public static void dataSplit(dataSet X, double v1, double v2, dataSet X1, dataSet X2)
{
if (v2 < v1)
{
throw new Exception("error");
}
X1.Clear();
X2.Clear();
X1.setDataInfo(X);
X2.setDataInfo(X);
int n1 = (int)(X.Count * v1);
int n2 = (int)(X.Count * v2);
for (int i = 0; i < X.Count; i++)
{
if (i >= n1 && i < n2)
{
X1.Add(X[i]);
}
else
{
X2.Add(X[i]);
}
}
}
public dataSet randomShuffle()
{
List <int> ri = randomTool <int> .getShuffledIndexList(this.Count);
dataSet X = new dataSet(this.NTag, this.NFeature);
foreach (int i in ri)
{
X.Add(this[i]);
}
return(X);
}
public static void dataSplit(dataSet X, double v, dataSet X1, dataSet X2)
{
X1.Clear();
X2.Clear();
X1.setDataInfo(X);
X2.setDataInfo(X);
int n = (int)(X.Count * v);
for (int i = 0; i < X.Count; i++)
{
if (i < n)
{
X1.Add(X[i]);
}
else
{
X2.Add(X[i]);
}
}
}
public static void dataSizeScale(dataSet X)
{
dataSet XX = new dataSet();
XX.setDataInfo(X);
foreach (dataSeq im in X)
{
XX.Add(im);
}
X.Clear();
int n = (int)(XX.Count * Global.trainSizeScale);
for (int i = 0; i < n; i++)
{
int j = i;
if (j > XX.Count - 1)
{
j %= XX.Count - 1;
}
X.Add(XX[j]);
}
X.setDataInfo(XX);
}