编程的必备——头文件

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编程的必备——头文件

在编写程序时,头文件是不可或缺的一部分。它包含了程序中所需的各种函数和变量的声明、宏定义以及其他的重要信息。头文件的作用类似于一本程序的索引,它告诉编译器应该如何处理程序中使用的函数和变量。

在C和C++语言中,使用#include指令将头文件包含到源代码中。头文件通常具有.h或.hpp的扩展名,以便区分源代码文件。

让我们来看一个简单的例子,演示如何使用头文件。假设我们正在编写一个计算圆的面积和周长的程序。

首先,我们需要定义一个头文件circle.h,其中包含用于计算圆面积和周长的函数声明。

// circle.h

#ifndef CIRCLE_H
#define CIRCLE_H

const double PI = 3.14159;

double calculateArea(double radius);
double calculatePerimeter(double radius);

#endif

在这个头文件中,我们使用了#ifndef、#define以及#endif来确保头文件只被包含一次。这是为了防止多次包含同一个头文件而导致的重复定义错误。

接下来,我们需要编写一个源代码文件main.cpp,其中包含了实际的圆的面积和周长的计算逻辑。

// main.cpp

#include <iostream>
#include "circle.h" // 包含自定义的头文件

using namespace std;

int main
Turbo C环境下C语言图形编程必备的GRAPHICS.H头文件及配套BGI驱动资源包 这个资源包包含经典的C语言图形编程核心头文件GRAPHICS.H,专为Turbo C等DOS时代编译器设计,支持initgraph、line、circle、outtextxy、setcolor等基础绘图函数。配套提供main.c示例程序和必要配置结构,实际使用需绑定BGI图形驱动文件(如EGAVGA.BGI),并正确设置驱动路径与图形模式。适用于教学演示、老代码维护或嵌入式DOS环境下的简单可视化开发。注意:不兼容现代Windows图形API(如GDI、DirectX)、Linux X11或主流编译器(GC 阅读详情

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深度学习工具包 DeepLearningToolbox

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; 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