result.cpp

同样经典的由PLATT提出的SMO学习算法.C++语言实现.好用呢

#include "stdafx.h"
#include <stdio.h>
#include <math.h>
//#include <fstream.h>
#include <float.h>
#include "initializeTraining.h"
#include "learn.h"
#include "result.h"

int boundSv;
int unBoundSv;

void writeModel(FILE * out)
{
	int i,j, numSv;
	
	numSv=0; unBoundSv=0;boundSv=0;

	//find out the number of support vectors ,both bound and unbound
	for(i =1;i<=numExample;i++){
		if(lambda[i]>0){
			numSv++;
			if(lambda[i]<C)
				unBoundSv++;
			else
				boundSv++;
		}
	}
	//write to the model file
	if(kernelType ==0)
	  fprintf(out,"%d #Linear \n",0);  //fprintf(stderr,"%d #Linear \n",0);
	  
	else if(kernelType ==1)
		fprintf(out,"%d %d # Polynomial with degree %d\n",1,degree,degree);
	else if (kernelType ==2)
		fprintf(out,"%d %f #rbf with variance %f\n",2,sigmaSqr,sigmaSqr);

	fprintf(out,"%d # Number of features\n",maxFeature);

	if(kernelType ==0){
		for(i=1;i<=maxFeature;i++)
			fprintf(out,"%f ",weight[i]);
		fprintf(out,"#weight vector\n");
	}

	fprintf(out,"%6.5f #Threshold b\n",b);
	fprintf(out,"%6.5f #C parameter\n",C);
    fprintf(out,"%d #Number of support vectors\n",numSv); 

	/***write the value of (class label*lambda)**/

	if(numSv !=0){
		for(i=1;i<=numExample;i++){
			if(lambda[i]>0){
				if(target[i]==1)
					fprintf(out,"%.5f ",lambda[i]);
				else if(target[i]==-1)
	                fprintf(out,"%.5f ",-lambda[i]);
			
				for (j=0;j<nonZeroFeature[i];j++)
						fprintf(out,"%d:%.5f ",example[i][j]->id,example[i][j]->value);
				  fprintf(out,"\n");
			}
		}
	}
}

double calculateNorm(void)
{
	int i;
	double n;
	n=0;
	for(i=1;i<=maxFeature;i++)
		n+=weight[i]*weight[i];
	return sqrt(n);
}

double getb(void)
{
	return b;
}