public WeakLearnPara(List <WeakLearn> pool, int start, int count, AdaBoost ada, ManualResetEvent doneEvent)
{
m_pool = pool;
m_start = start;
m_count = count;
m_ada = ada;
m_doneEvent = doneEvent;
}
/// <summary>
/// foamliu, 2009/02/24, 初始化权重.
///
/// </summary>
private static void InitializeWeights(AdaBoost ada, ref double[] dist)
{
double vote = 1.0 / ada.m_m;
for (int i = 0; i < ada.m_m; i++)
{
dist[i] = vote;
}
}
/// <summary>
/// foamliu, 2009/02/24, 更新权重分布.
///
/// </summary>
/// <param name="at"></param>
/// <param name="ht"></param>
private static void UpdateDistribution(AdaBoost ada, ref double[] dist, double at, WeakLearn ht)
{
for (int i = 0; i < ada.m_m; i++)
{
Example example = ada.m_t_set[i];
int iResult = ht.Predict(example.X);
// foamliu, 2009/03/04, 更新权重, example.Label.Id 和 iResult 可能的取值均为 -1和+1,
// 当它们相同, 乘积为1, 整个项 > 0, 乘子 > 1, 权重提升;
// 当它们不同, 乘积为-1, 整个项 < 0, 乘子 < 1, 权重下降.
dist[i] = dist[i] * Math.Exp(-at * example.Label.Id * iResult);
}
}
/// <summary>
/// foamliu, 2009/02/24, 归一化权重分布.
///
/// </summary>
/// <param name="t"></param>
private static void NormalizeDistribution(AdaBoost ada, ref double[] dist)
{
double sum = 0.0;
for (int i = 0; i < ada.m_m; i++)
{
sum += dist[i];
}
double reciprocal = 1.0 / sum;
for (int i = 0; i < ada.m_m; i++)
{
// foamliu, 2009/02/04, 优化.
dist[i] *= reciprocal;
}
}