private void UpdateWeights(WeakClassifier bestClassifier, int numPos, int numNeg, float[] sampleWeight)
{
int count = numNeg + numPos;
double err = 0;
int i;
bool[] classifyResult = new bool[count];
bool result;
for (i = 0; i < numPos; i++)
{
result = bestClassifier.Predict(i);
if (result == false)
{
err += sampleWeight[i]; //累加分类错误的样本权值
}
classifyResult[i] = result;
}
for (i = numPos; i < count; i++)
{
result = bestClassifier.Predict(i);
if (result == true)
{
err += sampleWeight[i]; //累加分类错误的样本权值
}
classifyResult[i] = !result;
}
//调整样本权值
// logRatio( double val )
// {
// const double eps = 1e-5;
//
// val = max( val, eps );
// val = min( val, 1. - eps );
// return log( val/(1. - val) );
// }
const double eps = (double)1e-5;
double val = err;
val = Math.Max(val, eps);
val = Math.Min(val, 1 - eps);
err = val;
MyFloat factor = (MyFloat)(err / (1 - err));
for (int indexSample = 0; indexSample < count; indexSample++)
{
if (classifyResult[indexSample])
{
sampleWeight[indexSample] *= factor;
}
}
bestClassifier.Weight = -(MyFloat)Math.Log10(factor); //设定弱分类器权值α
}
/// <summary>
/// 使用AdaBoost算法训练一次，得到一个最优的弱分类器
/// </summary>
/// <param name="posSamples">正样本集</param>
/// <param name="negSamples">负样本集</param>
/// <param name="sampleWeight">样本权值，依次包含了正样本和负样本的权值</param>
/// <returns>分类误差最优的弱分类器</returns>
private WeakClassifier AdaBoost(int numPos, int numNeg, MyFloat[] sampleWeight)
{
int count = numNeg + numPos;
double minErr = double.MaxValue;
int minErrIndex = 0, i;
WeakClassifier[] weakClassifiers = WeakClassifierManager.Instance.WeakClassifiers;
int classifierCount = weakClassifiers.Length;
for (int indexClassifier = 0; indexClassifier < classifierCount; indexClassifier++)
{
WeakClassifier classifier = weakClassifiers[indexClassifier];
double errCount = 0;
bool result;
for (i = 0; i < numPos; i++)
{
//result=classifier.Predict(posSamples[i]);
result = classifier.Predict(i);
if (result == false)
{
errCount += sampleWeight[i]; //累加分类错误的样本权值
}
}
for (; i < count; i++)
{
//result = classifier.Predict(negSamples[i]);
result = classifier.Predict(i);
if (result == true)
{
errCount += sampleWeight[i]; //累加分类错误的样本权值
}
}
if (errCount < minErr) //记录最佳弱分类器
{
minErr = errCount;
minErrIndex = indexClassifier;
}
}
//DebugMsg.AddMessage(minErr.ToString(), 0);
bool[] classifyResult = new bool[count];
WeakClassifier bestClassifier = (WeakClassifier)weakClassifiers[minErrIndex].Clone();
{
bool result;
for (i = 0; i < numPos; i++)
{
result = bestClassifier.Predict(i);
classifyResult[i] = result;
}
for (; i < count; i++)
{
result = bestClassifier.Predict(i);
classifyResult[i] = !result;
}
}
//调整样本权值
// logRatio( double val )
// {
// const double eps = 1e-5;
//
// val = max( val, eps );
// val = min( val, 1. - eps );
// return log( val/(1. - val) );
// }
const double eps = (double)1e-5;
double val = minErr;
val = Math.Max(val, eps);
val = Math.Min(val, 1 - eps);
minErr = val;
MyFloat factor = (MyFloat)(minErr / (1 - minErr));
for (int indexSample = 0; indexSample < count; indexSample++)
{
if (classifyResult[indexSample])
{
sampleWeight[indexSample] *= factor;
}
}
//WeakClassifier bestClassifier= (WeakClassifier)weakClassifiers[minErrIndex].Clone();
//bestClassifier.Weight =(MyFloat) Math.Log10(1 / factor); //设定弱分类器权值α
bestClassifier.Weight = -(MyFloat)Math.Log10(factor); //设定弱分类器权值α
return(bestClassifier);
}
