SVM与C++源码实现

1. 推导出函数间隔最小

2. 约束优化函数变形至如下形式

/*
min 1/2*||w||^2
s.t.  (w[i]*x[i] + b[i] - y[i]) >= 0;
*/

3. 对偶函数

/*
min(para alpha) 1/2*sum(i)sum(j)(alpha[i]*alpha[j]*y[i]*y[j]*x[i]*x[j]) - sum(alpha[i])
s.t. sum(alpha[i] * y[i]) = 0
C>= alpha[i] >= 0
*

4. 根据KKT条件优化。。

下面是C++代码

/*********************************************************
**CopyRight by Weidi Xu, S.C.U.T in Guangdong, Guangzhou**
**********************************************************/

#include <iostream>
#include <cstdio>
#include <algorithm>
#include <cmath>

using std::sort;
using std::fabs;

const int MAX_DIMENSION = 2;
const int MAX_SAMPLES = 3;
double x[MAX_SAMPLES][MAX_DIMENSION];
double y[MAX_SAMPLES];
double alpha[MAX_SAMPLES];
double w[MAX_DIMENSION];
double b;
double c;
double eps = 1e-6;
struct _E{
double val;
int index;
}E[MAX_SAMPLES];

bool cmp(const _E & a, const _E & b)
{
return a.val < b.val;
}

int num_dimension;
int num_samples;

double max(double a,double b)
{
return a>b?a:b;
}

double min(double a,double b)
{
return a>b?b:a;
}

double kernal(double x1[], double x2[], double dimension)
{
double ans = 0 ;
for(int i = 0 ; i < dimension; i++)
{
ans += x1[i]*x2[i];
}
return ans;
}

double target_function()
{
double ans = 0;
for(int i = 0 ; i < num_samples; i++)
{
for(int j = 0 ; j < num_samples; j++)
{
ans += alpha[i]*alpha[j]*y[i]*y[j]*kernal(x[i],x[j],num_dimension);
}
}

for(int i = 0 ; i < num_samples; i++)
{
ans -= alpha[i];
}

return ans;
}

double g(double _x[], int dimension)
{
double ans = b;

for(int i = 0 ; i < num_samples; i++)
{
ans += alpha[i]*y[i]*kernal(x[i],_x,dimension);
}

return ans;
}

bool satisfy_constrains(int i, int dimension)
{
if(alpha[i] == 0)
{
if(y[i]*g(x[i], dimension) >= 1)
return true;
else
return false;
}
else if( alpha[i] > 0 && alpha[i] < c)
{
if(y[i] * g(x[i], dimension) ==  1)
return true;
else
return false;
}
else
{
if(y[i] * g(x[i], dimension) <=  1)
return true;
else
return false;
}
}

double calE(int i, int dimension)
{
return g(x[i], dimension) - y[i];
}

void calW()
{
for(int i = 0 ; i < num_dimension; i++)
{
w[i] = 0;
for(int j = 0 ; j < num_samples; j++)
{
w[i] += alpha[j] * y[j] * x[j][i];
}
}
return ;
}

void calB()
{
double ans = y[0];
for(int i = 0 ;  i < num_samples ; i++)
{
ans -= y[i]*alpha[i]*kernal(x[i], x[0], num_dimension);
}
b = ans;
return;
}

void recalB(int alpha1index,int alpha2index, int dimension, double alpha1old, double alpha2old)
{
double alpha1new = alpha[alpha1index];
double alpha2new = alpha[alpha2index];

alpha[alpha1index] = alpha1old;
alpha[alpha2index] = alpha2old;

double e1 = calE(alpha1index, num_dimension);
double e2 = calE(alpha2index, num_dimension);

alpha[alpha1index] = alpha1new;
alpha[alpha2index] = alpha2new;

double b1new = -e1 - y[alpha1index]*kernal(x[alpha1index], x[alpha1index], dimension)*(alpha1new - alpha1old);
b1new -= y[alpha2index]*kernal(x[alpha2index], x[alpha1index], dimension)*(alpha2new - alpha2old) + b;

double b2new = -e2 - y[alpha1index]*kernal(x[alpha1index], x[alpha2index], dimension)*(alpha1new - alpha1old);
b1new -= y[alpha2index]*kernal(x[alpha2index], x[alpha2index], dimension)*(alpha2new - alpha2old) + b;

b = (b1new + b2new)/2;
}

bool optimizehelp(int alpha1index,int alpha2index)
{
double alpha1new = alpha[alpha1index];
double alpha2new = alpha[alpha2index];

double alpha1old = alpha[alpha1index];
double alpha2old = alpha[alpha2index];

double H,L;

if(fabs(y[alpha1index] - y[alpha2index]) > eps)
{
L = max(0, alpha2old - alpha1old);
H = min(c, c + alpha2old - alpha1old);
}
else
{
L = max(0, alpha2old + alpha1old - c);
H = min(c, alpha2old + alpha1old);
}

//cal new
double lena = kernal(x[alpha1index], x[alpha1index], num_dimension) + kernal(x[alpha2index], x[alpha2index], num_dimension) - 2*kernal(x[alpha1index], x[alpha2index], num_dimension);
alpha2new = alpha2old + y[alpha2index]*(calE(alpha1index, num_dimension) - calE(alpha2index, num_dimension))/lena;

if(alpha2new > H)
{
alpha2new = H;
}
else if( alpha2new < L)
{
alpha2new = L;
}

alpha1new = alpha1old + y[alpha1index]*y[alpha2index]*(alpha2old - alpha2new);

double energyold = target_function();

alpha[alpha1index] = alpha1new;
alpha[alpha2index] = alpha2new;

double gap = 0.001;

recalB(alpha1index, alpha2index, num_dimension, alpha1old, alpha2old);
return true;
}

bool optimize()
{
int alpha1index = -1;
int alpha2index = -1;
double alpha2new = 0;
double alpha1new = 0;

