public void NormalizeDataSet(KPoint.NormType norm)
{
if (norm == KPoint.NormType.MaxAttr)
{
double[] attributes = new double[Dimensions];
for (int i = 0; i < attributes.Length; i++)
{
attributes[i] = 0.0;
}
foreach (KPoint p in PointList)
{
for (int i = 0; i < attributes.Length; i++)
{
attributes[i] = Math.Max(attributes[i], p[i]);
}
}
foreach (KPoint p in PointList)
{
p.Normalize(attributes);
}
}
else
{
foreach (KPoint p in PointList)
{
p.Normalize(norm);
}
}
}
//This routine will normalize data
private void button14_Click(object sender, EventArgs e)
{
//This code gets the normalization type from the dropdown
KPoint.NormType norm = KPoint.NormType.None;
if (comboBox4.SelectedIndex == 0)
{
norm = KPoint.NormType.GeometricMean;
}
else if (comboBox4.SelectedIndex == 1)
{
norm = KPoint.NormType.ArithmeticMean;
}
else if (comboBox4.SelectedIndex == 2)
{
norm = KPoint.NormType.Max;
}
else if (comboBox4.SelectedIndex == 3)
{
norm = KPoint.NormType.MaxAttr;
}
else if (comboBox4.SelectedIndex == 4)
{
norm = KPoint.NormType.None;
}
//This loads a list of datapoints and normalized is
var normPoints = new PointSet(pointSetFile);
List <int> featureSet = new List <int>();
for (int i = 0; i < normPoints.Dimensions; i++)
{
if (checkedListBox1.GetItemCheckState(i) == CheckState.Checked)
{
featureSet.Add(i);
}
}
normPoints = normPoints.GetReducedAttributeSet(featureSet);
normPoints.NormalizeDataSet(norm);
//This will create a sub directory to place the file in
String folder = pointSetFileShort.Substring(0, pointSetFileShort.IndexOf('.'));
System.IO.Directory.CreateDirectory(folder);
openFileDialog2.InitialDirectory = folder;
//now we write the values
String normFileName = folder + "/" + folder + "_" + norm.ToString() + featureSet.Count + "features" + ".txt";
normPoints.Save(normFileName);
MessageBox.Show("Normalized File Generated! " + normFileName);
}
public void NormalizeDataSet(KPoint.NormType norm)
{
if (norm == KPoint.NormType.MaxAttr)
{
double[] attributes = new double[Dimensions];
for (int i = 0; i < attributes.Length; i++)
{
attributes[i] = 0.0;
}
foreach (KPoint p in PointList)
{
for (int i = 0; i < attributes.Length; i++)
{
attributes[i] = Math.Max(attributes[i], p[i]);
}
}
foreach (KPoint p in PointList)
{
p.Normalize(attributes);
}
}
else if (norm == KPoint.NormType.ZeroMeanOneStd)
{
//Calculate the mean and std
double[] rowMeans = new double[Dimensions];
double[] rowSqrSum = new double[Dimensions];
double[] rowStds = new double[Dimensions];
//Calculate the mean and std
foreach (var point in PointList)
{
for (int d = 0; d < Dimensions; d++)
{
rowMeans[d] += point[d];
rowSqrSum[d] += point[d] * point[d];
}
}
for (int d = 0; d < Dimensions; d++)
{
rowMeans[d] /= PointList.Count;
double attrVar = rowSqrSum[d] / PointList.Count - rowMeans[d] * rowMeans[d];
rowStds[d] = Math.Sqrt(attrVar);
}
//Standardize the data
foreach (var point in PointList)
{
for (int d = 0; d < Dimensions; d++)
{
point[d] = (point[d] - rowMeans[d]) / rowStds[d];
}
}
}
else
{
foreach (KPoint p in PointList)
{
p.Normalize(norm);
}
}
}
