private static void WeakLearnThread(object o)
{
WeakLearnPara para = (WeakLearnPara)o;
for (int i = para.Start; i < para.Start + para.Count; i++)
{
WeakLearn learn = para.Pool[i];
learn.Distribution = para.AdaBoost.m_dist;
learn.Train();
learn.CalcR();
}
// foamliu, 2009/03/09, 完成, 通知主训练线程.
para.DoneEvent.Set();
}
/// <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.Examples[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, 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.Examples[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);
}
}