The code is fairly straightforward, except perhaps for the call to convertColumnIndexToModel. That call is necessary because if the user moves the columns around, the view s index for the column doesn t match the model s index for the column. For example, the user might drag the Vegetarian column (which the model considers to be at index 4) so it s displayed as the first column — at view index 0. Since prepareRenderer gives us the view index, we need to translate the view index to a model index so we can be sure we re dealing with the intended column
标签： convertColumnIndexToModel straightforward perhaps fairly
上传时间： 2013-12-10
上传用户：it男一枚
The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods
标签： publications concerning purpose chapter
上传时间： 2015-09-19
上传用户：叶山豪
the calculator s usage! after you have inputed 2 operators,choose + - * / function! But the only situation I did t deal with is that when you choos + fuction ,and the operaters signs is like this -A+B,just turn it to B-A!
标签： calculator the operators function
上传时间： 2016-02-12
上传用户：lili123
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa. For example, >> project.name = MyProject >> project.id = 1234 >> project.param.a = 3.1415 >> project.param.b = 42 becomes with str=xml_format(project, off ) "<project> <name>MyProject</name> <id>1234</id> <param> <a>3.1415</a> <b>42</b> </param> </project>" On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
标签： converts Toolbox complex logical
上传时间： 2016-02-12
上传用户：a673761058
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
Implement the following integer methods: a) Method celsius returns the Celsius equivalent of a Fahrenheit calculation celsius = 5.0 / 9.0 * ( fahrenheit - 32 ) b) Method fahrenheit returns the Fahrenheit equivalent of a Celsius the calculation fahrenheit = 9.0 / 5.0 * celsius + 32 c) Use the methods from parts (a) and (b) to write an application either to enter a Fahrenheit temperature and display the Celsius or to enter a Celsius temperature and display the Fahrenheit equivalent.
标签： equivalent Implement the following
上传时间： 2014-01-19
上传用户：jackgao
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
标签： Convolutional Networks Neural Guide to
上传时间： 2020-06-10
上传用户：shancjb
特点： 精确度0.1%满刻度 可作各式數學演算式功能如:A+B/A-B/AxB/A/B/A&B(Hi or Lo)/|A|/ 16 BIT类比输出功能 输入与输出绝缘耐压2仟伏特/1分钟(input/output/power) 宽范围交直流兩用電源設計 尺寸小,穩定性高
上传时间： 2014-12-23
上传用户：ydd3625
特点(FEATURES) 精确度0.1%满刻度 (Accuracy 0.1%F.S.) 可作各式数学演算式功能如:A+B/A-B/AxB/A/B/A&B(Hi or Lo)/|A| (Math functioA+B/A-B/AxB/A/B/A&B(Hi&Lo)/|A|/etc.....) 16 BIT 类比输出功能(16 bit DAC isolating analog output function) 输入/输出1/输出2绝缘耐压2仟伏特/1分钟(Dielectric strength 2KVac/1min. (input/output1/output2/power)) 宽范围交直流两用电源设计(Wide input range for auxiliary power) 尺寸小,稳定性高(Dimension small and High stability)
上传时间： 2013-11-24
上传用户：541657925
/*--------- 8051内核特殊功能寄存器 -------------*/ sfr ACC = 0xE0; //累加器 sfr B = 0xF0; //B 寄存器 sfr PSW = 0xD0; //程序状态字寄存器 sbit CY = PSW^7; //进位标志位 sbit AC = PSW^6; //辅助进位标志位 sbit F0 = PSW^5; //用户标志位0 sbit RS1 = PSW^4; //工作寄存器组选择控制位 sbit RS0 = PSW^3; //工作寄存器组选择控制位 sbit OV = PSW^2; //溢出标志位 sbit F1 = PSW^1; //用户标志位1 sbit P = PSW^0; //奇偶标志位 sfr SP = 0x81; //堆栈指针寄存器 sfr DPL = 0x82; //数据指针0低字节 sfr DPH = 0x83; //数据指针0高字节 /*------------ 系统管理特殊功能寄存器 -------------*/ sfr PCON = 0x87; //电源控制寄存器 sfr AUXR = 0x8E; //辅助寄存器 sfr AUXR1 = 0xA2; //辅助寄存器1 sfr WAKE_CLKO = 0x8F; //时钟输出和唤醒控制寄存器 sfr CLK_DIV = 0x97; //时钟分频控制寄存器 sfr BUS_SPEED = 0xA1; //总线速度控制寄存器 /*----------- 中断控制特殊功能寄存器 --------------*/ sfr IE = 0xA8; //中断允许寄存器 sbit EA = IE^7; //总中断允许位 sbit ELVD = IE^6; //低电压检测中断控制位 8051
上传时间： 2013-10-30
上传用户：yxgi5
