9-1至表9-8在SQL Explorer中自行创建数据表。数据库创建后需要分配LOGIN和MATER用户的访问权限。 4. 根据表9-9和表9-10设置初始数据，另外需要在PERSON数据表中设置一个用于登录系统的用户（ID=’MAT’，PASSWD=’PASSWORD’，AUTHORITY=’7’，STATE=’F’），同时在PART表中添加ID为’0000000000’的零件，名称为“。 5. 除了修改数据库连接的属性，还需要修改数据模块中LOGIN方法的相关用户密码。 -1. In addition two IBConsole users LOGIN and Mater. passwords are PASSWORD. 2. User login with MISDBA MISDB database. 3. In ISQL, the importation of Chapter 9 of SQL; Or, according to Table 9 -1 to Table 9-8 in SQL Explorer to create data sheets. After creating the database needs and Mater distribution LOGIN user access permissions. 4. According to Table 9-9 and Table 9-10 initial data set, Another need PERSON data tables set up a registration system for users (ID = 'MAT' PASSWD = 'PASSWORD' AUTHORITY ='7 ', STATE = 'F'), PART table at the same time adding ID to'0000000000 'parts, name. " 5. In addition to modify the database connection attribute, is also a need to modify data module LOGIN method of user passwords.
Update : 2008-10-13 Size : 70.06kb Publisher : bansom
[Other Databases] dCHAP9 DL : 0: 1. 在IBConsole中添加两个用户LOGIN和MATER，密码均为PASSWORD。 2. 用MISDBA用户登录MISDB数据库。 3. 在ISQL中，输入第9章提供的SQL语句；或者根据表9-1至表9-8在SQL Explorer中自行创建数据表。数据库创建后需要分配LOGIN和MATER用户的访问权限。 4. 根据表9-9和表9-10设置初始数据，另外需要在PERSON数据表中设置一个用于登录系统的用户（ID=’MAT’，PASSWD=’PASSWORD’，AUTHORITY=’7’，STATE=’F’），同时在PART表中添加ID为’0000000000’的零件，名称为“。 5. 除了修改数据库连接的属性，还需要修改数据模块中LOGIN方法的相关用户密码。 -1. In addition two IBConsole users LOGIN and Mater. passwords are PASSWORD. 2. User login with MISDBA MISDB database. 3. In ISQL, the importation of Chapter 9 of SQL; Or, according to Table 9-1 to Table 9-8 in SQL Explorer to create data sheets. After creating the database needs and Mater distribution LOGIN user access permissions. 4. According to Table 9-9 and Table 9-10 initial data set, Another need PERSON data tables set up a registration system for users (ID = 'MAT' PASSWD = 'PASSWORD' AUTHORITY ='7 ', STATE = 'F'), PART table at the same time adding ID to'0000000000 'parts, name. " 5. In addition to modify the database connection attribute, is also a need to modify data module LOGIN method of user passwords.
Update : 2025-02-17 Size : 70kb Publisher : bansom
[matlab] buck DL : 1: THE COMPUTER EXPLORATIONS TOOLBOX --------------------------------- This directory contains the Computer Explorations Toolbox, which is the set of M-files and data files which accompany the textbook "Computer Explorations in Signals and Systems using MATLAB" by John Buck, Michael Daniel, and Andrew Singer, Prentice Hall.
Update : 2025-02-17 Size : 554kb Publisher : mirtchev
[matlab] Geodetic_Transformations DL : 0: Geodetic Transformations Set of tools to perform transformations between projection, ellipsoidal and cartesian coordinates in either direction. Included are proj2ell - projection to ellipsoidal ell2cart - ellipsoidal to global cartesian d3trafo - datum transformation between global cartesian coordinate systems cart2ell - global cartesian to ellipsoidal ell2proj - ellipsoidal to projection helmert3d - calculate the d3trafo parameters from coordinate sets in two cartesian systems Some projections and ellipsoids are already defined in mat-Files. This functions are often used in geodesy when transforming between different coordinate systems, e.g. UTM to GK or GPS-data (WGS84) to UTM.
Update : 2025-02-17 Size : 82kb Publisher : Markus
[matlab] breast_cancer DL : 0: 对数据集breast_cancer，用LR，NB算法对其进行操作-On the data set breast_cancer, with LR, NB algorithms manipulate
Update : 2025-02-17 Size : 4kb Publisher : 李海
[AI-NN-PR] LDPC DL : 0: 这是关于LDPC信道编码模块设计的程序打开源程序，先运行gengrate_h.m程序，陆续将码长设置为756bit，列重设置为3，行重设置为9。在Workspace中同时将H、A、B、C、D、E、Hget、Fget、g、Tget这是个变量选择另存为encode_in.mat 格式。再运行main_encode.m进行编码，主程序运行后，在当前目录下，自动生成编码结果文件“encode—out.mat”，这将作为下一次扩频调制仿真实验的的输入信号。最后分别查看Workspace中的变量s（编码前数据）和xyuan（编码后数据）的波形。对比后，可以看出编码前的数据码片长度为504bit，编码后的码片数据长度为756bit。编码效率=编码前码片长度/编码后码片长度=2/3。-This is about the LDPC channel coding module design process Open source, first run gengrate_h.m program, phasing out the code length is set to 756bit, column re-set to 3, line weight is set to 9. In the Workspace in the same time, H, A, B, C, D, E, Hget, Fget, g, Tget This is a variable select Save As encode_in.mat format. Then run main_encode.m encoding, the main program running in the current directory, the results of automatically generated code file "encode-out.mat", which will serve as the next simulation of the spread spectrum modulation input signal. Finally, the variables were View Workspace in s (before encoding data) and xyuan (encoded data) waveform. After comparison, we can see the data before encoding chip length of 504bit, encoded data length of chip 756bit. Coded before coding efficiency = chip length/length of the encoded chip = 2/3.
