public DecisionTreeClassifyer getBestDecisionTreeClassifyer(float[] w)
{
//最小誤り率を無限大に初期化
float errMin = 10000;
for (int r = 0; r < data[0].GetLength(0); r++)
{
for (int op = 0; op <= 1; op++)
{
for (float v = this.min[r] - this.step[r]; v < this.min[r] + ((float)k + 1.0f) * this.step[r]; v += this.step[r])
{
float werr = 0.0f;
DecisionTreeClassifyer dtcf = new DecisionTreeClassifyer(v, r, op);
for (int j = 0; j < data.GetLength(0); j++)
{
werr += w[j] * (dtcf.classify(data[j]) == classElement[j] ? 0.0f : 1.0f);
}
if (errMin > werr)
{
best = dtcf;
errMin = werr;
}
}
}
}
return(best);
}
public DecisionTreeClassifyer[] boosting()
{
DecisionTreeClassifyer[] dtcf = new DecisionTreeClassifyer[k];
for (int i = 0; i < k; i++)
{
SelectBestDecisitonTree sbdt = new SelectBestDecisitonTree(data, classElemnt, p);
dtcf[i] = sbdt.getBestDecisionTreeClassifyer(w[i]);
//Miの誤り率の計算
errorM[i] = 0;
for (int j = 0; j < data.GetLength(0); j++)
{
errorM[i] += w[i][j] * (dtcf[i].classify(data[j]) == classElemnt[j]? 0.0f:1.0f);
}
dtcf[i].error = errorM[i];
if (i < k - 1)
{
for (int j = 0; j < data.GetLength(0); j++)
{
if (dtcf[i].classify(data[j]) == classElemnt[j])
{
w[i + 1][j] = w[i][j] * (errorM[i] / (1.0f - errorM[i]));
}
}
float wsum = 0;
for (int j = 0; j < data.GetLength(0); j++)
{
wsum += w[i + 1][j];
}
for (int j = 0; j < data.GetLength(0); j++)
{
w[i + 1][j] = w[i + 1][j] / wsum;
}
}
}
return(dtcf);
}