% --Specify input parameters clear all; distance = input('distance in L_D = '); beta2 = input('sgn(beta2) = '); %beta2 = -1; % default value chirp0 = input('C = '); %chirp0 = 0; % input pulse chirp(default value) N = input('N = '); % N = 1; % soliton order mshape = input('m = '); T_0 = 1; % in ps beta_2 = -20; % in ps^2/km L_D = T_0^2/abs(beta_2); % in km alpha = input('alpha = '); %alpha = 0.2; % in dB/km分析一下这段MATLAB代码
时间: 2023-08-04 20:05:53 浏览: 93
这段 MATLAB 代码主要是用来输入一些参数值,然后计算一些其他的参数值。具体来说:
- 第1行清除所有变量。
- 第2行输入距离值(以 L_D 为单位)。
- 第3行输入 beta2 符号的值。
- 第4行输入初始脉冲的色散值 chirp0。
- 第5行输入孤子阶数 N。
- 第6行输入脉冲形状参数 mshape。
- 第7行设置脉冲宽度 T_0 的值为 1 ps。
- 第8行设置色散参数 beta_2 的值为 -20 ps^2/km。
- 第9行计算 L_D 的值。
- 第10行输入衰减参数 alpha。
- 第11行以注释的形式提供了一些默认值。
这段代码的目的是为了提供输入参数和计算输出参数的框架,以便在后续的代码中使用。
相关问题
-- ================================================ -- Template generated from Template Explorer using: -- Create Procedure (New Menu).SQL -- -- Use the Specify Values for Template Parameters -- command (Ctrl-Shift-M) to fill in the parameter -- values below. -- -- This block of comments will not be included in -- the definition of the procedure. -- ================================================ SET ANSI_NULLS ON GO SET QUOTED_IDENTIFIER ON GO -- ============================================= -- Author: <Author,,Name> -- Create date: <Create Date,,> -- Description: <Description,,> -- ============================================= CREATE PROCEDURE <Procedure_Name, sysname, ProcedureName> -- Add the parameters for the stored procedure here <@Param1, sysname, @p1> <Datatype_For_Param1, , int> = <Default_Value_For_Param1, , 0>, <@Param2, sysname, @p2> <Datatype_For_Param2, , int> = <Default_Value_For_Param2, , 0> AS BEGIN -- SET NOCOUNT ON added to prevent extra result sets from -- interfering with SELECT statements. SET NOCOUNT ON; -- Insert statements for procedure here SELECT <@Param1, sysname, @p1>, <@Param2, sysname, @p2> END GO 详解
这段代码是一个 SQL 存储过程的模板,它用于创建一个存储过程。存储过程是一组预定义的 SQL 语句,可以在数据库中进行重复使用。
在这个模板中,你需要提供以下信息来创建存储过程:
- 作者(Author)
- 创建日期(Create date)
- 描述(Description)
- 存储过程的名称(Procedure_Name)
- 存储过程的参数(可以有多个参数)
在存储过程的定义部分,你可以定义存储过程的参数和默认值。例如,@Param1 和 @Param2 是存储过程的参数,它们分别具有数据类型 Datatype_For_Param1 和 Datatype_For_Param2,并且可以有默认值 Default_Value_For_Param1 和 Default_Value_For_Param2。
在存储过程的主体部分,你可以编写 SQL 语句来执行特定的操作。在这个模板中,它只是简单地选取了参数 @Param1 和 @Param2 的值并返回。
最后,使用 GO 关键字来结束存储过程的创建。
请注意,这只是一个模板,你需要根据实际需求来修改和添加相应的代码。
clear all; % TODO: Edit this to point to the folder your caffe mex file is in. % path_to_matcaffe = '/data/jkrause/cs231b/caffe-rc2/matlab/caffe'; path_to_matcaffe = 'C:/Users/DELL/Downloads/caffe-master/windows'; addpath(path_to_matcaffe) % Load up the image im = imread('peppers.png'); % Get some random image regions (format of each row is [x1 y1 x2 y2]) % Note: If you want to change the number of regions you extract features from, % then you need to change the first input_dim in cnn_deploy.prototxt. regions = [ 1 1 100 100; 100 50 400 250; 1 1 512 284; 200 200 230 220 100 100 300 200]; % Convert image from RGB to BGR and single, which caffe requires. im = single(im(:,:,[3 2 1])); % Get the image mean and crop it to the center mean_data = load('ilsvrc_2012_mean.mat'); image_mean = mean_data.image_mean; cnn_input_size = 227; % Input size to the cnn we trained. off = floor((size(image_mean,1) - cnn_input_size)/2)+1; image_mean = image_mean(off:off+cnn_input_size-1, off:off+cnn_input_size-1, :); % Extract each region ims = zeros(cnn_input_size, cnn_input_size, 3, size(regions, 1), 'single'); for i = 1:size(regions, 1) r = regions(i,:); reg = im(r(2):r(4), r(1):r(3), :); % Resize to input CNN size and subtract mean reg = imresize(reg, [cnn_input_size, cnn_input_size], 'bilinear', 'antialiasing', false); reg = reg - image_mean; % Swap dims 1 and 2 to work with caffe ims(:,:,:,i) = permute(reg, [2 1 3]); end % Initialize caffe with our network. % -cnn_deploy.prototxt gives the structure of the network we're using for % extracting features and is how we specify we want fc6 features. % -cnn512.caffemodel is the binary network containing all the learned weights. % -'test' indicates that we're only going to be extracting features and not % training anything init_key = caffe('init', 'cnn_deploy.prototxt', 'cnn512.caffemodel', 'test'); caffe('set_device', 0); % Specify which gpu we want to use. In this case, let's use the first gpu. caffe('set_mode_gpu'); %caffe('set_mode_cpu'); % Use if you want to use a cpu for whatever reason % Run the CNN f = caffe('forward', {ims}); % Convert the features to (num. dims) x (num. regions) feat = single(reshape(f{1}(:), [], size(ims, 4)));
这段代码的作用是使用已经训练好的CNN模型提取图片中指定区域的特征。首先,加载一张图片,然后选取几个随机的区域,并将每个区域缩放到CNN要求的输入尺寸。然后,使用已经训练好的CNN模型对这些区域进行特征提取,并将提取到的特征向量进行reshape操作,以便后续的处理。这段代码使用了Caffe库来实现CNN模型的初始化和特征提取。
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