Update : 2025-02-17 Size : 15kb Publisher : 吴健
[matlab] ELM DL : 0: 训练集/测试集产生 load spectra_data.mat 随机产生训练集和测试集 temp = randperm(size(NIR,1)) 训练集——50个样本 P_train = NIR(temp(1:50),:) T_train = octane(temp(1:50),:) 测试集——10个样本 P_test = NIR(temp(51:end),:) T_test = octane(temp(51:end),:) N = size(P_test,2) 数据归一化 - Training set/test set generation load spectra_data.mat randomly generated training set and test set temp = randperm (size (NIR, 1)) training set- 50 samples P_train = NIR (temp (1:50) ,:)' T_train = octane (temp (1:50 ),:)' test set- 10 samples P_test = NIR (temp (51: end ),:)' T_test = octane (temp (51 : end ),:)' N = size (P_test, 2) Data Normalization
Update : 2025-02-17 Size : 1kb Publisher : 王飞
[matlab] PCA DL : 1: 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduction, and then with the minimum error method to classify the data dimension reduction. Both compressed package matlab source code, but also write their own reports, as well. MAT format, the program calls for IRIS data set. Procedures detailed notes, it is easy to understand. Finally, the output to a txt file.
Update : 2025-02-17 Size : 92kb Publisher : yumingwei
[AI-NN-PR] mat DL : 0: 对iris数据集分类采用bp网络利用交叉验证优化参数-Classification of the iris data set bp network use of cross-validation optimization parameters
Update : 2025-02-17 Size : 2kb Publisher : 李强
[Industry research] fds60 DL : 0: ace Detection Program for MATLAB 2013a using Gabor Feature Extraction and Neural Networks ---------------------------------------------------------------- 1- copy all files and directories to the MATLAB s work folder * In order to run the program you must have Image Processing and Neural Networks Toolboxes 2- (Important) Navigate to the root folder which contains "main.m". 3- Type "main" or "run main" in the command window 4. Only fort the first time, the program creates Gabor filters and stores them in ./data/gabor.mat Training set dataset and stores it in ./data/imgdb.mat Neural Network and stores it in ./data/net.mat 5- imgdb is short for "image data base". 6- When the program menu appears click on "Train Network" and wait until the program is done with the training-ace Detection Program for MATLAB 2013a using Gabor Feature Extraction and Neural Networks ---------------------------------------------------------------- 1- copy all files and directories to the MATLAB s work folder * In order to run the program you must have Image Processing and Neural Networks Toolboxes 2- (Important) Navigate to the root folder which contains "main.m". 3- Type "main" or "run main" in the command window 4. Only fort the first time, the program creates Gabor filters and stores them in ./data/gabor.mat Training set dataset and stores it in ./data/imgdb.mat Neural Network and stores it in ./data/net.mat 5- imgdb is short for "image data base". 6- When the program menu appears click on "Train Network" and wait until the program is done with the training
Update : 2025-02-17 Size : 176kb Publisher : manu
[Other] 06671935 DL : 0: 本文中研究了C波段极化合成孔径雷达（SAR）数据表面光滑的特性。-In this paper, we study surface slick characterization in polarimetric C-band synthetic aperture radar (SAR) data. The objective is to identify the most powerful multipolarization SAR descriptors for mineral oil spill versus biogenic slick discrimina- tion. A systematic comparison of eight well-known multipolariza- tion features is provided. The analysis is performed on data that we collected during a large-scale oil spill exercise at the Frigg ﬁ eld situated northwest of Stavanger, in June 2011. Controlled oil spills and simulated look-alikes were simultaneously captured within ﬁ ne quad-polarization Radarsat-2 acquisitions during this exper- iment. Multipolarization features derived only the copolar- ized complex scattering coefﬁ cients are explored. We ﬁ nd that the two most powerful multipolarization features extracted from this data set are the geometric intensity, measuring the combined intensity based on the determinant of the coherency mat
Update : 2025-02-17 Size : 3.56mb Publisher : 火焰山的兔八哥
[Other] 51355207iris DL : 0: code for rise in mat lab and how use data set
Update : 2025-02-17 Size : 1kb Publisher : hassan121
[matlab] DM6 DL : 0: 主要包括一个测试数据集合mydata.mat,main.m，DBSCAN.m和PlotClusterinResult.m共4个文件，我们在测试实验实验中做了两个方面更改：1)更换了另外一个测试数据，测试数据来源于[13]（取其中的一部分）,2)添加了个K距离图部分代码(均在如下主程序代码中给出)，代码按照个人对k-distance graph的理解编写，如有错误之处，望大家指正，3）改变参数Eps值大小，输出结果并显示。(Including a test data set mydata.mat, main.m, DBSCAN.m and PlotClusterinResult.m, a total of 4 documents, we do two aspects of change in the experimental test: 1) to replace another test data, test data from [13] (part of the), 2) add a K distance map the code (both in the main program code are given in the following), according to the personal understanding of the k-distance graph code written, if wrong, hope you correct me, 3) value to change the parameters of Eps, output and display.)
