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_TARGETwhile(sscanf(p,"%d:%*f",&index)==1){max_index = max(max_index, index);SKIP_ELEMENTnum_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_TARGETwhile(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_ELEMENTnext_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_TARGETwhile(sscanf(p,"%d:%lf",&index,&value)==2){for(i=next_index;i<index;i++)output(i,0);output(index,value);SKIP_ELEMENTnext_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。