//cal E[]
for(int i = 0 ; i < num_samples; i++)
{
E[i].val = calE(i, num_dimension);
E[i].index = i;
}

//traverse the alpha1index with 0 < && < c
for(int i = 0 ; i < num_samples; i++)
{
alpha1new = alpha[i];

if(alpha1new > 0 && alpha1new < c)
{

if(satisfy_constrains(i, num_dimension))
continue;

sort(E, E+num_samples, cmp);

//simply find the maximum or minimun;
if(alpha1new > 0)
{
if(E[0].index == i)
{
;
}
else
{
alpha1index = i;
alpha2index = E[0].index;
if(optimizehelp(alpha1index, alpha2index))
{
return true;
}
}
}
else
{
if(E[num_samples-1].index == i)
{
;
}
else
{
alpha1index = i;
alpha2index = E[num_samples-1].index;
if(optimizehelp(alpha1index, alpha2index))
{
return true;
}
}
}

//find the alpha2 > 0 && < c
for(int j = 0 ; j < num_samples; j++)
{
alpha2new = alpha[j];

if(alpha2new > 0 && alpha2new < c)
{
alpha1index = i;
alpha2index = j;
if(optimizehelp(alpha1index , alpha2index))
{
return true;
}
}
}

//find other alpha2
for(int j = 0 ; j < num_samples; j++)
{
alpha2new = alpha[j];

if(!(alpha2new > 0 && alpha2new < c))
{
alpha1index = i;
alpha2index = j;
if(optimizehelp(alpha1index , alpha2index))
{
return true;
}
}
}
}
}

//find all alpha1
for(int i = 0 ; i < num_samples; i++)
{
alpha1new = alpha[i];

if(!(alpha1new > 0 && alpha1new < c))
{
if(satisfy_constrains(i, num_dimension))
continue;

sort(E, E+num_samples, cmp);

//simply find the maximum or minimun;
if(alpha1new > 0)
{
if(E[0].index == i)
{
;
}
else
{
alpha1index = i;
alpha2index = E[0].index;
if(optimizehelp(alpha1index, alpha2index))
{
return true;
}
}
}
else
{
if(E[num_samples-1].index == i)
{
;
}
else
{
alpha1index = i;
alpha2index = E[num_samples-1].index;
if(optimizehelp(alpha1index, alpha2index))
{
return true;
}
}
}

//find the alpha2 > 0 && < c
for(int j = 0 ; j < num_samples; j++)
{
alpha2new = alpha[j];

if(alpha2new > 0 && alpha2new < c)
{
alpha1index = i;
alpha2index = j;
if(optimizehelp(alpha1index , alpha2index))
{
return true;
}
}
}

//find other alpha2
for(int j = 0 ; j < num_samples; j++)
{
alpha2new = alpha[j];

if(!(alpha2new > 0 && alpha2new < c))
{
alpha1index = i;
alpha2index = j;
if(optimizehelp(alpha1index , alpha2index))
{
return true;
}
}
}
}
}

//for(int i = 0 ; i < num_samples; i++)
//{
//    alpha1new = alpha[i];

//    for(int j = 0 ; j < num_samples; j++)
//    {
//        if(1)
//        {
//            alpha1index = i;
//            alpha2index = j;
//            if(optimizehelp(alpha1index , alpha2index))
//            {
//                return true;
//            }
//        }
//    }
//}
return false;
}

bool check()
{
double sum = 0;
for(int i = 0 ; i < num_samples; i++)
{
sum += alpha[i] * y[i];
if(!(0 <= alpha[i] && alpha[i] <= c))
{
printf("alpha[%d]: %lf wrong\n", i, alpha[i]);
return false;
}
if(!satisfy_constrains(i, num_dimension))
{
printf("alpha[%d] not satisfy constrains\n", i);
return false;
}
}

if(fabs(sum) > eps)
{
printf("Sum = %lf\n", sum);
return false;
}
return true;
}
/*
min 1/2*||w||^2
s.t.  (w[i]*x[i] + b[i] - y[i]) >= 0;
*//*
step 1: cal alpha[]
step 2: cal w,b
*/

/*
min(para alpha) 1/2*sum(i)sum(j)(alpha[i]*alpha[j]*y[i]*y[j]*x[i]*x[j]) - sum(alpha[i])
s.t. sum(alpha[i] * y[i]) = 0
C>= alpha[i] >= 0
*/

int main()
{
scanf("%d%d", &num_samples, &num_dimension);

for(int i = 0 ; i < num_samples; i++)
{
for(int j = 0; j < num_dimension; j++)
{
scanf("%lf",&x[i][j]);
}
scanf("%lf",&y[i]);
}
c = 1;

//初值附为0；
for(int i = 0 ; i < num_samples; i++)
{
alpha[i] = 0;
}

int count = 0;
while(optimize()){
calB();
count++;
}
printf("%d ",count);

calW();
calB();

printf("y = ");

for(int i = 0 ; i < num_dimension; i++)
{
printf("%lf * x[%d] + ", w[i], i);
}
printf("%lf\n", b);

if(!check())
printf("Not satisfy KKT.\n");
else
printf("Satisfy KKT\n");
}

/*
3 2
3 3 1
4 3 1
1 1 -1
*/

实验结论：

1. SVM的收敛与迭代顺序和初值基本无关。

2. 将不满足kkt条件的alpha值进行修改不一定减少目标函数（未验证，实验的感觉是这样的）。因为加入每次目标函数减少的限制后，不收敛到最优值。