Update : 2025-02-17 Size : 1kb Publisher : zouhaozhe
[Graph Drawing] roi DL : 0: graph plot in mat lab for the training data set.
Update : 2025-02-17 Size : 3kb Publisher : christysujin
[Other] swishroll DL : 0: 这是瑞士卷数据集，isomap的原始数据集，mat格式，好用(Swiss roll data set in mat , originally used in ISOMAP)
Update : 2025-02-17 Size : 35kb Publisher : 1113WEWEAD
[Other] umist_cropped DL : 0: 人脸数据库umist数据，处理成.mat格式(Face database umist original file data set and . mat format)
Update : 2025-02-17 Size : 3.25mb Publisher : 叶乐
[Documents] frey_rawface DL : 0: 人脸数据集。mat文件。2000张人脸数据，560*1965的二维数据(Face data set. Mat file. 2000 face data, two dimensional data of 560*1965)
Update : 2025-02-17 Size : 972kb Publisher : xuwei679890
[Documents] data set DL : 0: 垃圾邮件集，包含正常邮件和垃圾邮件数据以及mat文件(Spam collection, including normal mail and spam data, and mat files.)
Update : 2025-02-17 Size : 15.44mb Publisher : 不资道叫什么
[Other] QT DL : 0: 本程序可读取Yokogawa 光谱仪多个数据文件(CSV格式)并寻峰。数据文件为多个光栅串的测试光谱。串联光栅波长间隔等距，可确定等距串联光栅的中心波长和峰值强度，将数据存入mat file 使用前请设置若干重要参数【wl_start】【wl_end】【FBGs_num】【Min_PowerValue】【Peaks_seperation】其他相关变量说明【OSA_data】所读入的光谱全图里面可能包含更宽的波长【OSA_data_FBGs】截取光栅串所在波长区间，由【wl_start】【wl_end】决定导出的数据文件为 FBGs.mat，其中所保存的重要变量为【FBGs_peaks】【FBGs_peaks】第1列：峰值所在序号；第2列：横轴-光栅串中心波长值；第3列：纵轴-光栅串光强值(This program can read multiple data files (CSV format) of Yokogawa spectrometer and find peaks. The data file is a test spectrum for multiple grating strings. The length of the series grating wavelength interval can determine the central wavelength and peak intensity of the isometric series grating. Before using the data into mat file, please set up some important parameters [wl_start] [wl_end] [FBGs_num] [Min_PowerValue] (Peaks_seperation] other related variables said the spectrum read by [OSA_data] The full graph may contain a wider wavelength [OSA_data_FBGs] to intercept the wavelength range of the grating string, and the data file derived by [wl_start] [wl_end] is FBGs.mat, in which the important variable is the [FBGs_peaks] [FBGs_peaks] first column: the peak in the sequence number; the second column: the transverse axis grating string. Center wavelength value; third column: longitudinal axis grating intensity value)
Update : 2025-02-17 Size : 4.08mb Publisher : 斑驳的夜
[matlab] data_batch_2 DL : 1: cifar-10数据集由10个类的60000个32x32彩色图像组成，每个类有6000个图像。有50000个训练图像和10000个测试图像。数据集分为五个训练批次和一个测试批次，每个批次有10000个图像。测试批次包含来自每个类别的恰好1000个随机选择的图像。训练批次以随机顺序包含剩余图像，但一些训练批次可能包含来自一个类别的图像比另一个更多。总体来说，五个训练集之和包含来自每个类的正好5000张图像。具体：batch2.mat文件，该训练集可以用于图片识别，非负矩阵分解等。(The cifar-10 dataset consists of 60000 32x32 color images of 10 classes, each of which has 6000 images. There are 50000 training images and 10000 test images. The data set is divided into five training batches and one test batch, each batch has 10000 images. The test batch contains exactly 1000 randomly selected images from each category. Training batches contain the remaining images in random order, but some training batches may contain more images from one category than another. Overall, the sum of the five training sets contains exactly 5000 images from each class. Specific: including 5 batch.mat File and a test file. It can be used for image recognition, nonnegative matrix decomposition and so on.)
Update : 2025-02-17 Size : 29.16mb Publisher : 鱼干儿

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