histogram.cc

经网络提出了一种基于蚁群聚类算法的径向基神经网络. 利用蚁群算法的并行寻优特征和挥发系数方法的自适应更改信息量的能力,并以球面聚类的方式确定了径向基神经网络中基函数的位置, 同时通过比较隐层神经元的相

#include "histogram.h"

double Histogram::findBucket (double value)
{
	return (round (value / bucketWidth)) * bucketWidth;
}

void Histogram::add (Histogram *h) 
{
	ASSERT (bucketWidth == h->bucketWidth);
	for (map<double,int>::const_iterator iter =
		value2count.begin();
		iter != value2count.end(); iter++)
	{
			add (iter->first, iter->second, false);
	}
	size += h->size;
	sum += h->sum;
}

void Histogram::add (double value, int count, bool doCoerce) 
{
	// to get more exact mean, remember values before we coerce them
	sum += (value * count);
	if (doCoerce) 
	{
		value = findBucket (value);
	}

	map<double,int>::iterator iter = value2count.find(value);
	if (iter == value2count.end()) 
	{
		value2count.insert (pair<double,int>(value,count));
		values.push_back (value);
	} 
	else
	{
		iter->second += count;
	}
	size += count;
}

double Histogram::mean ()
{
	if (size == 0)
		return 0.;
	return (sum / size);
}

double Histogram::percentile (double pct)
{
	sort (values.begin(), values.end());
	int currentIndex = 0;
	int targetIndex = int(size * pct);
	double currentValue;
	if (size == 0) return 0;

	for (map<double,int>::const_iterator iter =
		value2count.begin();
		iter != value2count.end(); iter++) 
	{
			currentIndex += iter->second;
			currentValue = iter->first;

			if (currentIndex >= targetIndex)
				break;
	}
	return currentValue;
}

ostream& operator << (ostream& os, Histogram* h) 
{
	for (map<double,int>::const_iterator iter =
		h->value2count.begin();
		iter != h->value2count.end(); iter++) 
	{
			os << iter->first << " -> " << iter->second << "\n";
	}
	return os;
}

/*
int main () 
{
printf ("histogram driver\n");
Histogram *h = new Histogram (2.);
h->add (2);
h->add (3);
h->add (3.2);
h->add (1.8);
h->add (10);
h->add (11);
h->add (9);

cout << h << endl;
cout << "mean " << h->mean() << endl;
cout << "p 0 " << h->percentile (0) << endl;
cout << "p .1 " << h->percentile (.1) << endl;
cout << "p .5 " << h->percentile (.5) << endl;
cout << "p .9 " << h->percentile (.9) << endl;
cout << "p 1 " << h->percentile (1) << endl;

}
*/