?? lineardiscriminant.cpp
字號:
#include <stdio.h>
#include <stdlib.h>
#include <iostream.h>
using namespace std;
#define TOTAL_NUMBER 50//樣本總數
#define TRAINING_NUMBER 30//用于訓練的樣本數量
#define TESTING_NUMBER 20//用于測試的樣本數量
#define BITMAP_SIZE 64//位圖大小
#define FEATURE_NUMBER//提取的特征數量
#define START_VALUE//w各分量的初值
#define STEP_LENGTH 5//步長
char map[BITMAP_SIZE][BITMAP_SIZE];//位圖
float feature[FEATURE_NUMBER+2];//特征第1位為對應的數字，第二位到倒數第二位為所有的特征，最后一位為擴展位(等于1)
int w0[FEATURE_NUMBER+1],w1[FEATURE_NUMBER+1],w4[FEATURE_NUMBER+1],w5[FEATURE_NUMBER+1],w6[FEATURE_NUMBER+1],w7[FEATURE_NUMBER+1],w8[FEATURE_NUMBER+1],w9[FEATURE_NUMBER+1];//判別函數(8類共8個)
void FeatureExtraction(int i)//處理數組map,提取特征，存入feature
{
}
void training()
{
int i,j,f,g,h;
int sum,get_figure;
while(f<8*TRAINING_NUMBER){
f=0;
for(i=1;i<=8*TRAINING_NUMBER;i++){
//從training_feature.txt中讀取一個樣本，存于數組feature中
sum=0;
get_figure=0;
h=0;
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w0[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=0;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w1[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=1;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w4[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=4;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w5[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=5;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w6[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=6;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w7[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=7;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w8[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=8;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w9[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=9;
}
if(get_figure!=feature[0]){
switch(get_figure)
{
case 0:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w0[j]=w0[j]+feature[j+1]*STEP_LENGTH;
}
case 1:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w1[j]=w1[j]+feature[j+1]*STEP_LENGTH;
}
case 4:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w4[j]=w4[j]+feature[j+1]*STEP_LENGTH;
}
case 5:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w5[j]=w5[j]+feature[j+1]*STEP_LENGTH;
}
case 6:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w6[j]=w6[j]+feature[j+1]*STEP_LENGTH;
}
case 7:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w7[j]=w7[j]+feature[j+1]*STEP_LENGTH;
}
case 8:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w8[j]=w8[j]+feature[j+1]*STEP_LENGTH;
}
case 9:
{
for(j=0;j<=FEATURE_NUMBER;j++)
w9[j]=w9[j]+feature[j+1]*STEP_LENGTH;
}
default:break;
}
}
else
f++;
}
}
return;
}
void testing()
{
int i,j,g,h;
int sum,get_figure;
for(i=1;i<=8*TRAINING_NUMBER;i++){
//從testing_feature.txt中讀取一個樣本，存于數組feature中
sum=0;
get_figure=0;
h=0;
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w0[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=0;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w1[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=1;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w4[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=4;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w5[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=5;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w6[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=6;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w7[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=7;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w8[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=8;
}
for(j=1;j<=FEATURE_NUMBER+1;j++){
g=feature[j]*w9[j-1];
h=h+g;
}
if(h>sum){
sum=h;
get_figure=9;
}
cout<<feature[0]<<" "<<get_figure<<"\n";//真實值與識別值的對比
}
}
int main()
{
int i,j;
//----------------------特征提取---------------------
//1，選取特征
//2，輸入8個文件(8個數字，每個數字一個文件，每個文件中包含50個樣本)
// 輸出兩個文件(一個文件存訓練樣本提取出的8*30個特征向量，另一個文件存測試樣本提取出的8*20個特征向量)
ifstream inf;
ofstream training_outf,testing_outf;
training_outf.open("training_feature.txt");
testing_outf.open("testing_feature.txt");
for(i=0;i<=9;i++){
switch(i)
{
case 0:inf.open("digit0.dat");
case 1:inf.open("digit1.dat");
case 4:inf.open("digit4.dat");
case 5:inf.open("digit5.dat");
case 6:inf.open("digit6.dat");
case 7:inf.open("digit7.dat");
case 8:inf.open("digit8.dat");
case 9:inf.open("digit9.dat");
default:break;
}
char c;
inf>>noskipws;//不忽略空白，把每行最后那個'\n'也讀進來
int sample_number=0;//已提取特征的樣本數，前30個用于訓練，存入文件(training_feature.txt)，后20個用于測試，存入文件(testing_feature.txt)
int j;
float d;
while(inf>>c){
int s=0,t=0;
int stopline=0;
while((inf>>c)&&(stopline!=BITMAP_SIZE)){
if(c!='\n'){
map[s][t]=c;
t++;
}
else{
s++;
t=0;
stopline++;
}
}
sample_number++;
//處理數組map,包括提取特征，存入feature，再將feature存入文件(training_feature.txt)或(testing_feature.txt)
FeatureExtraction(i);//i代表數字'i'
if(sample_number<=30){
for(j=0;j<=FEATURE_NUMBER+1;j++){
d=feature[j];
training_outf<<d;
if(j<FEATURE_NUMBER+1)
training_outf<<" ";
else
training_outf<<"\n";
}
}
else{
for(j=0;j<=FEATURE_NUMBER+1;j++){
d=feature[j];
testing_outf<<d;
if(j<FEATURE_NUMBER+1)
testning_outf<<" ";
else
testing_outf<<"\n";
}
}
inf.close();
}
training_outf.close();
testing_outf.close();
//----------------------訓練-------------------------
//1，設定一個步長STEP_LENGTH
//2，為每一個判別函數(8類共8個)的系數向量w設初值，維數=特征個數+1(對應判別函數中的常數)
/*3，用訓練樣本訓練判別函數，每一步都是把一個樣本作為8個函數的輸入，分別計算輸出，
得出分類結果(輸出值最大的那個類)，如果分類結果正確，則無需修改函數的系數w向量，
如果分類結果錯誤，則修改應分得的類的判別函數的系數w,w是一個向量，對于每一個
分量，都增加一個因子：(訓練樣本的相應分量*步長STEP_LENGTH)
*/
//4，循環，每次循環都要使用全部的訓練樣本(8*30個)，直到在一次循環中10個判別函數的系數都不改變
for(i=0;i<=FEATURE_NUMBER){
w0[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w1[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w4[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w5[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w6[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w7[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w8[i]=START_VALUE;
}
for(i=0;i<=FEATURE_NUMBER){
w9[i]=START_VALUE;
}
training();
//----------------------測試-------------------------
//1，選一個測試樣本作為8個函數的輸入，分別計算輸出，得出分類結果(輸出值最大的那個類)
//2，循環，對每個測試樣本計算分類結果
testing();
system("PAUSE");
return 0;
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