// AnnBP.cpp: implementation of the CAnnBP class.
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//
//////////////////////////////////////////////////////////////////////
#include "StdAfx.h"
#include "AnnBP.h"
#include "math.h"
//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////
CAnnBP::CAnnBP()
{
eta1=0.3;
momentum1=0.3;
}
CAnnBP::~CAnnBP()
{
}
double CAnnBP::drnd()
{
return ((double) rand() / (double) BIGRND);
}
/*** 返回-1.0到1.0之间的双精度随机数 ***/
double CAnnBP::dpn1()
{
return (double) (rand())/(32767/2)-1;
}
/*** 作用函数，目前是S型函数 ***/
double CAnnBP::squash(double x)
{
return (1.0 / (1.0 + exp(-x)));
}
/*** 申请1维双精度实数数组 ***/
double* CAnnBP::alloc_1d_dbl(int n)
{
double *new1;
new1 = (double *) malloc ((unsigned) (n * sizeof (double)));
if (new1 == NULL) {
AfxMessageBox("ALLOC_1D_DBL: Couldn't allocate array of doubles\n");
return (NULL);
}
return (new1);
}
/*** 申请2维双精度实数数组 ***/
double** CAnnBP::alloc_2d_dbl(int m, int n)
{
int i;
double **new1;
new1 = (double **) malloc ((unsigned) (m * sizeof (double *)));
if (new1 == NULL) {
AfxMessageBox("ALLOC_2D_DBL: Couldn't allocate array of dbl ptrs\n");
return (NULL);
}
for (i = 0; i m; i++) {
new1[i] = alloc_1d_dbl(n);
}
return (new1);
}
/*** 随机初始化权值 ***/
void CAnnBP::bpnn_randomize_weights(double **w, int m, int n)
{
int i, j;
for (i = 0; i = m; i++) {
for (j = 0; j = n; j++) {
w[i][j] = dpn1();
}
}
}
/*** 0初始化权值 ***/
void CAnnBP::bpnn_zero_weights(double **w, int m, int n)
{
int i, j;
for (i = 0; i = m; i++) {
for (j = 0; j = n; j++) {
w[i][j] = 0.0;
}
}
}
/*** 设置随机数种子 ***/
void CAnnBP::bpnn_initialize(int seed)
{
CString msg,s;
msg="Random number generator seed:";
s.Format("%d",seed);
AfxMessageBox(msg+s);
srand(seed);
}
/*** 创建BP网络 ***/
BPNN* CAnnBP::bpnn_internal_create(int n_in, int n_hidden, int n_out)
{
BPNN *newnet;
newnet = (BPNN *) malloc (sizeof (BPNN));
if (newnet == NULL) {
printf("BPNN_CREATE: Couldn't allocate neural network\n");
return (NULL);
}
newnet-input_n = n_in;
newnet-hidden_n = n_hidden;
newnet-output_n = n_out;
newnet-input_units = alloc_1d_dbl(n_in + 1);
newnet-hidden_units = alloc_1d_dbl(n_hidden + 1);
newnet-output_units = alloc_1d_dbl(n_out + 1);
newnet-hidden_delta = alloc_1d_dbl(n_hidden + 1);
newnet-output_delta = alloc_1d_dbl(n_out + 1);
newnet-target = alloc_1d_dbl(n_out + 1);
newnet-input_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
newnet-hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);
newnet-input_prev_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
newnet-hidden_prev_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);
return (newnet);
}
/* 释放BP网络所占地内存空间 */
void CAnnBP::bpnn_free(BPNN *net)
{
int n1, n2, i;
n1 = net-input_n;
n2 = net-hidden_n;
free((char *) net-input_units);
free((char *) net-hidden_units);
free((char *) net-output_units);
free((char *) net-hidden_delta);
free((char *) net-output_delta);
free((char *) net-target);
for (i = 0; i = n1; i++) {
free((char *) net-input_weights[i]);
free((char *) net-input_prev_weights[i]);
}
free((char *) net-input_weights);
free((char *) net-input_prev_weights);
for (i = 0; i = n2; i++) {
free((char *) net-hidden_weights[i]);
free((char *) net-hidden_prev_weights[i]);
}
free((char *) net-hidden_weights);
free((char *) net-hidden_prev_weights);
free((char *) net);
}
/*** 创建一个BP网络，并初始化权值***/
BPNN* CAnnBP::bpnn_create(int n_in, int n_hidden, int n_out)
{
BPNN *newnet;
