www.pudn.com > bp_cpp.zip > demo.cpp

// 
//	demo.cpp 
//	2004-05-06 by ShanLanshan 
// 
//	此程序用于测试神经元的库函数 
//	它展示了怎样用这个库所生成的多层BP网络来求解宇称问题 
//	在Visual Stduio 2003上编译通过，要注意启动运行时信息的支持 
 
#include  
#include  
#include  
#include "public/ann/neuron.h" 
#include "public/ann/bp/bpneuron.h" 
#include "public/prob.h" 
#include "public/ann/annlib.h" 
#include "public/ann/vec_container.h" 
#include "public/ann/vector.h" 
#include "public/ann/annfunc/annfunc.h" 
#include "Windows.h" 
#include  
 
using namespace ann; 
 
int main() 
{ 
	char s[1024]; 
	const char *file = "C:\\bpnet.txt"; 
 
	for (int i=0; i< 0; ++i) { 
		std::cout << prob_get_range(0, 3) << " "; 
	} 
	//std::cin >> s; 
 
	AnnLib annlib; 
	if (!annlib.init()) { 
		std::cout << "ann lib init failed\n"; 
		return -1; 
	} 
 
	AnnFuncGroup *fg; 
	fg = annfunc_get("sign"); 
	assert(fg != NULL); 
 
	AnnSampleSet ass("宇称5"); 
 
	{ 
		std::ofstream os; 
		os.open("C:\\YC5.txt"); 
		if (os.is_open()) { 
			os << ass; 
		} 
	} 
	return 0; 
 
	BPNet net; 
	AnnVector vec_input, vec_output, vec_tutor; 
 
	bool openfile = true; 
	std::ifstream is; 
	if (openfile) 
		is.open(file); 
	if (openfile && is.is_open()) { 
		std::cout << "open file ok\n"; 
		net << is; 
	} else { 
		//assert(net.init("2 * 16 * 8 * 2 * 1")); 
		char initstr[64]; 
		sprintf(initstr, "%d * %s * %d", 
				ass.m_input_division, 
				"100 * 16", 
				ass.m_output_division); 
		std::cout << "Init: " << initstr << std::endl; 
		net.init(initstr); 
		net.mutual_conn(); 
		prob_set_seed(0); 
		net.random(); 
	} 
 
 
	//net.set_output_func(annfunc_get("sech6"), 0); 
	net.set_output_func(annfunc_get("sech6"), 1); 
	net.set_output_func(annfunc_get("sin"), 2); 
	//net.set_output_func(annfunc_get("sech6"), 3); 
	net.set_output_func(annfunc_get("sech6_cache")); 
	//net.set_output_func(annfunc_get("sech6")); 
	//net.set_output_func(annfunc_get("sin")); 
 
	vec_output.set_size(net.get_output_division()); 
 
	int right = 0, k = 0; 
	time_t t1,t2; 
 
	t1 = time(NULL); 
 
	for (int m = 0; m < 1; ++m) { 
		//net.random(); 
		right = 0; 
		std::cout << m << std::endl; 
		for (int i=400000; i>0; --i) { 
			printf("."); 
			ass.get_sample(&vec_input, &vec_tutor); 
			net.set_input(&vec_input); 
			net.convert(); 
			net.get_output(&vec_output); 
			net.learn(&vec_tutor); 
			fg->multi_main(vec_output.get_buffer(), vec_output.get_size(), NULL); 
			vec_output.release_buffer(); 
 
			if (0 && (i % 100) == 0) { 
				std::cout << i << std::endl; 
				Sleep(10); 
			} 
			//if (i < 200) 
			if (0) 
			std::cout << "\n\nSample" << i << " " << vec_input << std::endl 
				<< "tutor : " << vec_tutor << std::endl 
				<< "output: " << vec_output << std::endl 
				<< std::endl; 
			k ++; 
			if (vec_tutor == vec_output) { 
				++right; 
				if (right >= 600) break; 
			} else { 
				right = 0; 
			} 
		} 
	} 
 
	t2 = time(NULL); 
	std::cout << static_cast(t2 - t1) << "seconds passed\n"; 
	std::cout << "Get correct in step " << k << ", ok\n"; 
 
	std::ofstream fs; 
	fs.open(file); 
	fs << net; 
 
	std::cin >> s; 
	return 0; 
}