libsvm数据缩放方法

assumption: min index of attributes is 1 
pass 1: find out max index of attributes ：

1.1也就是找出每行有多少个特征数据，因为libsvm特征格式中每个特征前面带有下标，缺失的认为是0，这样避免稀疏矩阵，以提高计算速度。其实我获取的数据即便是0值也进行了保存，如果在保存特征时进行0值判断的话，会变得有点麻烦，也就简单化处理。这是以后可以修改的一个地方。

1.2创建保存最值的数组，并初始化。

if(restore_filename)
	{
		int idx, c;
		fp_restore = fopen(restore_filename,"r");
		c = fgetc(fp_restore);
		if(c == 'y')
		{
			readline(fp_restore);
			readline(fp_restore);
			readline(fp_restore);
		}
		readline(fp_restore);
		readline(fp_restore);
		while(fscanf(fp_restore,"%d %*f %*f\n",&idx) == 1)
			max_index = max(idx,max_index);
		rewind(fp_restore);
	}
	while(readline(fp)!=NULL)
	{
		char *p=line;

		SKIP_TARGET

		while(sscanf(p,"%d:%*f",&index)==1)
		{
			max_index = max(max_index, index);
			SKIP_ELEMENT
			num_nonzeros++;
		}		
	}
	rewind(fp);

//创建保存最值的数组
	feature_max = (double *)malloc((max_index+1)* sizeof(double));
	feature_min = (double *)malloc((max_index+1)* sizeof(double));
	if(feature_max == NULL || feature_min == NULL)
	{
		fprintf(stderr,"can't allocate enough memory\n");
		exit(1);
	}

//初始化
	for(i=0;i<=max_index;i++)
	{
		feature_max[i]=-DBL_MAX;
		feature_min[i]=DBL_MAX;
	}

pass 2: find out min/max value，找出每行中的最大与最小值，并传递到相应数组。

while(readline(fp)!=NULL)
	{
		char *p=line;
		int next_index=1;
		double target;
		double value;

		sscanf(p,"%lf",&target);
		y_max = max(y_max,target);
		y_min = min(y_min,target);
		
		SKIP_TARGET

		while(sscanf(p,"%d:%lf",&index,&value)==2)
		{
			for(i=next_index;i<index;i++)
			{
				feature_max[i]=max(feature_max[i],0);
				feature_min[i]=min(feature_min[i],0);
			}
			
			feature_max[index]=max(feature_max[index],value);
			feature_min[index]=min(feature_min[index],value);

			SKIP_ELEMENT
			next_index=index+1;
		}		

		for(i=next_index;i<=max_index;i++)
		{
			feature_max[i]=max(feature_max[i],0);
			feature_min[i]=min(feature_min[i],0);
		}	
	}

	rewind(fp);

　　pass 3: scale 缩放

while(readline(fp)!=NULL)
	{
		char *p=line;
		int next_index=1;
		double target;
		double value;
		sscanf(p,"%lf",&target);
		output_target(target);
		SKIP_TARGET
		while(sscanf(p,"%d:%lf",&index,&value)==2)
		{
			for(i=next_index;i<index;i++)
				output(i,0);
			
			output(index,value);

			SKIP_ELEMENT
			next_index=index+1;
		}		
		for(i=next_index;i<=max_index;i++)
			output(i,0);

		printf("\n");
	}

　　

void output_target(double value)
{
	if(y_scaling)
	{
		if(value == y_min)
			value = y_lower;
		else if(value == y_max)
			value = y_upper;
		else value = y_lower + (y_upper-y_lower) *
			     (value - y_min)/(y_max-y_min);
	}
	printf("%g ",value);
}

　　

效果：消除了奇异样本数据对处理过程的影响。