newnet = bpnn_internal_create(n_in, n_hidden, n_out);
#ifdef INITZERO
bpnn_zero_weights(newnet-input_weights, n_in, n_hidden);
#else
bpnn_randomize_weights(newnet-input_weights, n_in, n_hidden);
#endif
bpnn_randomize_weights(newnet-hidden_weights, n_hidden, n_out);
bpnn_zero_weights(newnet-input_prev_weights, n_in, n_hidden);
bpnn_zero_weights(newnet-hidden_prev_weights, n_hidden, n_out);
return (newnet);
}
void CAnnBP::bpnn_layerforward(double *l1, double *l2, double **conn, int n1, int n2)
{
double sum;
int j, k;
/*** 设置阈值 ***/
l1[0] = 1.0;
/*** 对于第二层的每个神经元 ***/
for (j = 1; j = n2; j++) {
/*** 计算输入的加权总和 ***/
sum = 0.0;
for (k = 0; k = n1; k++) {
sum += conn[k][j] * l1[k];
}
l2[j] = squash(sum);
}
}
/* 输出误差 */
void CAnnBP::bpnn_output_error(double *delta, double *target, double *output, int nj, double *err)
{
int j;
double o, t, errsum;
errsum = 0.0;
for (j = 1; j = nj; j++) {
o = output[j];
t = target[j];
delta[j] = o * (1.0 - o) * (t - o);
errsum += ABS(delta[j]);
}
*err = errsum;
}
/* 隐含层误差 */
void CAnnBP::bpnn_hidden_error(double *delta_h, int nh, double *delta_o, int no, double **who, double *hidden, double *err)
{
int j, k;
double h, sum, errsum;
errsum = 0.0;
for (j = 1; j = nh; j++) {
h = hidden[j];
sum = 0.0;
for (k = 1; k = no; k++) {
sum += delta_o[k] * who[j][k];
}
delta_h[j] = h * (1.0 - h) * sum;
errsum += ABS(delta_h[j]);
}
*err = errsum;
}
/* 调整权值 */
void CAnnBP::bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double **w, double **oldw, double eta, double momentum)
{
double new_dw;
int k, j;
ly[0] = 1.0;
for (j = 1; j = ndelta; j++) {
for (k = 0; k = nly; k++) {
new_dw = ((eta * delta[j] * ly[k]) + (momentum * oldw[k][j]));
w[k][j] += new_dw;
oldw[k][j] = new_dw;
}
}
}
/* 进行前向运算 */
void CAnnBP::bpnn_feedforward(BPNN *net)
{
int in, hid, out;
in = net-input_n;
hid = net-hidden_n;
out = net-output_n;
/*** Feed forward input activations. ***/
bpnn_layerforward(net-input_units, net-hidden_units,
net-input_weights, in, hid);
bpnn_layerforward(net-hidden_units, net-output_units,
net-hidden_weights, hid, out);
}
/* 训练BP网络 */
void CAnnBP::bpnn_train(BPNN *net, double eta, double momentum, double *eo, double *eh)
{
int in, hid, out;
double out_err, hid_err;
in = net-input_n;
hid = net-hidden_n;
out = net-output_n;
/*** 前向输入激活 ***/
bpnn_layerforward(net-input_units, net-hidden_units,
net-input_weights, in, hid);
bpnn_layerforward(net-hidden_units, net-output_units,
net-hidden_weights, hid, out);
/*** 计算隐含层和输出层误差 ***/
bpnn_output_error(net-output_delta, net-target, net-output_units,
out, out_err);
bpnn_hidden_error(net-hidden_delta, hid, net-output_delta, out,
net-hidden_weights, net-hidden_units, hid_err);
*eo = out_err;
*eh = hid_err;
/*** 调整输入层和隐含层权值 ***/
bpnn_adjust_weights(net-output_delta, out, net-hidden_units, hid,
net-hidden_weights, net-hidden_prev_weights, eta, momentum);
bpnn_adjust_weights(net-hidden_delta, hid, net-input_units, in,
net-input_weights, net-input_prev_weights, eta, momentum);
}
/* 保存BP网络 */
void CAnnBP::bpnn_save(BPNN *net, char *filename)
{
CFile file;
char *mem;
int n1, n2, n3, i, j, memcnt;
double dvalue, **w;
n1 = net-input_n; n2 = net-hidden_n; n3 = net-output_n;
printf("Saving %dx%dx%d network to '%s'\n", n1, n2, n3, filename);
try
{
file.Open(filename,CFile::modeWrite|CFile::modeCreate|CFile::modeNoTruncate);
}
catch(CFileException* e)
{
e-ReportError();
e-Delete();
}
file.Write(n1,sizeof(int));
file.Write(n2,sizeof(int));
