private static double ApproximateChiSquared(Instances S, int attributeIndex, int targetAttributeIndex)
{
// In the int array, element 1 is Positive and element 2 is Negative
int indexOfPositive = 0;
int indexOfNegative = 1;
Dictionary <int, int[]> examplesList = new Dictionary <int, int[]>();
for (int i = 0; i < S.attribute(attributeIndex).numValues(); i++)
{
examplesList.Add(i, new int[2]);
}
// Partition each example into a bucket based on its attribute value
// Also, get a count of positive and negative target values
double p = 0;
double n = 0;
int droppedExamples = 0;
for (int i = 0; i < S.numInstances(); i++)
{
double value = S.instance(i).value(attributeIndex);
if (Double.IsNaN(value))
{
// Drop missing/unknown values but keep track of how many are dropped
droppedExamples++;
Log.LogVerbose("IsNaN encountered calculating chi-squared stat for attribute {0}", attributeIndex);
continue;
}
int targetValue = (int)S.instance(i).value(targetAttributeIndex);
if (targetValue == ID3.PositiveTargetValue)
{
p++;
examplesList[(int)value][indexOfPositive]++;
}
else if (targetValue == ID3.NegativeTargetValue)
{
n++;
examplesList[(int)value][indexOfNegative]++;
}
else
{
throw new Exception(String.Format("Unexpected targetValue value of {0}", targetValue));
}
}
// Go through each partition to sum up the Chi-Squared statistic
double chiSquaredStatistic = 0;
for (int i = 0; i < S.attribute(attributeIndex).numValues(); i++)
{
double pi = examplesList[i][indexOfPositive];
double ni = examplesList[i][indexOfNegative];
double expectedPi = ChiSquare.ExpectedPi(p, n, pi, ni);
double expectedNi = ChiSquare.ExpectedNi(p, n, pi, ni);
double piTerm = expectedPi == 0 ? 0 : Math.Pow(pi - expectedPi, 2) / expectedPi;
double niTerm = expectedNi == 0 ? 0 : Math.Pow(ni - expectedNi, 2) / expectedNi;
chiSquaredStatistic += piTerm + niTerm;
}
if (Double.IsNaN(chiSquaredStatistic))
{
}
return(chiSquaredStatistic);
}
