////
/// <summary>
/// 求delta_i
/// </summary>
/// <param name="empirical_e">@param empirical_e fi的期望</param>
/// <param name="fi">@param fi fi的下标</param>
/// <returns></returns>
//private double iis_solve_delta(double empirical_e, int fi)
private double iis_solve_delta(double empirical_e, string fi)
{
double delta = 0;
double f_newton, df_newton;
double[,] p_yx = calc_prob_y_given_x();
int iters = 0;
while (iters < 50) // 牛顿法
{
f_newton = df_newton = 0;
for (int i = 0; i < instances.Count; i++)
{
MLInstance <int, int> instance = instances[i];
MLFeature <int> tfeature = instance.Feature;
Feature feature = new Feature(tfeature);
int index = features.IndexOf(feature);
if (index == -1)
{
index = feature.getIndex(instance.Feature.Count);
if (string.Join("", features[index]) == string.Join("", feature))
{
features[index] = feature;
}
}
for (int y = minY; y <= maxY; y++)
{
int f_sharp = apply_f_sharp(feature, y);
double prod = p_yx[index, y] * functions[fi].Apply(feature, y) * Math.Exp(delta * f_sharp);
f_newton += prod;
df_newton += prod * f_sharp;
}
}
f_newton = empirical_e - f_newton / N; // g
df_newton = -df_newton / N; // g的导数
if (Math.Abs(f_newton) < 0.0000001)
{
return(delta);
}
double ratio = f_newton / df_newton;
delta -= ratio;
if (Math.Abs(ratio) < EPSILON)
{
return(delta);
}
iters++;
}
return(double.NaN); //如果不收敛，返回NaN
throw new Exception("IIS did not converge"); // w_i不收敛
}
/**
* 分类
* @param instance
* @return
*/
public override int Classify(MLInstance <int, int> instance)
{
double max = 0;
int label = -1;
//for (int y = minY; y <= maxY; y++)
for (int y = FeatureSummary.LabelList[0]; y <= FeatureSummary.LabelList[FeatureSummary.LabelCnt - 1]; y++)
{
double sum = 0;
//for (int i = 0; i < functions.Count; i++)
int i = 0;
foreach (string key in functions.Keys)
{
sum += Math.Exp(w[i] * functions[key].Apply((MLFeature <int>)instance.Feature, y));
i++;
}
if (sum > max)
{
max = sum;
label = y;
}
}
return(label);
}
////public double CheckInstances(MLInstances<LabelT,FeatureT> TestList)
////{
//// List<MLInstance<LabelT, FeatureT>> list = new List<MLInstance<LabelT, FeatureT>>();
//// list.AddRange(TestList);
//// return CheckInstances(list);
////}
public abstract LabelT Classify(MLInstance <LabelT, FeatureT> instances);