file.Write(n3,sizeof(int));
memcnt = 0;
w = net-input_weights;
mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(double)));
// mem = (char *) malloc (((n1+1) * (n2+1) * sizeof(double)));
for (i = 0; i = n1; i++) {
for (j = 0; j = n2; j++) {
dvalue = w[i][j];
//fastcopy(mem[memcnt], dvalue, sizeof(double));
fastcopy(mem[memcnt], dvalue, sizeof(double));
memcnt += sizeof(double);
}
}
file.Write(mem,sizeof(double)*(n1+1)*(n2+1));
free(mem);
memcnt = 0;
w = net-hidden_weights;
mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(double)));
// mem = (char *) malloc (((n2+1) * (n3+1) * sizeof(double)));
for (i = 0; i = n2; i++) {
for (j = 0; j = n3; j++) {
dvalue = w[i][j];
fastcopy(mem[memcnt], dvalue, sizeof(double));
// fastcopy(mem[memcnt], dvalue, sizeof(double));
memcnt += sizeof(double);
}
}
file.Write(mem, (n2+1) * (n3+1) * sizeof(double));
// free(mem);
file.Close();
return;
}
/* 从文件中读取BP网络 */
BPNN* CAnnBP::bpnn_read(char *filename)
{
char *mem;
BPNN *new1;
int n1, n2, n3, i, j, memcnt;
CFile file;
try
{
file.Open(filename,CFile::modeRead|CFile::modeCreate|CFile::modeNoTruncate);
}
catch(CFileException* e)
{
e-ReportError();
e-Delete();
}
// printf("Reading '%s'\n", filename);// fflush(stdout);
file.Read(n1, sizeof(int));
file.Read(n2, sizeof(int));
file.Read(n3, sizeof(int));
new1 = bpnn_internal_create(n1, n2, n3);
// printf("'%s' contains a %dx%dx%d network\n", filename, n1, n2, n3);
// printf("Reading input weights..."); // fflush(stdout);
memcnt = 0;
mem = (char *) malloc (((n1+1) * (n2+1) * sizeof(double)));
file.Read(mem, ((n1+1)*(n2+1))*sizeof(double));
for (i = 0; i = n1; i++) {
for (j = 0; j = n2; j++) {
//fastcopy((new1-input_weights[i][j]), mem[memcnt], sizeof(double));
fastcopy((new1-input_weights[i][j]), mem[memcnt], sizeof(double));
memcnt += sizeof(double);
}
}
free(mem);
// printf("Done\nReading hidden weights..."); //fflush(stdout);
memcnt = 0;
mem = (char *) malloc (((n2+1) * (n3+1) * sizeof(double)));
file.Read(mem, (n2+1) * (n3+1) * sizeof(double));
for (i = 0; i = n2; i++) {
for (j = 0; j = n3; j++) {
//fastcopy((new1-hidden_weights[i][j]), mem[memcnt], sizeof(double));
fastcopy((new1-hidden_weights[i][j]), mem[memcnt], sizeof(double));
memcnt += sizeof(double);
}
}
free(mem);
file.Close();
printf("Done\n"); //fflush(stdout);
bpnn_zero_weights(new1-input_prev_weights, n1, n2);
bpnn_zero_weights(new1-hidden_prev_weights, n2, n3);
return (new1);
}
void CAnnBP::CreateBP(int n_in, int n_hidden, int n_out)
{
net=bpnn_create(n_in,n_hidden,n_out);
}
void CAnnBP::FreeBP()
{
bpnn_free(net);
}
void CAnnBP::Train(double *input_unit,int input_num, double *target,int target_num, double *eo, double *eh)
{
for(int i=1;i=input_num;i++)
{
net-input_units[i]=input_unit[i-1];
}
for(int j=1;j=target_num;j++)
{
net-target[j]=target[j-1];
}
bpnn_train(net,eta1,momentum1,eo,eh);
}
void CAnnBP::Identify(double *input_unit,int input_num,double *target,int target_num)
{
for(int i=1;i=input_num;i++)
{
net-input_units[i]=input_unit[i-1];
}
bpnn_feedforward(net);
for(int j=1;j=target_num;j++)
{
target[j-1]=net-output_units[j];
}
}
void CAnnBP::Save(char *filename)
{
bpnn_save(net,filename);
}
void CAnnBP::Read(char *filename)
{
net=bpnn_read(filename);
}
void CAnnBP::SetBParm(double eta, double momentum)
{
eta1=eta;
momentum1=momentum;
}
void CAnnBP::Initialize(int seed)
{
bpnn_initialize(seed);
}
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