/// <summary>
/// Prints Weka data set
/// </summary>
/// <param name="wekaDataset"> Instances Weka data set </param>
private static void printDataSet(Instances wekaDataset)
{
Console.WriteLine("Weka dataset");
System.Collections.IEnumerator en = wekaDataset.enumerateInstances();
while (en.hasMoreElements())
{
Instance instance = (Instance)en.nextElement();
double[] values = instance.toDoubleArray();
Console.WriteLine(Arrays.ToString(values));
Console.WriteLine(instance.stringValue(instance.classIndex()));
}
}
/// <summary>
/// {@inheritDoc}
/// @since 1.2
/// </summary>
public virtual bool Contains(double x, double y)
{
if (!CachedBounds.Contains(x, y))
{
return(false);
}
System.Collections.IEnumerator enum_ = Curves.elements();
int crossings = 0;
while (enum_.hasMoreElements())
{
Curve c = (Curve)enum_.nextElement();
crossings += c.crossingsFor(x, y);
}
return((crossings & 1) == 1);
}
/// <summary>
/// Converts Weka data set to Neuroph data set </summary>
/// <param name="wekaDataset"> Instances Weka data set </param>
/// <param name="numInputs"> int Number of inputs </param>
/// <param name="numOutputs"> int Number of outputs </param>
/// <returns> Neuroph data set </returns>
public static DataSet convertWekaToNeurophDataset(Instances wekaDataset, int numInputs, int numOutputs)
{
if (numInputs <= 0)
{
throw new System.ArgumentException("Number of inputs in DataSet cannot be zero or negative!");
}
if (numOutputs < 0)
{
throw new System.ArgumentException("Number of outputs in DataSet cannot be negative!");
}
if (numOutputs + numInputs < wekaDataset.numAttributes())
{
throw new System.ArgumentException("Number of outputs and inputs should be equal to number of attributes from data set!");
}
// create supervised or unsupervised data set that will be returned
DataSet neurophDataset = null;
if (numOutputs > 0)
{
neurophDataset = new DataSet(numInputs, numOutputs);
}
else
{
neurophDataset = new DataSet(numInputs);
}
List <double?> classValues = new List <double?>();
// get all different class values (as ints) from weka dataset
foreach (Instance inst in wekaDataset)
{
double?classDouble = inst.classValue();
if (!classValues.Contains(classDouble))
{
classValues.Add(classDouble);
}
}
System.Collections.IEnumerator en = wekaDataset.enumerateInstances();
while (en.hasMoreElements()) // iterate all instances from dataset
{
Instance instance = (Instance)en.nextElement();
double[] values = instance.toDoubleArray(); // get all the values from current instance
if (numOutputs == 0) // add unsupervised row
{
neurophDataset.addRow(values);
} // add supervised row
else
{
double[] inputs = new double[numInputs];
double[] outputs = new double[numOutputs];
// set inputs
for (int k = 0; k < values.Length; k++)
{
if (k < numInputs)
{
inputs[k] = values[k];
}
}
// set binary values for class outputs
int k = 0;
foreach (double?entry in classValues)
{
if ((double)entry == instance.classValue()) // if the
{
outputs[k] = 1;
}
else
{
outputs[k] = 0;
}
k++;
}
DataSetRow row = new DataSetRow(inputs, outputs);
row.Label = instance.stringValue(instance.classIndex());
neurophDataset.addRow(row);
}
}
return(neurophDataset);
}